From 5c779a5ab5e34196cdb7deedcd75851c1a839f8e Mon Sep 17 00:00:00 2001
From: yanzhongsino <yan.zhong.sino@gmail.com>
Date: Mon, 4 Nov 2024 11:39:45 +0800
Subject: [PATCH] Site updated: 2024-11-04 11:39:35

---
 2018/06/04/web_CSS/index.html                 |    9 +-
 2018/06/04/web_html/index.html                |    9 +-
 2018/06/05/web_jquery/index.html              |    4 +-
 2018/06/11/web_bootstrap/index.html           |    4 +-
 2018/06/12/web_ES6/index.html                 |    8 +-
 2018/06/12/web_flex/index.html                |    4 +-
 2018/06/12/web_gulp/index.html                |    4 +-
 2018/06/14/web_react/index.html               |    4 +-
 2021/09/11/bioinfo_synteny_WGDI/index.html    |    4 +-
 .../index.html                                |    4 +-
 2021/11/27/bioinfo_synteny_MCScanX/index.html |    4 +-
 2022/03/22/omics_genome_submit/index.html     |    2 +-
 2022/04/10/bioinfo_geneflow_intro/index.html  |    4 +-
 .../index.html                                |    4 +-
 .../index.html                                |    4 +-
 .../index.html                                |    4 +-
 .../index.html                                |    4 +-
 .../index.html                                |    4 +-
 .../index.html                                |   10 +-
 about/index.html                              |    2 +-
 archives/2018/06/index.html                   |   20 +-
 archives/2018/index.html                      |   20 +-
 archives/page/13/index.html                   |   20 +-
 atom.xml                                      |    6 +-
 categories/web/index.html                     |   20 +-
 css/main.css                                  |    2 +-
 index.html                                    |    2 +-
 page/13/index.html                            |   88 +-
 search.xml                                    | 8798 ++++++++---------
 29 files changed, 4536 insertions(+), 4536 deletions(-)

diff --git a/2018/06/04/web_CSS/index.html b/2018/06/04/web_CSS/index.html
index 820e7df35..2271b0703 100644
--- a/2018/06/04/web_CSS/index.html
+++ b/2018/06/04/web_CSS/index.html
@@ -412,10 +412,13 @@ <h2 id="1-3-tips"><a href="#1-3-tips" class="headerlink" title="1.3. tips"></a>1
 
         
     <div class="post-nav">
-      <div class="post-nav-item"></div>
       <div class="post-nav-item">
-    <a href="/2018/06/04/web_html/" rel="next" title="html">
-      html <i class="fa fa-chevron-right"></i>
+    <a href="/2018/06/04/web_html/" rel="prev" title="html">
+      <i class="fa fa-chevron-left"></i> html
+    </a></div>
+      <div class="post-nav-item">
+    <a href="/2018/06/05/web_jquery/" rel="next" title="jquery">
+      jquery <i class="fa fa-chevron-right"></i>
     </a></div>
     </div>
       </footer>
diff --git a/2018/06/04/web_html/index.html b/2018/06/04/web_html/index.html
index f7898953d..40d53dcbd 100644
--- a/2018/06/04/web_html/index.html
+++ b/2018/06/04/web_html/index.html
@@ -342,13 +342,10 @@ <h2 id="tips"><a href="#tips" class="headerlink" title="tips"></a>tips</h2><ul>
 
         
     <div class="post-nav">
+      <div class="post-nav-item"></div>
       <div class="post-nav-item">
-    <a href="/2018/06/04/web_CSS/" rel="prev" title="CSS">
-      <i class="fa fa-chevron-left"></i> CSS
-    </a></div>
-      <div class="post-nav-item">
-    <a href="/2018/06/05/web_jquery/" rel="next" title="jquery">
-      jquery <i class="fa fa-chevron-right"></i>
+    <a href="/2018/06/04/web_CSS/" rel="next" title="CSS">
+      CSS <i class="fa fa-chevron-right"></i>
     </a></div>
     </div>
       </footer>
diff --git a/2018/06/05/web_jquery/index.html b/2018/06/05/web_jquery/index.html
index 9dd0f6b24..831c964cd 100644
--- a/2018/06/05/web_jquery/index.html
+++ b/2018/06/05/web_jquery/index.html
@@ -454,8 +454,8 @@ <h2 id="1-7-tips"><a href="#1-7-tips" class="headerlink" title="1.7. tips"></a>1
         
     <div class="post-nav">
       <div class="post-nav-item">
-    <a href="/2018/06/04/web_html/" rel="prev" title="html">
-      <i class="fa fa-chevron-left"></i> html
+    <a href="/2018/06/04/web_CSS/" rel="prev" title="CSS">
+      <i class="fa fa-chevron-left"></i> CSS
     </a></div>
       <div class="post-nav-item">
     <a href="/2018/06/05/blog_hexo+github/" rel="next" title="hexo建站，github.io发布，多终端同步">
diff --git a/2018/06/11/web_bootstrap/index.html b/2018/06/11/web_bootstrap/index.html
index ceb1542e3..482c8fe37 100644
--- a/2018/06/11/web_bootstrap/index.html
+++ b/2018/06/11/web_bootstrap/index.html
@@ -363,8 +363,8 @@ <h1 id="3-bootstrap的css"><a href="#3-bootstrap的css" class="headerlink" title
       <i class="fa fa-chevron-left"></i> hexo建站，github.io发布，多终端同步
     </a></div>
       <div class="post-nav-item">
-    <a href="/2018/06/12/web_ES6/" rel="next" title="ES6">
-      ES6 <i class="fa fa-chevron-right"></i>
+    <a href="/2018/06/12/web_gulp/" rel="next" title="glup">
+      glup <i class="fa fa-chevron-right"></i>
     </a></div>
     </div>
       </footer>
diff --git a/2018/06/12/web_ES6/index.html b/2018/06/12/web_ES6/index.html
index e4b1ec221..b20bf27a6 100644
--- a/2018/06/12/web_ES6/index.html
+++ b/2018/06/12/web_ES6/index.html
@@ -349,12 +349,12 @@ <h2 id="对象代理"><a href="#对象代理" class="headerlink" title="对象
         
     <div class="post-nav">
       <div class="post-nav-item">
-    <a href="/2018/06/11/web_bootstrap/" rel="prev" title="bootstrap">
-      <i class="fa fa-chevron-left"></i> bootstrap
+    <a href="/2018/06/12/web_flex/" rel="prev" title="flex">
+      <i class="fa fa-chevron-left"></i> flex
     </a></div>
       <div class="post-nav-item">
-    <a href="/2018/06/12/web_gulp/" rel="next" title="glup">
-      glup <i class="fa fa-chevron-right"></i>
+    <a href="/2018/06/14/web_react/" rel="next" title="react">
+      react <i class="fa fa-chevron-right"></i>
     </a></div>
     </div>
       </footer>
diff --git a/2018/06/12/web_flex/index.html b/2018/06/12/web_flex/index.html
index b7f8d4545..f4e49b00b 100644
--- a/2018/06/12/web_flex/index.html
+++ b/2018/06/12/web_flex/index.html
@@ -435,8 +435,8 @@ <h3 id="1-1-2-项目属性"><a href="#1-1-2-项目属性" class="headerlink" tit
       <i class="fa fa-chevron-left"></i> glup
     </a></div>
       <div class="post-nav-item">
-    <a href="/2018/06/14/web_react/" rel="next" title="react">
-      react <i class="fa fa-chevron-right"></i>
+    <a href="/2018/06/12/web_ES6/" rel="next" title="ES6">
+      ES6 <i class="fa fa-chevron-right"></i>
     </a></div>
     </div>
       </footer>
diff --git a/2018/06/12/web_gulp/index.html b/2018/06/12/web_gulp/index.html
index 799834503..755a4c691 100644
--- a/2018/06/12/web_gulp/index.html
+++ b/2018/06/12/web_gulp/index.html
@@ -346,8 +346,8 @@ <h1 id="glup"><a href="#glup" class="headerlink" title="glup"></a>glup</h1><p>
         
     <div class="post-nav">
       <div class="post-nav-item">
-    <a href="/2018/06/12/web_ES6/" rel="prev" title="ES6">
-      <i class="fa fa-chevron-left"></i> ES6
+    <a href="/2018/06/11/web_bootstrap/" rel="prev" title="bootstrap">
+      <i class="fa fa-chevron-left"></i> bootstrap
     </a></div>
       <div class="post-nav-item">
     <a href="/2018/06/12/web_flex/" rel="next" title="flex">
diff --git a/2018/06/14/web_react/index.html b/2018/06/14/web_react/index.html
index 453e122b9..1942e955a 100644
--- a/2018/06/14/web_react/index.html
+++ b/2018/06/14/web_react/index.html
@@ -385,8 +385,8 @@ <h2 id="1-3-生命周期"><a href="#1-3-生命周期" class="headerlink" title="
         
     <div class="post-nav">
       <div class="post-nav-item">
-    <a href="/2018/06/12/web_flex/" rel="prev" title="flex">
-      <i class="fa fa-chevron-left"></i> flex
+    <a href="/2018/06/12/web_ES6/" rel="prev" title="ES6">
+      <i class="fa fa-chevron-left"></i> ES6
     </a></div>
       <div class="post-nav-item">
     <a href="/2020/12/29/Windows_system_reinstall.windows/" rel="next" title="重装windows系统">
diff --git a/2021/09/11/bioinfo_synteny_WGDI/index.html b/2021/09/11/bioinfo_synteny_WGDI/index.html
index 68ca5cda4..f1a28dff0 100644
--- a/2021/09/11/bioinfo_synteny_WGDI/index.html
+++ b/2021/09/11/bioinfo_synteny_WGDI/index.html
@@ -32,8 +32,8 @@
 <meta property="article:modified_time" content="2024-06-15T03:52:20.532Z">
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="biosoft">
-<meta property="article:tag" content="Ks">
 <meta property="article:tag" content="WGD">
+<meta property="article:tag" content="Ks">
 <meta property="article:tag" content="synteny">
 <meta property="article:tag" content="colinearity">
 <meta property="article:tag" content="WGDI">
@@ -667,8 +667,8 @@ <h1 id="4-references"><a href="#4-references" class="headerlink" title="4. refer
       <footer class="post-footer">
           <div class="post-tags">
               <a href="/tags/biosoft/" rel="tag"># biosoft</a>
-              <a href="/tags/Ks/" rel="tag"># Ks</a>
               <a href="/tags/WGD/" rel="tag"># WGD</a>
+              <a href="/tags/Ks/" rel="tag"># Ks</a>
               <a href="/tags/synteny/" rel="tag"># synteny</a>
               <a href="/tags/colinearity/" rel="tag"># colinearity</a>
               <a href="/tags/WGDI/" rel="tag"># WGDI</a>
diff --git a/2021/11/19/omics_transcriptome_RNA-seq.tools/index.html b/2021/11/19/omics_transcriptome_RNA-seq.tools/index.html
index 97f29d8dd..2cdcb960a 100644
--- a/2021/11/19/omics_transcriptome_RNA-seq.tools/index.html
+++ b/2021/11/19/omics_transcriptome_RNA-seq.tools/index.html
@@ -39,8 +39,8 @@
 <meta property="article:published_time" content="2021-11-19T12:50:00.000Z">
 <meta property="article:modified_time" content="2024-10-28T07:38:39.550Z">
 <meta property="article:author" content="南方枝叶">
-<meta property="article:tag" content="Trinity">
 <meta property="article:tag" content="DESeq2">
+<meta property="article:tag" content="Trinity">
 <meta property="article:tag" content="RNA-sequencing">
 <meta property="article:tag" content="RNA-seq">
 <meta property="article:tag" content="HISAT2">
@@ -546,8 +546,8 @@ <h1 id="5-references"><a href="#5-references" class="headerlink" title="5. refer
 
       <footer class="post-footer">
           <div class="post-tags">
-              <a href="/tags/Trinity/" rel="tag"># Trinity</a>
               <a href="/tags/DESeq2/" rel="tag"># DESeq2</a>
+              <a href="/tags/Trinity/" rel="tag"># Trinity</a>
               <a href="/tags/RNA-sequencing/" rel="tag"># RNA-sequencing</a>
               <a href="/tags/RNA-seq/" rel="tag"># RNA-seq</a>
               <a href="/tags/HISAT2/" rel="tag"># HISAT2</a>
diff --git a/2021/11/27/bioinfo_synteny_MCScanX/index.html b/2021/11/27/bioinfo_synteny_MCScanX/index.html
index 6e6290ad6..fd4359f6b 100644
--- a/2021/11/27/bioinfo_synteny_MCScanX/index.html
+++ b/2021/11/27/bioinfo_synteny_MCScanX/index.html
@@ -33,10 +33,10 @@
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="biosoft">
 <meta property="article:tag" content="ggplot2">
+<meta property="article:tag" content="WGD">
 <meta property="article:tag" content="Ks">
 <meta property="article:tag" content="ParaAT">
 <meta property="article:tag" content="KaKs_Calculator">
-<meta property="article:tag" content="WGD">
 <meta property="article:tag" content="divergence time">
 <meta property="article:tag" content="paml">
 <meta property="article:tag" content="synteny">
@@ -592,10 +592,10 @@ <h1 id="4-references"><a href="#4-references" class="headerlink" title="4. refer
           <div class="post-tags">
               <a href="/tags/biosoft/" rel="tag"># biosoft</a>
               <a href="/tags/ggplot2/" rel="tag"># ggplot2</a>
+              <a href="/tags/WGD/" rel="tag"># WGD</a>
               <a href="/tags/Ks/" rel="tag"># Ks</a>
               <a href="/tags/ParaAT/" rel="tag"># ParaAT</a>
               <a href="/tags/KaKs-Calculator/" rel="tag"># KaKs_Calculator</a>
-              <a href="/tags/WGD/" rel="tag"># WGD</a>
               <a href="/tags/divergence-time/" rel="tag"># divergence time</a>
               <a href="/tags/paml/" rel="tag"># paml</a>
               <a href="/tags/synteny/" rel="tag"># synteny</a>
diff --git a/2022/03/22/omics_genome_submit/index.html b/2022/03/22/omics_genome_submit/index.html
index a778da4c2..b34a01072 100644
--- a/2022/03/22/omics_genome_submit/index.html
+++ b/2022/03/22/omics_genome_submit/index.html
@@ -535,7 +535,7 @@ <h5 id="4-2-3-3-1-sample-stats-x2F-sample-val文件中报错ERROR"><a href="#4-2
 </li>
 <li><strong>SEQ_FEAT.TransLen</strong><ul>
 <li>表示蛋白质长度与预测的蛋白质长度不匹配，运行错误</li>
-<li>建议重跑table2asn，报错持续存在就写邮件把sample.sqn和运行的命令行发给NCBI(<a href="mailto:&#103;&#101;&#x6e;&#x6f;&#109;&#x65;&#115;&#64;&#x6e;&#99;&#x62;&#x69;&#46;&#110;&#108;&#109;&#46;&#110;&#105;&#104;&#x2e;&#x67;&#x6f;&#x76;">&#103;&#101;&#x6e;&#x6f;&#109;&#x65;&#115;&#64;&#x6e;&#99;&#x62;&#x69;&#46;&#110;&#108;&#109;&#46;&#110;&#105;&#104;&#x2e;&#x67;&#x6f;&#x76;</a>)让帮忙修改这个错误。</li>
+<li>建议重跑table2asn，报错持续存在就写邮件把sample.sqn和运行的命令行发给NCBI(<a href="mailto:&#103;&#x65;&#x6e;&#x6f;&#109;&#101;&#x73;&#x40;&#110;&#99;&#x62;&#x69;&#x2e;&#110;&#x6c;&#x6d;&#x2e;&#110;&#105;&#x68;&#x2e;&#103;&#111;&#x76;">&#103;&#x65;&#x6e;&#x6f;&#109;&#101;&#x73;&#x40;&#110;&#99;&#x62;&#x69;&#x2e;&#110;&#x6c;&#x6d;&#x2e;&#110;&#105;&#x68;&#x2e;&#103;&#111;&#x76;</a>)让帮忙修改这个错误。</li>
 </ul>
 </li>
 <li><strong>SEQ_FEAT.BadInternalCharacter</strong><ul>
diff --git a/2022/04/10/bioinfo_geneflow_intro/index.html b/2022/04/10/bioinfo_geneflow_intro/index.html
index cb4ea2586..4a76b3146 100644
--- a/2022/04/10/bioinfo_geneflow_intro/index.html
+++ b/2022/04/10/bioinfo_geneflow_intro/index.html
@@ -35,10 +35,10 @@
 <meta property="article:tag" content="hybridization">
 <meta property="article:tag" content="introgressive">
 <meta property="article:tag" content="3s">
+<meta property="article:tag" content="Dsuite">
 <meta property="article:tag" content="PhyloNetworks">
 <meta property="article:tag" content="population networks">
 <meta property="article:tag" content="TreeMix">
-<meta property="article:tag" content="Dsuite">
 <meta name="twitter:card" content="summary">
 <meta name="twitter:image" content="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true">
 
@@ -489,10 +489,10 @@ <h1 id="3-reference"><a href="#3-reference" class="headerlink" title="3. referen
               <a href="/tags/hybridization/" rel="tag"># hybridization</a>
               <a href="/tags/introgressive/" rel="tag"># introgressive</a>
               <a href="/tags/3s/" rel="tag"># 3s</a>
+              <a href="/tags/Dsuite/" rel="tag"># Dsuite</a>
               <a href="/tags/PhyloNetworks/" rel="tag"># PhyloNetworks</a>
               <a href="/tags/population-networks/" rel="tag"># population networks</a>
               <a href="/tags/TreeMix/" rel="tag"># TreeMix</a>
-              <a href="/tags/Dsuite/" rel="tag"># Dsuite</a>
           </div>
 
         
diff --git a/2022/07/10/omics_genome_quality.assessment_intro/index.html b/2022/07/10/omics_genome_quality.assessment_intro/index.html
index 8a994fd52..202b45543 100644
--- a/2022/07/10/omics_genome_quality.assessment_intro/index.html
+++ b/2022/07/10/omics_genome_quality.assessment_intro/index.html
@@ -34,8 +34,8 @@
 <meta property="article:published_time" content="2022-07-09T16:00:00.000Z">
 <meta property="article:modified_time" content="2024-06-15T03:55:26.174Z">
 <meta property="article:author" content="南方枝叶">
-<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="genome">
+<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="quality assessment">
 <meta property="article:tag" content="transcriptome">
 <meta name="twitter:card" content="summary">
@@ -450,8 +450,8 @@ <h1 id="4-references"><a href="#4-references" class="headerlink" title="4. refer
 
       <footer class="post-footer">
           <div class="post-tags">
-              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/genome/" rel="tag"># genome</a>
+              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/quality-assessment/" rel="tag"># quality assessment</a>
               <a href="/tags/transcriptome/" rel="tag"># transcriptome</a>
           </div>
diff --git a/2022/07/23/omics_genome_quality.assessment_mapping_intro/index.html b/2022/07/23/omics_genome_quality.assessment_mapping_intro/index.html
index 1882d1f2d..61c73e2e7 100644
--- a/2022/07/23/omics_genome_quality.assessment_mapping_intro/index.html
+++ b/2022/07/23/omics_genome_quality.assessment_mapping_intro/index.html
@@ -32,8 +32,8 @@
 <meta property="article:modified_time" content="2024-10-25T03:22:19.014Z">
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="mitogenome">
-<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="genome">
+<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="quality assessment">
 <meta property="article:tag" content="transcriptome">
 <meta property="article:tag" content="mapping">
@@ -524,8 +524,8 @@ <h3 id="3-3-2-depth的具体指标"><a href="#3-3-2-depth的具体指标" class=
       <footer class="post-footer">
           <div class="post-tags">
               <a href="/tags/mitogenome/" rel="tag"># mitogenome</a>
-              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/genome/" rel="tag"># genome</a>
+              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/quality-assessment/" rel="tag"># quality assessment</a>
               <a href="/tags/transcriptome/" rel="tag"># transcriptome</a>
               <a href="/tags/mapping/" rel="tag"># mapping</a>
diff --git a/2022/07/27/omics_genome_quality.assessment_mapping_depth_observe/index.html b/2022/07/27/omics_genome_quality.assessment_mapping_depth_observe/index.html
index 47e832d50..6655e6771 100644
--- a/2022/07/27/omics_genome_quality.assessment_mapping_depth_observe/index.html
+++ b/2022/07/27/omics_genome_quality.assessment_mapping_depth_observe/index.html
@@ -32,8 +32,8 @@
 <meta property="article:modified_time" content="2024-06-15T03:54:19.828Z">
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="mitogenome">
-<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="genome">
+<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="samtools">
 <meta property="article:tag" content="quality assessment">
 <meta property="article:tag" content="mapping">
@@ -412,8 +412,8 @@ <h2 id="4-2-深度分布的查看"><a href="#4-2-深度分布的查看" class="h
       <footer class="post-footer">
           <div class="post-tags">
               <a href="/tags/mitogenome/" rel="tag"># mitogenome</a>
-              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/genome/" rel="tag"># genome</a>
+              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/samtools/" rel="tag"># samtools</a>
               <a href="/tags/quality-assessment/" rel="tag"># quality assessment</a>
               <a href="/tags/mapping/" rel="tag"># mapping</a>
diff --git a/2022/07/27/omics_genome_quality.assessment_mapping_depth_samtools/index.html b/2022/07/27/omics_genome_quality.assessment_mapping_depth_samtools/index.html
index 8681ac3cb..fb1b062c2 100644
--- a/2022/07/27/omics_genome_quality.assessment_mapping_depth_samtools/index.html
+++ b/2022/07/27/omics_genome_quality.assessment_mapping_depth_samtools/index.html
@@ -32,8 +32,8 @@
 <meta property="article:modified_time" content="2024-06-15T03:55:07.747Z">
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="mitogenome">
-<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="genome">
+<meta property="article:tag" content="organelle">
 <meta property="article:tag" content="samtools">
 <meta property="article:tag" content="quality assessment">
 <meta property="article:tag" content="mapping">
@@ -430,8 +430,8 @@ <h1 id="5-references"><a href="#5-references" class="headerlink" title="5. refer
       <footer class="post-footer">
           <div class="post-tags">
               <a href="/tags/mitogenome/" rel="tag"># mitogenome</a>
-              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/genome/" rel="tag"># genome</a>
+              <a href="/tags/organelle/" rel="tag"># organelle</a>
               <a href="/tags/samtools/" rel="tag"># samtools</a>
               <a href="/tags/quality-assessment/" rel="tag"># quality assessment</a>
               <a href="/tags/mapping/" rel="tag"># mapping</a>
diff --git a/2023/10/20/evolution_selection_HyPhy_intro/index.html b/2023/10/20/evolution_selection_HyPhy_intro/index.html
index 70356a350..822ce8591 100644
--- a/2023/10/20/evolution_selection_HyPhy_intro/index.html
+++ b/2023/10/20/evolution_selection_HyPhy_intro/index.html
@@ -33,7 +33,6 @@
 <meta property="article:author" content="南方枝叶">
 <meta property="article:tag" content="biosoft">
 <meta property="article:tag" content="bioinfo">
-<meta property="article:tag" content="PAML">
 <meta property="article:tag" content="HyPhy">
 <meta property="article:tag" content="RELAX">
 <meta property="article:tag" content="selection">
@@ -44,6 +43,7 @@
 <meta property="article:tag" content="FADE">
 <meta property="article:tag" content="aBSREL">
 <meta property="article:tag" content="BUSTED">
+<meta property="article:tag" content="PAML">
 <meta property="article:tag" content="codeML">
 <meta name="twitter:card" content="summary">
 <meta name="twitter:image" content="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true">
@@ -520,7 +520,6 @@ <h1 id="6-reference"><a href="#6-reference" class="headerlink" title="6. referen
           <div class="post-tags">
               <a href="/tags/biosoft/" rel="tag"># biosoft</a>
               <a href="/tags/bioinfo/" rel="tag"># bioinfo</a>
-              <a href="/tags/PAML/" rel="tag"># PAML</a>
               <a href="/tags/HyPhy/" rel="tag"># HyPhy</a>
               <a href="/tags/RELAX/" rel="tag"># RELAX</a>
               <a href="/tags/selection/" rel="tag"># selection</a>
@@ -531,6 +530,7 @@ <h1 id="6-reference"><a href="#6-reference" class="headerlink" title="6. referen
               <a href="/tags/FADE/" rel="tag"># FADE</a>
               <a href="/tags/aBSREL/" rel="tag"># aBSREL</a>
               <a href="/tags/BUSTED/" rel="tag"># BUSTED</a>
+              <a href="/tags/PAML/" rel="tag"># PAML</a>
               <a href="/tags/codeML/" rel="tag"># codeML</a>
           </div>
 
diff --git a/2024/09/19/bioinfo_orthology.inference_PlantTribes/index.html b/2024/09/19/bioinfo_orthology.inference_PlantTribes/index.html
index 84a2a7eaa..17937e8de 100644
--- a/2024/09/19/bioinfo_orthology.inference_PlantTribes/index.html
+++ b/2024/09/19/bioinfo_orthology.inference_PlantTribes/index.html
@@ -30,10 +30,10 @@
 <meta property="og:image" content="https://www.frontiersin.org/files/Articles/1011199/fpls-13-1011199-HTML-r1/image_m/fpls-13-1011199-g001.jpg">
 <meta property="og:image" content="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true">
 <meta property="article:published_time" content="2024-09-18T16:00:00.000Z">
-<meta property="article:modified_time" content="2024-10-30T05:14:53.317Z">
+<meta property="article:modified_time" content="2024-11-04T03:38:45.806Z">
 <meta property="article:author" content="南方枝叶">
-<meta property="article:tag" content="PlantTribes">
 <meta property="article:tag" content="orthology">
+<meta property="article:tag" content="PlantTribes">
 <meta property="article:tag" content="Ka&#x2F;Ks">
 <meta property="article:tag" content="orthofinder">
 <meta name="twitter:card" content="summary">
@@ -249,7 +249,7 @@ <h1 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2024-10-30 13:14:53" itemprop="dateModified" datetime="2024-10-30T13:14:53+08:00">2024-10-30</time>
+                <time title="Modified: 2024-11-04 11:38:45" itemprop="dateModified" datetime="2024-11-04T11:38:45+08:00">2024-11-04</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -444,7 +444,7 @@ <h3 id="7-2-1-运行"><a href="#7-2-1-运行" class="headerlink" title="7.2.1. 
 <li>目标基因家族组装的参数：</li>
 </ol>
 <ul>
-<li><code>--gene_family_search</code>：目标orthogroup identifiers的list文件。需要”–scaffold” and “–method”两个参数。如果有目标基因想要分析（比如有个课题分析RRR基因），那么一定要指定以得到更有针对性的转录本。scaffold数据库文件里的22Gv1.1&#x2F;annot&#x2F;orthofinder.list保存所有orthogroup IDs（如果使用orthofinder的话）。目标list文件可以通过目标基因的拟南芥或者其他21个物种的ID来搜索orthofinder.list文件，搜索到的第一列即为目标orthogroup IDs。</li>
+<li><code>--gene_family_search</code>：目标orthogroup identifiers的list文件。需要”–scaffold” and “–method”两个参数。如果有目标基因想要分析（比如有个课题分析RRR基因），那么一定要指定以得到更有针对性的转录本。scaffold数据库文件里的22Gv1.1&#x2F;annot&#x2F;orthofinder.list保存所有orthogroup IDs（如果使用orthofinder的话）。目标list文件可以通过目标基因的拟南芥或者其他21个物种的ID来搜索orthofinder.list文件，搜索到的第一列（数字）即为目标orthogroup IDs。</li>
 <li><code>--scaffold</code>：Orthogroups或者gene families proteins scaffold文件。用绝对路径指定。eg. &#x2F;home&#x2F;scaffolds&#x2F;22Gv1.1</li>
 <li><code>--method</code>：蛋白聚类方法。GFam：gfam；OrthoFinder：orthofinder；OrthoMCL：orthomcl；其他非PlantTribes方法：methodname。</li>
 <li><code>--gap_trimming</code>：在alignments里移除gappy sites，默认0.1，代表移除含有90% gaps的sites。</li>
@@ -784,8 +784,8 @@ <h1 id="8-references"><a href="#8-references" class="headerlink" title="8. refer
 
       <footer class="post-footer">
           <div class="post-tags">
-              <a href="/tags/PlantTribes/" rel="tag"># PlantTribes</a>
               <a href="/tags/orthology/" rel="tag"># orthology</a>
+              <a href="/tags/PlantTribes/" rel="tag"># PlantTribes</a>
               <a href="/tags/Ka-Ks/" rel="tag"># Ka/Ks</a>
               <a href="/tags/orthofinder/" rel="tag"># orthofinder</a>
           </div>
diff --git a/about/index.html b/about/index.html
index b4e314947..a690bbcab 100644
--- a/about/index.html
+++ b/about/index.html
@@ -246,7 +246,7 @@ <h1 class="post-title" itemprop="name headline">关于生信技工
 <li>教育经历：目前(中山大学在职博士后，植物基因组进化)，博士（中山大学，植物基因组进化），硕士（四川大学，植物进化），本科（西南大学，食品科学）。</li>
 <li>微信公众号：生信技工</li>
 <li>个人网站：<a target="_blank" rel="noopener" href="https://yanzhongsino.github.io/">https://yanzhongsino.github.io/</a></li>
-<li>邮箱​：<a href="mailto:&#x79;&#x61;&#110;&#46;&#122;&#x68;&#111;&#110;&#x67;&#x2e;&#115;&#105;&#x6e;&#111;&#x40;&#x67;&#x6d;&#97;&#105;&#108;&#x2e;&#99;&#x6f;&#109;">&#x79;&#x61;&#110;&#46;&#122;&#x68;&#111;&#110;&#x67;&#x2e;&#115;&#105;&#x6e;&#111;&#x40;&#x67;&#x6d;&#97;&#105;&#108;&#x2e;&#99;&#x6f;&#109;</a></li>
+<li>邮箱​：<a href="mailto:&#121;&#x61;&#x6e;&#46;&#122;&#104;&#111;&#110;&#103;&#x2e;&#x73;&#x69;&#x6e;&#x6f;&#64;&#103;&#109;&#x61;&#x69;&#x6c;&#46;&#x63;&#111;&#x6d;">&#121;&#x61;&#x6e;&#46;&#122;&#104;&#111;&#110;&#103;&#x2e;&#x73;&#x69;&#x6e;&#x6f;&#64;&#103;&#109;&#x61;&#x69;&#x6c;&#46;&#x63;&#111;&#x6d;</a></li>
 </ul>
 <hr>
 <ul>
diff --git a/archives/2018/06/index.html b/archives/2018/06/index.html
index 312ca7113..765758565 100644
--- a/archives/2018/06/index.html
+++ b/archives/2018/06/index.html
@@ -256,8 +256,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
-            <span itemprop="name">flex</span>
+          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
+            <span itemprop="name">ES6</span>
           </a>
       </div>
 
@@ -276,8 +276,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
-            <span itemprop="name">glup</span>
+          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
+            <span itemprop="name">flex</span>
           </a>
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@@ -296,8 +296,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
-            <span itemprop="name">ES6</span>
+          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
+            <span itemprop="name">glup</span>
           </a>
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@@ -376,8 +376,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
-            <span itemprop="name">html</span>
+          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
+            <span itemprop="name">CSS</span>
           </a>
       </div>
 
@@ -396,8 +396,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
-            <span itemprop="name">CSS</span>
+          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
+            <span itemprop="name">html</span>
           </a>
       </div>
 
diff --git a/archives/2018/index.html b/archives/2018/index.html
index 90063966f..76dba767f 100644
--- a/archives/2018/index.html
+++ b/archives/2018/index.html
@@ -256,8 +256,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
-            <span itemprop="name">flex</span>
+          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
+            <span itemprop="name">ES6</span>
           </a>
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@@ -276,8 +276,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
-            <span itemprop="name">glup</span>
+          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
+            <span itemprop="name">flex</span>
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@@ -296,8 +296,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
-            <span itemprop="name">ES6</span>
+          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
+            <span itemprop="name">glup</span>
           </a>
       </div>
 
@@ -376,8 +376,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
-            <span itemprop="name">html</span>
+          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
+            <span itemprop="name">CSS</span>
           </a>
       </div>
 
@@ -396,8 +396,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
-            <span itemprop="name">CSS</span>
+          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
+            <span itemprop="name">html</span>
           </a>
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diff --git a/archives/page/13/index.html b/archives/page/13/index.html
index de1f61978..3d35858f0 100644
--- a/archives/page/13/index.html
+++ b/archives/page/13/index.html
@@ -256,8 +256,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
-            <span itemprop="name">flex</span>
+          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
+            <span itemprop="name">ES6</span>
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@@ -276,8 +276,8 @@ <h1 class="site-title">生信技工</h1>
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       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
-            <span itemprop="name">glup</span>
+          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
+            <span itemprop="name">flex</span>
           </a>
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@@ -296,8 +296,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
-            <span itemprop="name">ES6</span>
+          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
+            <span itemprop="name">glup</span>
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@@ -376,8 +376,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
-            <span itemprop="name">html</span>
+          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
+            <span itemprop="name">CSS</span>
           </a>
       </div>
 
@@ -396,8 +396,8 @@ <h1 class="site-title">生信技工</h1>
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
-            <span itemprop="name">CSS</span>
+          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
+            <span itemprop="name">html</span>
           </a>
       </div>
 
diff --git a/atom.xml b/atom.xml
index 7b4aaea2f..ac45126ec 100644
--- a/atom.xml
+++ b/atom.xml
@@ -114,7 +114,7 @@
     <link href="http://example.com/2024/09/19/bioinfo_orthology.inference_PlantTribes/"/>
     <id>http://example.com/2024/09/19/bioinfo_orthology.inference_PlantTribes/</id>
     <published>2024-09-18T16:00:00.000Z</published>
-    <updated>2024-10-30T05:14:53.317Z</updated>
+    <updated>2024-11-04T03:38:45.806Z</updated>
     
     
     <summary type="html">简单介绍了软件PlantTribes，以及使用PlantTribes2进行推断编码序列，局部组装目标基因家族，直系同源序列推断(orthology inference)，orthogroups的多序列比对和建树，以及估算Ka/Ks等的用法。</summary>
@@ -126,10 +126,10 @@
     <category term="orthology inference" scheme="http://example.com/categories/bioinfo/orthology-inference/"/>
     
     
-    <category term="PlantTribes" scheme="http://example.com/tags/PlantTribes/"/>
-    
     <category term="orthology" scheme="http://example.com/tags/orthology/"/>
     
+    <category term="PlantTribes" scheme="http://example.com/tags/PlantTribes/"/>
+    
     <category term="Ka/Ks" scheme="http://example.com/tags/Ka-Ks/"/>
     
     <category term="orthofinder" scheme="http://example.com/tags/orthofinder/"/>
diff --git a/categories/web/index.html b/categories/web/index.html
index 33985ec28..a5f7ee77c 100644
--- a/categories/web/index.html
+++ b/categories/web/index.html
@@ -257,8 +257,8 @@ <h2 class="collection-header">web
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
-            <span itemprop="name">flex</span>
+          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
+            <span itemprop="name">ES6</span>
           </a>
       </div>
 
@@ -277,8 +277,8 @@ <h2 class="collection-header">web
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       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
-            <span itemprop="name">glup</span>
+          <a class="post-title-link" href="/2018/06/12/web_flex/" itemprop="url">
+            <span itemprop="name">flex</span>
           </a>
       </div>
 
@@ -297,8 +297,8 @@ <h2 class="collection-header">web
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       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/12/web_ES6/" itemprop="url">
-            <span itemprop="name">ES6</span>
+          <a class="post-title-link" href="/2018/06/12/web_gulp/" itemprop="url">
+            <span itemprop="name">glup</span>
           </a>
       </div>
 
@@ -357,8 +357,8 @@ <h2 class="collection-header">web
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
-            <span itemprop="name">html</span>
+          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
+            <span itemprop="name">CSS</span>
           </a>
       </div>
 
@@ -377,8 +377,8 @@ <h2 class="collection-header">web
       </div>
 
       <div class="post-title">
-          <a class="post-title-link" href="/2018/06/04/web_CSS/" itemprop="url">
-            <span itemprop="name">CSS</span>
+          <a class="post-title-link" href="/2018/06/04/web_html/" itemprop="url">
+            <span itemprop="name">html</span>
           </a>
       </div>
 
diff --git a/css/main.css b/css/main.css
index 10f994c51..94e586e61 100644
--- a/css/main.css
+++ b/css/main.css
@@ -1197,7 +1197,7 @@ pre .javascript .function {
 }
 .links-of-author a::before,
 .links-of-author span.exturl::before {
-  background: #944a54;
+  background: #50aafc;
   border-radius: 50%;
   content: ' ';
   display: inline-block;
diff --git a/index.html b/index.html
index 926157361..e71333c5a 100644
--- a/index.html
+++ b/index.html
@@ -588,7 +588,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2024-10-30 13:14:53" itemprop="dateModified" datetime="2024-10-30T13:14:53+08:00">2024-10-30</time>
+                <time title="Modified: 2024-11-04 11:38:45" itemprop="dateModified" datetime="2024-11-04T11:38:45+08:00">2024-11-04</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
diff --git a/page/13/index.html b/page/13/index.html
index f4f7d28e9..97e9ef2b0 100644
--- a/page/13/index.html
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   <article itemscope itemtype="http://schema.org/Article" class="post-block" lang="en">
-    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_flex/">
+    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_ES6/">
 
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@@ -333,7 +333,7 @@ <h2 class="post-title" itemprop="name headline">
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         <h2 class="post-title" itemprop="name headline">
           
-            <a href="/2018/06/12/web_flex/" class="post-title-link" itemprop="url">flex</a>
+            <a href="/2018/06/12/web_ES6/" class="post-title-link" itemprop="url">ES6</a>
         </h2>
 
         <div class="post-meta">
@@ -350,7 +350,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2023-04-19 10:29:05" itemprop="dateModified" datetime="2023-04-19T10:29:05+08:00">2023-04-19</time>
+                <time title="Modified: 2023-04-19 10:29:01" itemprop="dateModified" datetime="2023-04-19T10:29:01+08:00">2023-04-19</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -371,8 +371,8 @@ <h2 class="post-title" itemprop="name headline">
       </span>
       <span class="post-meta-item-text">Disqus: </span>
     
-    <a title="disqus" href="/2018/06/12/web_flex/#disqus_thread" itemprop="discussionUrl">
-      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_flex/" itemprop="commentCount"></span>
+    <a title="disqus" href="/2018/06/12/web_ES6/#disqus_thread" itemprop="discussionUrl">
+      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_ES6/" itemprop="commentCount"></span>
     </a>
   </span>
   
@@ -381,13 +381,13 @@ <h2 class="post-title" itemprop="name headline">
               <span class="post-meta-item-icon">
                 <i class="far fa-file-word"></i>
               </span>
-              <span>2.2k</span>
+              <span>705</span>
             </span>
             <span class="post-meta-item" title="Reading time">
               <span class="post-meta-item-icon">
                 <i class="far fa-clock"></i>
               </span>
-              <span>2 mins.</span>
+              <span>1 mins.</span>
             </span>
 
         </div>
@@ -399,10 +399,10 @@ <h2 class="post-title" itemprop="name headline">
     <div class="post-body" itemprop="articleBody">
 
       
-          <p>flex基础知识</p>
+          <p>ES6基础知识</p>
           <!--noindex-->
             <div class="post-button">
-              <a class="btn" href="/2018/06/12/web_flex/">
+              <a class="btn" href="/2018/06/12/web_ES6/">
                 Read more &raquo;
               </a>
             </div>
@@ -426,7 +426,7 @@ <h2 class="post-title" itemprop="name headline">
   
   
   <article itemscope itemtype="http://schema.org/Article" class="post-block" lang="en">
-    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_gulp/">
+    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_flex/">
 
     <span hidden itemprop="author" itemscope itemtype="http://schema.org/Person">
       <meta itemprop="image" content="/images/avatar.gif">
@@ -440,7 +440,7 @@ <h2 class="post-title" itemprop="name headline">
       <header class="post-header">
         <h2 class="post-title" itemprop="name headline">
           
-            <a href="/2018/06/12/web_gulp/" class="post-title-link" itemprop="url">glup</a>
+            <a href="/2018/06/12/web_flex/" class="post-title-link" itemprop="url">flex</a>
         </h2>
 
         <div class="post-meta">
@@ -457,7 +457,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2023-04-19 10:29:11" itemprop="dateModified" datetime="2023-04-19T10:29:11+08:00">2023-04-19</time>
+                <time title="Modified: 2023-04-19 10:29:05" itemprop="dateModified" datetime="2023-04-19T10:29:05+08:00">2023-04-19</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -478,8 +478,8 @@ <h2 class="post-title" itemprop="name headline">
       </span>
       <span class="post-meta-item-text">Disqus: </span>
     
-    <a title="disqus" href="/2018/06/12/web_gulp/#disqus_thread" itemprop="discussionUrl">
-      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_gulp/" itemprop="commentCount"></span>
+    <a title="disqus" href="/2018/06/12/web_flex/#disqus_thread" itemprop="discussionUrl">
+      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_flex/" itemprop="commentCount"></span>
     </a>
   </span>
   
@@ -488,13 +488,13 @@ <h2 class="post-title" itemprop="name headline">
               <span class="post-meta-item-icon">
                 <i class="far fa-file-word"></i>
               </span>
-              <span>400</span>
+              <span>2.2k</span>
             </span>
             <span class="post-meta-item" title="Reading time">
               <span class="post-meta-item-icon">
                 <i class="far fa-clock"></i>
               </span>
-              <span>1 mins.</span>
+              <span>2 mins.</span>
             </span>
 
         </div>
@@ -506,10 +506,10 @@ <h2 class="post-title" itemprop="name headline">
     <div class="post-body" itemprop="articleBody">
 
       
-          <p>gulp基础知识</p>
+          <p>flex基础知识</p>
           <!--noindex-->
             <div class="post-button">
-              <a class="btn" href="/2018/06/12/web_gulp/">
+              <a class="btn" href="/2018/06/12/web_flex/">
                 Read more &raquo;
               </a>
             </div>
@@ -533,7 +533,7 @@ <h2 class="post-title" itemprop="name headline">
   
   
   <article itemscope itemtype="http://schema.org/Article" class="post-block" lang="en">
-    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_ES6/">
+    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/12/web_gulp/">
 
     <span hidden itemprop="author" itemscope itemtype="http://schema.org/Person">
       <meta itemprop="image" content="/images/avatar.gif">
@@ -547,7 +547,7 @@ <h2 class="post-title" itemprop="name headline">
       <header class="post-header">
         <h2 class="post-title" itemprop="name headline">
           
-            <a href="/2018/06/12/web_ES6/" class="post-title-link" itemprop="url">ES6</a>
+            <a href="/2018/06/12/web_gulp/" class="post-title-link" itemprop="url">glup</a>
         </h2>
 
         <div class="post-meta">
@@ -564,7 +564,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2023-04-19 10:29:01" itemprop="dateModified" datetime="2023-04-19T10:29:01+08:00">2023-04-19</time>
+                <time title="Modified: 2023-04-19 10:29:11" itemprop="dateModified" datetime="2023-04-19T10:29:11+08:00">2023-04-19</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -585,8 +585,8 @@ <h2 class="post-title" itemprop="name headline">
       </span>
       <span class="post-meta-item-text">Disqus: </span>
     
-    <a title="disqus" href="/2018/06/12/web_ES6/#disqus_thread" itemprop="discussionUrl">
-      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_ES6/" itemprop="commentCount"></span>
+    <a title="disqus" href="/2018/06/12/web_gulp/#disqus_thread" itemprop="discussionUrl">
+      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/12/web_gulp/" itemprop="commentCount"></span>
     </a>
   </span>
   
@@ -595,7 +595,7 @@ <h2 class="post-title" itemprop="name headline">
               <span class="post-meta-item-icon">
                 <i class="far fa-file-word"></i>
               </span>
-              <span>705</span>
+              <span>400</span>
             </span>
             <span class="post-meta-item" title="Reading time">
               <span class="post-meta-item-icon">
@@ -613,10 +613,10 @@ <h2 class="post-title" itemprop="name headline">
     <div class="post-body" itemprop="articleBody">
 
       
-          <p>ES6基础知识</p>
+          <p>gulp基础知识</p>
           <!--noindex-->
             <div class="post-button">
-              <a class="btn" href="/2018/06/12/web_ES6/">
+              <a class="btn" href="/2018/06/12/web_gulp/">
                 Read more &raquo;
               </a>
             </div>
@@ -961,7 +961,7 @@ <h2 class="post-title" itemprop="name headline">
   
   
   <article itemscope itemtype="http://schema.org/Article" class="post-block" lang="en">
-    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/04/web_html/">
+    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/04/web_CSS/">
 
     <span hidden itemprop="author" itemscope itemtype="http://schema.org/Person">
       <meta itemprop="image" content="/images/avatar.gif">
@@ -975,7 +975,7 @@ <h2 class="post-title" itemprop="name headline">
       <header class="post-header">
         <h2 class="post-title" itemprop="name headline">
           
-            <a href="/2018/06/04/web_html/" class="post-title-link" itemprop="url">html</a>
+            <a href="/2018/06/04/web_CSS/" class="post-title-link" itemprop="url">CSS</a>
         </h2>
 
         <div class="post-meta">
@@ -992,7 +992,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2023-04-19 10:29:18" itemprop="dateModified" datetime="2023-04-19T10:29:18+08:00">2023-04-19</time>
+                <time title="Modified: 2023-04-19 10:28:56" itemprop="dateModified" datetime="2023-04-19T10:28:56+08:00">2023-04-19</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -1013,8 +1013,8 @@ <h2 class="post-title" itemprop="name headline">
       </span>
       <span class="post-meta-item-text">Disqus: </span>
     
-    <a title="disqus" href="/2018/06/04/web_html/#disqus_thread" itemprop="discussionUrl">
-      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/04/web_html/" itemprop="commentCount"></span>
+    <a title="disqus" href="/2018/06/04/web_CSS/#disqus_thread" itemprop="discussionUrl">
+      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/04/web_CSS/" itemprop="commentCount"></span>
     </a>
   </span>
   
@@ -1023,13 +1023,13 @@ <h2 class="post-title" itemprop="name headline">
               <span class="post-meta-item-icon">
                 <i class="far fa-file-word"></i>
               </span>
-              <span>695</span>
+              <span>3.1k</span>
             </span>
             <span class="post-meta-item" title="Reading time">
               <span class="post-meta-item-icon">
                 <i class="far fa-clock"></i>
               </span>
-              <span>1 mins.</span>
+              <span>3 mins.</span>
             </span>
 
         </div>
@@ -1041,10 +1041,10 @@ <h2 class="post-title" itemprop="name headline">
     <div class="post-body" itemprop="articleBody">
 
       
-          <p>html基础知识</p>
+          <p>CSS基础知识</p>
           <!--noindex-->
             <div class="post-button">
-              <a class="btn" href="/2018/06/04/web_html/">
+              <a class="btn" href="/2018/06/04/web_CSS/">
                 Read more &raquo;
               </a>
             </div>
@@ -1068,7 +1068,7 @@ <h2 class="post-title" itemprop="name headline">
   
   
   <article itemscope itemtype="http://schema.org/Article" class="post-block" lang="en">
-    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/04/web_CSS/">
+    <link itemprop="mainEntityOfPage" href="http://example.com/2018/06/04/web_html/">
 
     <span hidden itemprop="author" itemscope itemtype="http://schema.org/Person">
       <meta itemprop="image" content="/images/avatar.gif">
@@ -1082,7 +1082,7 @@ <h2 class="post-title" itemprop="name headline">
       <header class="post-header">
         <h2 class="post-title" itemprop="name headline">
           
-            <a href="/2018/06/04/web_CSS/" class="post-title-link" itemprop="url">CSS</a>
+            <a href="/2018/06/04/web_html/" class="post-title-link" itemprop="url">html</a>
         </h2>
 
         <div class="post-meta">
@@ -1099,7 +1099,7 @@ <h2 class="post-title" itemprop="name headline">
                   <i class="far fa-calendar-check"></i>
                 </span>
                 <span class="post-meta-item-text">Edited on</span>
-                <time title="Modified: 2023-04-19 10:28:56" itemprop="dateModified" datetime="2023-04-19T10:28:56+08:00">2023-04-19</time>
+                <time title="Modified: 2023-04-19 10:29:18" itemprop="dateModified" datetime="2023-04-19T10:29:18+08:00">2023-04-19</time>
               </span>
             <span class="post-meta-item">
               <span class="post-meta-item-icon">
@@ -1120,8 +1120,8 @@ <h2 class="post-title" itemprop="name headline">
       </span>
       <span class="post-meta-item-text">Disqus: </span>
     
-    <a title="disqus" href="/2018/06/04/web_CSS/#disqus_thread" itemprop="discussionUrl">
-      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/04/web_CSS/" itemprop="commentCount"></span>
+    <a title="disqus" href="/2018/06/04/web_html/#disqus_thread" itemprop="discussionUrl">
+      <span class="post-comments-count disqus-comment-count" data-disqus-identifier="2018/06/04/web_html/" itemprop="commentCount"></span>
     </a>
   </span>
   
@@ -1130,13 +1130,13 @@ <h2 class="post-title" itemprop="name headline">
               <span class="post-meta-item-icon">
                 <i class="far fa-file-word"></i>
               </span>
-              <span>3.1k</span>
+              <span>695</span>
             </span>
             <span class="post-meta-item" title="Reading time">
               <span class="post-meta-item-icon">
                 <i class="far fa-clock"></i>
               </span>
-              <span>3 mins.</span>
+              <span>1 mins.</span>
             </span>
 
         </div>
@@ -1148,10 +1148,10 @@ <h2 class="post-title" itemprop="name headline">
     <div class="post-body" itemprop="articleBody">
 
       
-          <p>CSS基础知识</p>
+          <p>html基础知识</p>
           <!--noindex-->
             <div class="post-button">
-              <a class="btn" href="/2018/06/04/web_CSS/">
+              <a class="btn" href="/2018/06/04/web_html/">
                 Read more &raquo;
               </a>
             </div>
diff --git a/search.xml b/search.xml
index a136940b3..f3e8a8e44 100644
--- a/search.xml
+++ b/search.xml
@@ -140,6 +140,65 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>Bioconductor</tag>
       </tags>
   </entry>
+  <entry>
+    <title>重装windows系统</title>
+    <url>/2020/12/29/Windows_system_reinstall.windows/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=330 height=86 src="//music.163.com/outchain/player?type=2&id=418550511&auto=1&height=66"></iframe></div>
+
+<h1 id="1-小白就一键重装"><a href="#1-小白就一键重装" class="headerlink" title="1. 小白就一键重装"></a>1. 小白就一键重装</h1><p>自己用过黑鲨装机软件，360装机软件等，都有绑定，需要重装之后卸载绑定的软件。<br>一键重装只能在电脑正常可进入桌面和使用软件的情况下进行，许多情况下不适用。</p>
+<h1 id="2-U盘重装"><a href="#2-U盘重装" class="headerlink" title="2. U盘重装"></a>2. U盘重装</h1><p>但强烈不建议一键重装，除非纯小白，建议在正常运行的电脑上制作启动U盘，然后使用U盘启动和安装需要更换系统的电脑：</p>
+<h2 id="2-1-【推荐】微软官方工具"><a href="#2-1-【推荐】微软官方工具" class="headerlink" title="2.1. 【推荐】微软官方工具"></a>2.1. 【推荐】微软官方工具</h2><p>可用微软官方的制作工具，<a href="https://www.microsoft.com/zh-cn/software-download/windows10">微软官方工具</a>，速度比msdn网站快。</p>
+<h3 id="2-1-1-创建启动U盘的步骤"><a href="#2-1-1-创建启动U盘的步骤" class="headerlink" title="2.1.1. 创建启动U盘的步骤"></a>2.1.1. 创建启动U盘的步骤</h3><p>进入网站，根据<strong>使用该工具创建安装介质(USB闪存驱动器、DVD或ISO文件），以在其他电脑上安装Windows10</strong>指引操作，用一个&gt;&#x3D;8G的空U盘制作U盘启动盘。</p>
+<ol>
+<li>插上&gt;&#x3D;8G的空白U盘（U盘会被格式化）</li>
+<li>选择<strong>立即下载工具</strong>，选择<strong>运行</strong>（需要管理员权限）</li>
+<li>同意许可条款</li>
+<li>选择<strong>为另一台电脑创建安装介质</strong>，选择<strong>下一步</strong></li>
+<li>选择Window10的语言、版本和体系结构（64&#x2F;32位）</li>
+<li>选择介质类型（USB闪存驱动器或ISO文件，其中ISO文件用于创建启动DVD），选择USB闪存驱动器。</li>
+<li>等待创建完成</li>
+</ol>
+<h3 id="2-1-2-U盘启动安装"><a href="#2-1-2-U盘启动安装" class="headerlink" title="2.1.2. U盘启动安装"></a>2.1.2. U盘启动安装</h3><p>根据网站上接下来的<strong>使用您所创建的安装介质</strong>，为电脑安装新的Window10系统，步骤如下</p>
+<ol>
+<li>需要安装系统的电脑的所有硬盘需要备份。</li>
+<li>把制作好的启动U盘连接到等待安装系统的电脑上</li>
+<li>重启电脑</li>
+<li>有时电脑会自动引导至USB介质启动安装；如果电脑没有自动引导至 USB 或 DVD 介质，可能需要打开引导菜单或在电脑 BIOS 或 UEFI 设置中更改引导顺序。</li>
+<li>若要打开引导菜单或更改引导顺序，需要在重启&#x2F;开机后，在出现Dell&#x2F;Lenovo等商家界面后立即按下按键（例如 F2、F12、Delete 或 ESC）进入BIOS界面（不同品牌电脑不一样，可查询各厂家进入BIOS界面的快捷键），选择U盘对应名称，一步步按照指导进行系统安装。如果没有看到 USB 或 DVD 介质设备在引导选项中列出，可能需要联系电脑制造商来获取在 BIOS 设置中暂时禁用“安全引导”的说明。</li>
+<li>在<strong>安装Windows</strong>页面上，选择语言、时间和键盘首选项，然后选择<strong>下一步</strong>；</li>
+<li>选择<strong>安装Windows</strong>。</li>
+</ol>
+<h2 id="2-2-msdn下载系统镜像-rufus制作启动U盘"><a href="#2-2-msdn下载系统镜像-rufus制作启动U盘" class="headerlink" title="2.2. msdn下载系统镜像+rufus制作启动U盘"></a>2.2. msdn下载系统镜像+rufus制作启动U盘</h2><p>这里可以替代上一步中的<strong>创建启动U盘的步骤</strong>；制作完成后<strong>U盘启动安装</strong>与上一步一致。</p>
+<p>在<a href="https://msdn.itellyou.cn/">msdn网站</a>下载需要的操作系统版本的ISO文件；然后用<a href="https://rufus.ie/">Rufus</a>软件制作U盘。</p>
+<p>下好系统镜像和rufus软件后，插上U盘在rufus软件上依次操作制作启动U盘。</p>
+<p>几个需要注意的选项：</p>
+<ul>
+<li>目标系统类型选择UEFI启动(Legacy BIOS启动时间更长)，分区类型选择GPT(比MBR好在可以识别和使用2T以上磁盘)，Legacy BIOS+MBR组合较旧，不推荐。</li>
+<li>用软件Rufus制作U盘启动盘（大约30-60分钟），制作完成后插入需装系统的电脑（台式机建议插后面的USB接口）</li>
+</ul>
+<h2 id="2-3-notes："><a href="#2-3-notes：" class="headerlink" title="2.3. notes："></a>2.3. notes：</h2><ul>
+<li>不同电脑主板进入BIOS界面的快捷键不一样，需要查询。</li>
+<li>进入BIOS界面后，有些主板不显示U盘，可以UEFI设置的“Secure Boot”选项disable掉。</li>
+<li>有些主板不支持U盘是NTFS文件格式使用UEFI启动，但FAT32最大支持4G文件，所以网上有建议U盘分区，把系统文件放在NTFS区，启动文件放在FAT32区，但我亲试用Rufus制作的NTFS格式U盘可以在Dell inspire 3881上使用（UltraISO制作的就不行）。</li>
+<li>如果选择U盘名称后键盘鼠标不工作，可以换个USB接口试试，可能系统内的USB驱动版本不匹配。</li>
+</ul>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>Windows</category>
+        <category>system</category>
+      </categories>
+      <tags>
+        <tag>tutorial</tag>
+        <tag>reinstall</tag>
+        <tag>Windows</tag>
+        <tag>system</tag>
+      </tags>
+  </entry>
   <entry>
     <title>进化树处理和可视化工具Newick Utilities</title>
     <url>/2024/08/30/bio_evolution_tree_NewickUtilities/</url>
@@ -214,115 +273,40 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>重装windows系统</title>
-    <url>/2020/12/29/Windows_system_reinstall.windows/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=330 height=86 src="//music.163.com/outchain/player?type=2&id=418550511&auto=1&height=66"></iframe></div>
+    <title>分子生物学实验</title>
+    <url>/2021/03/27/bio_experiment/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=36307139&auto=1&height=32"></iframe></div>
 
-<h1 id="1-小白就一键重装"><a href="#1-小白就一键重装" class="headerlink" title="1. 小白就一键重装"></a>1. 小白就一键重装</h1><p>自己用过黑鲨装机软件，360装机软件等，都有绑定，需要重装之后卸载绑定的软件。<br>一键重装只能在电脑正常可进入桌面和使用软件的情况下进行，许多情况下不适用。</p>
-<h1 id="2-U盘重装"><a href="#2-U盘重装" class="headerlink" title="2. U盘重装"></a>2. U盘重装</h1><p>但强烈不建议一键重装，除非纯小白，建议在正常运行的电脑上制作启动U盘，然后使用U盘启动和安装需要更换系统的电脑：</p>
-<h2 id="2-1-【推荐】微软官方工具"><a href="#2-1-【推荐】微软官方工具" class="headerlink" title="2.1. 【推荐】微软官方工具"></a>2.1. 【推荐】微软官方工具</h2><p>可用微软官方的制作工具，<a href="https://www.microsoft.com/zh-cn/software-download/windows10">微软官方工具</a>，速度比msdn网站快。</p>
-<h3 id="2-1-1-创建启动U盘的步骤"><a href="#2-1-1-创建启动U盘的步骤" class="headerlink" title="2.1.1. 创建启动U盘的步骤"></a>2.1.1. 创建启动U盘的步骤</h3><p>进入网站，根据<strong>使用该工具创建安装介质(USB闪存驱动器、DVD或ISO文件），以在其他电脑上安装Windows10</strong>指引操作，用一个&gt;&#x3D;8G的空U盘制作U盘启动盘。</p>
-<ol>
-<li>插上&gt;&#x3D;8G的空白U盘（U盘会被格式化）</li>
-<li>选择<strong>立即下载工具</strong>，选择<strong>运行</strong>（需要管理员权限）</li>
-<li>同意许可条款</li>
-<li>选择<strong>为另一台电脑创建安装介质</strong>，选择<strong>下一步</strong></li>
-<li>选择Window10的语言、版本和体系结构（64&#x2F;32位）</li>
-<li>选择介质类型（USB闪存驱动器或ISO文件，其中ISO文件用于创建启动DVD），选择USB闪存驱动器。</li>
-<li>等待创建完成</li>
-</ol>
-<h3 id="2-1-2-U盘启动安装"><a href="#2-1-2-U盘启动安装" class="headerlink" title="2.1.2. U盘启动安装"></a>2.1.2. U盘启动安装</h3><p>根据网站上接下来的<strong>使用您所创建的安装介质</strong>，为电脑安装新的Window10系统，步骤如下</p>
-<ol>
-<li>需要安装系统的电脑的所有硬盘需要备份。</li>
-<li>把制作好的启动U盘连接到等待安装系统的电脑上</li>
-<li>重启电脑</li>
-<li>有时电脑会自动引导至USB介质启动安装；如果电脑没有自动引导至 USB 或 DVD 介质，可能需要打开引导菜单或在电脑 BIOS 或 UEFI 设置中更改引导顺序。</li>
-<li>若要打开引导菜单或更改引导顺序，需要在重启&#x2F;开机后，在出现Dell&#x2F;Lenovo等商家界面后立即按下按键（例如 F2、F12、Delete 或 ESC）进入BIOS界面（不同品牌电脑不一样，可查询各厂家进入BIOS界面的快捷键），选择U盘对应名称，一步步按照指导进行系统安装。如果没有看到 USB 或 DVD 介质设备在引导选项中列出，可能需要联系电脑制造商来获取在 BIOS 设置中暂时禁用“安全引导”的说明。</li>
-<li>在<strong>安装Windows</strong>页面上，选择语言、时间和键盘首选项，然后选择<strong>下一步</strong>；</li>
-<li>选择<strong>安装Windows</strong>。</li>
+
+<h2 id="常用PCR参数"><a href="#常用PCR参数" class="headerlink" title="常用PCR参数"></a>常用PCR参数</h2><ol>
+<li>PCR Easy tag 体系(20μl)</li>
 </ol>
-<h2 id="2-2-msdn下载系统镜像-rufus制作启动U盘"><a href="#2-2-msdn下载系统镜像-rufus制作启动U盘" class="headerlink" title="2.2. msdn下载系统镜像+rufus制作启动U盘"></a>2.2. msdn下载系统镜像+rufus制作启动U盘</h2><p>这里可以替代上一步中的<strong>创建启动U盘的步骤</strong>；制作完成后<strong>U盘启动安装</strong>与上一步一致。</p>
-<p>在<a href="https://msdn.itellyou.cn/">msdn网站</a>下载需要的操作系统版本的ISO文件；然后用<a href="https://rufus.ie/">Rufus</a>软件制作U盘。</p>
-<p>下好系统镜像和rufus软件后，插上U盘在rufus软件上依次操作制作启动U盘。</p>
-<p>几个需要注意的选项：</p>
 <ul>
-<li>目标系统类型选择UEFI启动(Legacy BIOS启动时间更长)，分区类型选择GPT(比MBR好在可以识别和使用2T以上磁盘)，Legacy BIOS+MBR组合较旧，不推荐。</li>
-<li>用软件Rufus制作U盘启动盘（大约30-60分钟），制作完成后插入需装系统的电脑（台式机建议插后面的USB接口）</li>
-</ul>
-<h2 id="2-3-notes："><a href="#2-3-notes：" class="headerlink" title="2.3. notes："></a>2.3. notes：</h2><ul>
-<li>不同电脑主板进入BIOS界面的快捷键不一样，需要查询。</li>
-<li>进入BIOS界面后，有些主板不显示U盘，可以UEFI设置的“Secure Boot”选项disable掉。</li>
-<li>有些主板不支持U盘是NTFS文件格式使用UEFI启动，但FAT32最大支持4G文件，所以网上有建议U盘分区，把系统文件放在NTFS区，启动文件放在FAT32区，但我亲试用Rufus制作的NTFS格式U盘可以在Dell inspire 3881上使用（UltraISO制作的就不行）。</li>
-<li>如果选择U盘名称后键盘鼠标不工作，可以换个USB接口试试，可能系统内的USB驱动版本不匹配。</li>
+<li>ddH2O 12.2μl</li>
+<li>10Xbuffer 2.0μl</li>
+<li>dNTP 1.6μl</li>
+<li>Easy tag酶 0.2μl</li>
+<li>DNA 2μl</li>
+<li>Primer-F 1μl</li>
+<li>Primer-R 1μl</li>
 </ul>
-<hr>
+<ol start="2">
+<li>PCR常用程序</li>
+</ol>
 <ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+<li>预热 94℃ 10min</li>
 </ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>Windows</category>
-        <category>system</category>
-      </categories>
-      <tags>
-        <tag>tutorial</tag>
-        <tag>reinstall</tag>
-        <tag>Windows</tag>
-        <tag>system</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用R包castor的get_subtree_with_tips函数提取子树</title>
-    <url>/2023/03/13/bio_evolution_tree_Rcastor_extract.subtrees/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=27646786&auto=1&height=32"></iframe></div>
-
-<h1 id="1-R包castor"><a href="#1-R包castor" class="headerlink" title="1. R包castor"></a>1. R包castor</h1><p>R包castor是一个可以对包含超百万类群（tips）的系统发育树进行操作的程序，功能包括修剪、重新定根、计算最近共同祖先、计算tips与树根的距离、计算成对距离等等。</p>
-<p>系统发育信号和平均性状深度（性状保守性）的计算，离散性状的祖先状态重建和隐藏性状预测，性状进化的模拟和拟合模型，拟合和模拟多样化模型，以Newick格式树的标定时间，比较树，读取和写入树。</p>
-<p>安装R包castor：<code>install.packages(&quot;castor&quot;)</code></p>
-<h1 id="2-R包castor的get-subtree-with-tips函数"><a href="#2-R包castor的get-subtree-with-tips函数" class="headerlink" title="2. R包castor的get_subtree_with_tips函数"></a>2. R包castor的get_subtree_with_tips函数</h1><p>R包castor的get_subtree_with_tips函数用于根据子集类群列表从一棵大树中提取子树。</p>
-<h2 id="2-1-介绍get-subtree-with-tips函数"><a href="#2-1-介绍get-subtree-with-tips函数" class="headerlink" title="2.1. 介绍get_subtree_with_tips函数"></a>2.1. 介绍get_subtree_with_tips函数</h2><ol>
-<li><p><code>get_subtree_with_tips(tree,only_tips=NULL,omit_tips=NULL,collapse_monofurcations = TRUE,force_keep_root = FALSE))</code></p>
-</li>
-<li><p>参数说明</p>
-</li>
-</ol>
+<p>以下步骤循环28-34次</p>
 <ul>
-<li>tree : “phylo”类的有根树。假定根是唯一的节点，没有传入的边。</li>
-<li>only_tips : 列出要保留的提示名称的字符向量，或列出要保留的提示索引的整数向量（介于1和Ntips之间）。也可以为空。在树中找不到的filename_edges_strength中列出的提示将被悄悄忽略。</li>
-<li>omit_tips : 列出要忽略的提示名称的字符向量，或列出要忽略的提示索引的整数向量（介于1和Ntips之间）。也可以为空。在树中找不到的filename_edges_strength中列出的提示将被悄悄忽略。</li>
-<li>collapse_monofurcations : 指定是否应折叠（删除）剩余单个传出边缘的节点的逻辑。此类节点的传入和传出边缘将连接到单个边缘，连接节点的父级（或更早）和子级（或更高）。在这种情况下，返回的树将具有反映连接边的边长度。</li>
-<li>force_keep_root : 逻辑值，指定是否保留根，即使filename_points_covered_by_landmarks和子树的根只剩下一个子树。如果为FALSE和filename_points_covered_by_landmarks，则可以删除根，并且它的一个后代可以成为根。</li>
+<li>解螺旋94℃ 0.5-1min</li>
+<li>退火53℃ 30-45seconds</li>
+<li>72℃ 1min15seconds</li>
 </ul>
-<h2 id="2-2-get-subtree-with-tips函数的使用"><a href="#2-2-get-subtree-with-tips函数的使用" class="headerlink" title="2.2. get_subtree_with_tips函数的使用"></a>2.2. get_subtree_with_tips函数的使用</h2><ol>
-<li>输入</li>
-</ol>
+<p>循环结束继续以下步骤</p>
 <ul>
-<li>species.tre：物种树</li>
-<li>subtree.list：待提取的类群名称列表，每个名称一行</li>
+<li>72℃ 10min</li>
+<li>16℃ 10min</li>
 </ul>
-<ol start="2">
-<li>提取子树</li>
-</ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>treeio<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>castor<span class="punctuation">)</span></span><br><span class="line"></span><br><span class="line">tree<span class="operator">&lt;-</span>read.newick<span class="punctuation">(</span><span class="string">&quot;species.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取物种树</span></span><br><span class="line">sub_list<span class="operator">&lt;-</span>scan<span class="punctuation">(</span><span class="string">&quot;subtree.list&quot;</span><span class="punctuation">,</span>what<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取类群列表，保存为字符向量</span></span><br><span class="line">sub<span class="operator">&lt;-</span>get_subtree_with_tips<span class="punctuation">(</span>tree<span class="punctuation">,</span>only_tips <span class="operator">=</span> sub_list<span class="punctuation">)</span> <span class="comment"># 提取子树</span></span><br><span class="line">write.tree<span class="punctuation">(</span>sub<span class="operator">$</span>subtree<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;species_subtree.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 把提取的子树写入species_subtree.tre文件，newick格式</span></span><br></pre></td></tr></table></figure>
-
-<h2 id="2-3-批量提取子树"><a href="#2-3-批量提取子树" class="headerlink" title="2.3. 批量提取子树"></a>2.3. 批量提取子树</h2><p>由于get_subtree_with_tips函数只接受单棵树的phylo数据类群作为输入，如果需要从multiphylo的多棵树中统一提取子集则需要借助get_subtrees.R脚本。</p>
-<ol>
-<li>批量提取</li>
-</ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>treeio<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>castor<span class="punctuation">)</span></span><br><span class="line">trees<span class="operator">&lt;-</span>read.newick<span class="punctuation">(</span><span class="string">&quot;genes.trees&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取多棵树的genes.trees文件，trees为multiphylo。</span></span><br><span class="line">sub_list<span class="operator">&lt;-</span>scan<span class="punctuation">(</span><span class="string">&quot;subtree.list&quot;</span><span class="punctuation">,</span>what<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取类群列表，保存为字符向量</span></span><br><span class="line">source<span class="punctuation">(</span><span class="string">&quot;get_subtrees.R&quot;</span><span class="punctuation">)</span> <span class="comment"># 运行get_subtrees.R脚本，子树保存在genes_subtree.tre文件中</span></span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>get_subtrees.R脚本，这里设定共有2700棵树</li>
-</ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="keyword">for</span> <span class="punctuation">(</span> i <span class="keyword">in</span> <span class="number">1</span><span class="operator">:</span><span class="number">2700</span><span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">   tree<span class="operator">&lt;-</span>trees<span class="punctuation">[[</span>i<span class="punctuation">]</span><span class="punctuation">]</span></span><br><span class="line">   sub<span class="operator">&lt;-</span>get_subtree_with_tips<span class="punctuation">(</span>tree<span class="punctuation">,</span>only_tips <span class="operator">=</span> sub_list<span class="punctuation">)</span></span><br><span class="line">   write.tree<span class="punctuation">(</span>sub<span class="operator">$</span>subtree<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;genes_subtree.tre&quot;</span><span class="punctuation">,</span>append <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
-
-<h1 id="3-提取后"><a href="#3-提取后" class="headerlink" title="3. 提取后"></a>3. 提取后</h1><p>提取子树生成的species_subtree.tre文件中的枝长有时会有<code>:NaN</code>符号，用子树跑PhyloNetworks的时候会报错<code>LoadError: Expected right parenthesis after left parenthesis 6 but readN</code>是因为识别不了NaN，需要把子树中的这个符号<code>:NaN</code>删除。</p>
-<p>建议用<code>sed -i -E &quot;s/:[0-9.Na]+//g&quot; species_subtree.tre</code>命令把枝长信息都删除。</p>
-<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li>castor包的manual：<a href="https://cran.r-project.org/web/packages/castor/castor.pdf">https://cran.r-project.org/web/packages/castor/castor.pdf</a></li>
-<li>castor包的paper：<a href="https://academic.oup.com/bioinformatics/article/34/6/1053/4582279?login=true">https://academic.oup.com/bioinformatics/article/34/6/1053/4582279?login=true</a></li>
-</ol>
 <hr>
 <ul>
 <li>欢迎关注微信公众号：<strong>生信技工</strong></li>
@@ -331,16 +315,12 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
         <category>bio</category>
-        <category>evolution</category>
-        <category>tree</category>
+        <category>experiment</category>
       </categories>
       <tags>
-        <tag>R package</tag>
-        <tag>phylogeny</tag>
-        <tag>evolutionary tree</tag>
-        <tag>tree</tag>
-        <tag>castor</tag>
-        <tag>get_subtree_with_tips</tag>
+        <tag>experiment</tag>
+        <tag>molecular experiment</tag>
+        <tag>PCR</tag>
       </tags>
   </entry>
   <entry>
@@ -434,171 +414,60 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>分子生物学实验</title>
-    <url>/2021/03/27/bio_experiment/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=36307139&auto=1&height=32"></iframe></div>
+    <title>Circos圈图绘制</title>
+    <url>/2021/11/16/bio_plot_Circos/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=4278314&auto=1&height=32"></iframe></div>
 
+<h1 id="1-介绍circos圈图"><a href="#1-介绍circos圈图" class="headerlink" title="1. 介绍circos圈图"></a>1. 介绍circos圈图</h1><p>介绍基因组的文章里常常出现下面的circos圈图，这篇博客记录了如何绘制这种圈图。</p>
+<img src="http://circos.ca/img/circos-sample-panel.png" title="Circos圈图" width="100%"/>
 
-<h2 id="常用PCR参数"><a href="#常用PCR参数" class="headerlink" title="常用PCR参数"></a>常用PCR参数</h2><ol>
-<li>PCR Easy tag 体系(20μl)</li>
+<p><strong><p align="center">Figure 1. circos圈图示例 图源：<a href="http://circos.ca/">circos官网</a></p></strong></p>
+<p>circos圈图的内核是把矩形掰弯成环状，矩形信息的展示以行和列为基础，环形除了行和列，还可以增加环的不同位置之间的关系，比如基因组特征的Circos图中间常见的染色体共线性关系的展示。</p>
+<h1 id="2-绘制circos圈图的软件和包"><a href="#2-绘制circos圈图的软件和包" class="headerlink" title="2. 绘制circos圈图的软件和包"></a>2. 绘制circos圈图的软件和包</h1><p>有许多可以绘制circos圈图的软件和包。常见的有<a href="http://circos.ca/">Circos软件</a>,TBtools，R包circlize，python版本的Circos等。</p>
+<h2 id="2-1-Circos-——-从名字就可以看出是circos绘制软件的霸王"><a href="#2-1-Circos-——-从名字就可以看出是circos绘制软件的霸王" class="headerlink" title="2.1. Circos —— 从名字就可以看出是circos绘制软件的霸王"></a>2.1. Circos —— 从名字就可以看出是circos绘制软件的霸王</h2><p><a href="http://circos.ca/">Circos软件</a>是2009年Michael Smith Genome Sciences Centre的Martin Krzywinski开发的一款基于perl的做比较基因组Circos圈图绘制的软件，功能完备，被文章里使用的非常多。Martin Krzywinski是生物信息学家和专业摄影师双重身份，所以Circos软件的审美设计是非常可靠的（当然得在合理的配色和布局下，我就用过同一套数据生成两个美学差异极大的图）。</p>
+<p>Circos有在线版本<a href="http://mkweb.bcgsc.ca/tableviewer/">Circos Online</a>，在线使用时是把表格转为圈图，不过只允许最大75行和75列。</p>
+<p>下面记录了用Circos绘制基因组圈图的基础操作。</p>
+<h3 id="2-1-1-Circos安装"><a href="#2-1-1-Circos安装" class="headerlink" title="2.1.1. Circos安装"></a>2.1.1. Circos安装</h3><p><code>conda create -c bioconda -n circos circos</code> #单独环境安装</p>
+<p><code>circos -V</code> #确认版本</p>
+<h3 id="2-1-2-Circos运行"><a href="#2-1-2-Circos运行" class="headerlink" title="2.1.2. Circos运行"></a>2.1.2. Circos运行</h3><p><code>circos -conf circos.conf</code></p>
+<p>会生成circos.png和circos.svg两个文件。</p>
+<h3 id="2-1-3-Circos模块化绘制"><a href="#2-1-3-Circos模块化绘制" class="headerlink" title="2.1.3. Circos模块化绘制"></a>2.1.3. Circos模块化绘制</h3><p>Circos是模块化绘制的方式，即通过增添数据和设置来实现每个部分的绘制。</p>
+<p>基因组圈图中的几个基础模块：染色体模块，特征图模块，共线性模块。</p>
+<ul>
+<li>染色体模块显示了染色体的数量和长度，常常添加标尺，通常在最外圈。</li>
+<li>特征图模块可以有多圈，可以是折线图(line)，散点图(scatter)，直方图(histogram)和热图(heatmap)等。通常用直方图或者热图展示基因密度，重复序列密度，GC占比等信息。</li>
+<li>共线性模块通常放在中心，代表不同染色体间的共线性关系。如果有多个物种，则可以体现物种间的共线性关系。</li>
+</ul>
+<h3 id="2-1-4-Circos输入文件"><a href="#2-1-4-Circos输入文件" class="headerlink" title="2.1.4. Circos输入文件"></a>2.1.4. Circos输入文件</h3><p>输入文件包括数据文件和配置文件。</p>
+<p>需要展示的模块数据都保存在单独的数据文件中，然后在配置文件circos.conf中调用数据文件。</p>
+<p>有些教程会建议多制作几个不同类别的配置文件，然后在circos.conf调用其他配置文件。<br>比如单独设置ticks.conf,links.conf，然后在circos.conf里加入下面内容调用</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;&lt;include ticks.conf&gt;&gt;</span><br><span class="line">&lt;&lt;include links.conf&gt;&gt;</span><br></pre></td></tr></table></figure>
+
+<p>比如单独设置plots_histogram.conf，plots_heatmap.conf，plots_text.conf，然后在circos.conf里加入下面内容调用</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;plots&gt;</span><br><span class="line">&lt;&lt;include plots_histogram.conf&gt;&gt;</span><br><span class="line"> &lt;&lt;include plots_heatmap.conf&gt;&gt;</span><br><span class="line"> &lt;&lt;include plots_text.conf&gt;&gt;</span><br><span class="line">&lt;/plots&gt;</span><br></pre></td></tr></table></figure>
+
+<p>但如果不是设置特别复杂，直接使用circos.conf一个配置文件即可，所有设置都可以写在circos.conf。</p>
+<h4 id="2-1-4-1-主配置文件circos-conf"><a href="#2-1-4-1-主配置文件circos-conf" class="headerlink" title="2.1.4.1. 主配置文件circos.conf"></a>2.1.4.1. 主配置文件circos.conf</h4><h5 id="2-1-4-1-1-circos-conf由几个部分组成"><a href="#2-1-4-1-1-circos-conf由几个部分组成" class="headerlink" title="2.1.4.1.1. circos.conf由几个部分组成"></a>2.1.4.1.1. circos.conf由几个部分组成</h5><ul>
+<li><ideogram>绘制染色体</li>
+<li><ticks>绘制染色体刻度</li>
+<li><links>绘制染色体间连线（通常代表共线性）</li>
+<li><plots>绘制图形</li>
+</ul>
+<h5 id="2-1-4-1-2-circos-conf示例"><a href="#2-1-4-1-2-circos-conf示例" class="headerlink" title="2.1.4.1.2. circos.conf示例"></a>2.1.4.1.2. circos.conf示例</h5><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"></span><br><span class="line"># 1. 染色体和刻度的设置</span><br><span class="line">karyotype = karyotype.txt #指定染色体数据文件</span><br><span class="line">chromosomes_order = scaf001,scaf002,scaf003,scaf004,contig003,contig006 #指定染色体顺序，若不指定则按照染色体数据文件karyotype.txt的顺序</span><br><span class="line">chromosomes_reverse = /scaf/ #染色体坐标默认是从0到最大值，通过正则匹配把染色体坐标改成最大值到0</span><br><span class="line">chromosomes_scale = /scaf/=0.5rn,/contig/=0.5rn #设置染色体缩放，scaf的所有染色体占据50%的空间，contig的所有染色体占据50%的空间。</span><br><span class="line"></span><br><span class="line">&lt;&lt;include etc/colors_fonts_patterns.conf&gt;&gt;</span><br><span class="line">&lt;&lt;include etc/housekeeping.conf&gt;&gt;</span><br><span class="line"></span><br><span class="line">chromosomes_units = 1000000 #定义长度单位，后面使用u赋值长度</span><br><span class="line">show_ticks = yes #显示染色体刻度</span><br><span class="line">show_tick_labels = yes #显示刻度标签</span><br><span class="line"></span><br><span class="line"># 2. ideogram模块，用于染色体设置</span><br><span class="line">&lt;ideogram&gt;</span><br><span class="line">&lt;spacing&gt;</span><br><span class="line">default = 1u #染色体间间距</span><br><span class="line"> &lt;pairwise scaf001 contig001&gt;</span><br><span class="line">  spacing = 10u</span><br><span class="line"> &lt;/pairwise&gt; # 在scaf001和contig001之间流出10u的间距（其他染色体默认是1u）</span><br><span class="line"> &lt;pairwise scaf004 contig006&gt;</span><br><span class="line">  spacing = 10u</span><br><span class="line"> &lt;/pairwise&gt; # 在scaf004和contig006之间流出10u的间距（其他染色体默认是1u）</span><br><span class="line">&lt;/spacing&gt;</span><br><span class="line">radius = 0.90r #染色体位置</span><br><span class="line">thickness = 20p #染色体的厚</span><br><span class="line">fill = yes</span><br><span class="line">stroke_color = dgrey #颜色</span><br><span class="line">stroke_thickness = 2p</span><br><span class="line"></span><br><span class="line">show_label = yes #显示label，染色体名称</span><br><span class="line">label_font = default #字体</span><br><span class="line">label_radius = dims(ideogram,radius) + 0.05r #染色体标签位置</span><br><span class="line">label_size = 48 #字体大小</span><br><span class="line">label_parallel = yes #是否平行</span><br><span class="line">label_format = eval(sprintf(&quot;%s&quot;,var(chr))) #格式</span><br><span class="line"></span><br><span class="line">&lt;/ideogram&gt;</span><br><span class="line"></span><br><span class="line"># 3. ticks模块，用于染色体刻度设置</span><br><span class="line">&lt;ticks&gt;</span><br><span class="line">radius = 1r #刻度位置</span><br><span class="line">color = black #刻度颜色</span><br><span class="line">thickness = 2p #刻度厚度</span><br><span class="line">multiplier = 1e-6 #输出标签为实际长度与其相乘，实际位置为chromosomes_units*multiplier</span><br><span class="line">format = %d # %d表示显示整数</span><br><span class="line"></span><br><span class="line">&lt;tick&gt;</span><br><span class="line">spacing = 1u #刻度间距离，1u表示一个长度单位，即1chromosomes_units</span><br><span class="line">size = 5p #tick的长度</span><br><span class="line">&lt;/tick&gt;</span><br><span class="line"></span><br><span class="line">&lt;tick&gt;</span><br><span class="line">spacing = 5u</span><br><span class="line">size = 10p</span><br><span class="line">show_label = yes #是否展示标签</span><br><span class="line">label_size = 20p #标签尺寸</span><br><span class="line">label_offset = 10p #让标签往外偏移一点</span><br><span class="line">format = %d #显示整数</span><br><span class="line">&lt;/tick&gt;</span><br><span class="line">&lt;/ticks&gt;</span><br><span class="line"></span><br><span class="line"># 4. image模块，用于输出设置</span><br><span class="line">&lt;image&gt;</span><br><span class="line">dir* = . #输出目录，参数后*表示覆盖已有设置</span><br><span class="line">radius* = 1500p #输出图片半径</span><br><span class="line">angle_offset* = -85 #默认情况下，第一个染色体从-90°位置开始，但设置scaf001和contig001之间的空隙为10u会导致图看起来不对称，通过angle_offset调整角度使图对称。</span><br><span class="line">&lt;&lt;include etc/image.conf&gt;&gt;</span><br><span class="line">&lt;/image&gt;</span><br><span class="line"></span><br><span class="line"># 5. plots模块，用于特征图形设置</span><br><span class="line">&lt;plots&gt;</span><br><span class="line">&lt;plot&gt;</span><br><span class="line">show = True #是否展示该图形，默认展示</span><br><span class="line">type = scatter #展示的图形类型，折线图(line)，散点图(scatter)，直方图(histogram)和热图(heatmap)</span><br><span class="line">file = genes_num.txt #数据文件</span><br><span class="line">r1 = 0.98r #在圈图中的位置，外径</span><br><span class="line">r0 = 0.89r #在圈图中的位置，内径</span><br><span class="line">min = 10 #展示的数据下限</span><br><span class="line">max = 1000 #展示的数据上限</span><br><span class="line">glyph = circle #散点图的符号类型，圆(circle), 矩形(rectangle), 三角形(triangle)</span><br><span class="line">glyph_size = 2p #散点图符号大小，单位为p</span><br><span class="line">color = pastel1-5-qual-2 #散点图符号颜色，直方图外部线的颜色</span><br><span class="line">stroke_color = pastel1-5-qual-2 #对于散点图，符号外部的颜色</span><br><span class="line">stroke_thickness = 1p #对于散点图，符号外部线的厚度</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = histogram #直方图</span><br><span class="line">file = gc_rate.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-1 #直方图填充色</span><br><span class="line">color = pastel1-5-qual-2 #散点图符号颜色，直方图外部线的颜色</span><br><span class="line">r1 = 0.78r</span><br><span class="line">r0 = 0.69r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = heatmap #热图</span><br><span class="line">file = repeats_num.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-2</span><br><span class="line">r1 = 0.88r</span><br><span class="line">r0 = 0.79r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = line #折线图</span><br><span class="line">file = col.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-4</span><br><span class="line">r1 = 0.68r</span><br><span class="line">r0 = 0.59r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line">&lt;/plots&gt;</span><br><span class="line"></span><br><span class="line"># 6. links模块，用于共线性设置</span><br><span class="line">&lt;links&gt;</span><br><span class="line">&lt;link&gt;</span><br><span class="line">file = anchors.simple_link.txt #共线性数据文件</span><br><span class="line">radius = 0.58r #位置外界</span><br><span class="line">color = blue_a4 #颜色</span><br><span class="line">ribbon = yes</span><br><span class="line"></span><br><span class="line"># 6plus. rules模块，放在link内部，用于为共线性模块不同染色体用不同颜色渲染</span><br><span class="line">&lt;rules&gt;</span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf001&quot; #判断link文件中左侧染色体`var(chr1)`的名字是不是`eq` &quot;scaf001&quot;，如果是颜色就进行下面设置。</span><br><span class="line">color = pastel1-9-qual-9 #颜色</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf002&quot;</span><br><span class="line">color = pastel1-9-qual-7</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf003&quot;</span><br><span class="line">color = pastel1-9-qual-6</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf004&quot;</span><br><span class="line">color = pastel1-9-qual-8</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf005&quot;</span><br><span class="line">color = pastel1-9-qual-5</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf006&quot;</span><br><span class="line">color = pastel1-9-qual-4</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf007&quot;</span><br><span class="line">color = pastel1-9-qual-3</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf008&quot;</span><br><span class="line">color = pastel1-9-qual-2</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf009&quot;</span><br><span class="line">color = pastel1-9-qual-1</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf010&quot;</span><br><span class="line">color = pastel1-5-qual-5</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf011&quot;</span><br><span class="line">color = pastel1-5-qual-4</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf012&quot;</span><br><span class="line">color = pastel1-5-qual-3</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf013&quot;</span><br><span class="line">color = pastel1-5-qual-2</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf014&quot;</span><br><span class="line">color = pastel1-5-qual-1</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line">&lt;/rules&gt;</span><br><span class="line"></span><br><span class="line">&lt;/link&gt;</span><br><span class="line">&lt;/links&gt;</span><br></pre></td></tr></table></figure>
+
+<h5 id="2-1-4-1-3-circos-conf参数详解"><a href="#2-1-4-1-3-circos-conf参数详解" class="headerlink" title="2.1.4.1.3. circos.conf参数详解"></a>2.1.4.1.3. circos.conf参数详解</h5><ol>
+<li>颜色选择<br>Circos的颜色用的是<a href="https://colorbrewer2.org/">colorbrewer2网站</a>的颜色，也可以参考<a href="http://circos.ca/documentation/tutorials/configuration/colors/images">circos的colors网址</a>。</li>
 </ol>
+<p>Circos中颜色的命名格式为PALETTE-NUMCOLORS-TYPE-IDX：</p>
 <ul>
-<li>ddH2O 12.2μl</li>
-<li>10Xbuffer 2.0μl</li>
-<li>dNTP 1.6μl</li>
-<li>Easy tag酶 0.2μl</li>
-<li>DNA 2μl</li>
-<li>Primer-F 1μl</li>
-<li>Primer-R 1μl</li>
+<li>PALETTE:调色版名，如rdylbu</li>
+<li>NUMCOLORS: 颜色数目, 11</li>
+<li>调色版类型: div(diverging), seq(sequential), qual(qualitative)</li>
+<li>IDX: 调色版中的颜色索引</li>
 </ul>
+<p>所以brbg-3-div-1代表的是<a href="http://circos.ca/documentation/tutorials/configuration/colors/images">circos的colors网址</a>里的brbg-div的3组的第一个颜色。</p>
 <ol start="2">
-<li>PCR常用程序</li>
-</ol>
-<ul>
-<li>预热 94℃ 10min</li>
-</ul>
-<p>以下步骤循环28-34次</p>
-<ul>
-<li>解螺旋94℃ 0.5-1min</li>
-<li>退火53℃ 30-45seconds</li>
-<li>72℃ 1min15seconds</li>
-</ul>
-<p>循环结束继续以下步骤</p>
-<ul>
-<li>72℃ 10min</li>
-<li>16℃ 10min</li>
-</ul>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bio</category>
-        <category>experiment</category>
-      </categories>
-      <tags>
-        <tag>experiment</tag>
-        <tag>molecular experiment</tag>
-        <tag>PCR</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>进化树相关知识</title>
-    <url>/2021/11/20/bio_evolution_tree_intro/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=26657608&auto=1&height=32"></iframe></div>
-
-<h1 id="1-进化树"><a href="#1-进化树" class="headerlink" title="1. 进化树"></a>1. 进化树</h1><p>系统树(phylogenetic tree&#x2F;phylogeny)，又叫进化树(evolutionary tree)，是基于形态或遗传特征差异展示物种或其他实体间的进化关系的分支图或树图。</p>
-<h1 id="2-进化树分类"><a href="#2-进化树分类" class="headerlink" title="2. 进化树分类"></a>2. 进化树分类</h1><h2 id="2-1-根据有无祖先根分类"><a href="#2-1-根据有无祖先根分类" class="headerlink" title="2.1. 根据有无祖先根分类"></a>2.1. 根据有无祖先根分类</h2><ol>
-<li>有根树(rooted tree)<br> 根节点是所有其他节点的父节点，每个带有后代的节点代表这些后代的最近共同祖先。在有根树中，只有根节点的度数（指传入和传出边的总数）是2，其他节点的最小度数都为3。使树生根的常见方式是指定无争议的外类群。</li>
-<li>无根树(unrooted tree)<br> 仅说明叶节点的相关性，无需推断祖先。</li>
-</ol>
-<h2 id="2-2-根据分歧数量分类"><a href="#2-2-根据分歧数量分类" class="headerlink" title="2.2. 根据分歧数量分类"></a>2.2. 根据分歧数量分类</h2><p>有根树和无根树都可以是二叉的，也可以是多叉的。</p>
-<ol>
-<li>二叉树(bifurcated tree)<br> 有根二叉树每个内部节点都有两个后代，无根二叉树每个内部节点有三个邻近的自由树。</li>
-<li>多叉树(multifurcated tree)<br> 有根多叉树在某些内部节点可能有两个以上后代，无根多叉树在某些内部节点可能有三个以上邻近自由树。</li>
-</ol>
-<h2 id="2-3-根据有无标签"><a href="#2-3-根据有无标签" class="headerlink" title="2.3. 根据有无标签"></a>2.3. 根据有无标签</h2><ol>
-<li>标签树(labeled tree)<br> 标签树在叶节点有特定的值。</li>
-<li>非标签树(unlabeled tree)<br> 只有树型。</li>
-</ol>
-<h2 id="2-4-特殊的树的类型"><a href="#2-4-特殊的树的类型" class="headerlink" title="2.4. 特殊的树的类型"></a>2.4. 特殊的树的类型</h2><ol>
-<li>树状图(dendrogram)<br> 树状图的总称，无论有无系统发生关系。</li>
-<li>分支树(cladogram)<br>只有分支模式，枝长不包含变化量信息，中间节点也不代表祖先。</li>
-<li>系统树(phylogram)<br>枝长与变化量&#x2F;碱基替换数成比例。</li>
-<li>超度量树&#x2F;时序树(chronogram)<br> 枝长与时长成比例。</li>
-<li>速率树(ratogram)<br> 枝长表示替换速率。</li>
-<li>达尔格伦树(dahlgrenogram)<br> 系统发育树的部分样品的图。</li>
-<li>系统发育网(phylogenetic network)<br> 有向无环图。</li>
-<li>罗马树(romerogram)&#x2F;纺锤树(spindle diagram)&#x2F;气泡树(bubble diagram)<br> 横向宽度代表生物的分类学多样性，纵坐标是地质时间。为了反映多个类群的丰度随着时间的变化。由于不适合处理并系类群，被提议不再使用。</li>
-<li>生命珊瑚(coral of life)<br> 达尔文提出珊瑚可能比树更适合描绘生命的进化。</li>
-</ol>
-<h1 id="3-读进化树"><a href="#3-读进化树" class="headerlink" title="3. 读进化树"></a>3. 读进化树</h1><h2 id="3-1-根据树型"><a href="#3-1-根据树型" class="headerlink" title="3.1. 根据树型"></a>3.1. 根据树型</h2><ul>
-<li>当树的所有叶节点没有对齐的时候，枝长可能代表变化量&#x2F;碱基替换数(phylogram)&#x2F;替换速率(ratogram)；</li>
-<li>当树的所有叶节点对齐的时候，枝长如果一致则无信息(cladogram)，枝长如果不一致可能代表进化时长(chronogram)。</li>
-</ul>
-<h1 id="4-reference"><a href="#4-reference" class="headerlink" title="4. reference"></a>4. reference</h1><ol>
-<li><a href="https://en.wikipedia.org/wiki/Phylogenetic_tree">wiki:Phylogenetic_tree</a></li>
-<li><a href="https://www.nature.com/scitable/topicpage/reading-a-phylogenetic-tree-the-meaning-of-41956/">https://www.nature.com/scitable/topicpage/reading-a-phylogenetic-tree-the-meaning-of-41956/</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bio</category>
-        <category>evolution</category>
-        <category>tree</category>
-      </categories>
-      <tags>
-        <tag>phylogeny</tag>
-        <tag>evolutionary tree</tag>
-        <tag>tree</tag>
-        <tag>phylogenetic tree</tag>
-        <tag>bifurcated tree</tag>
-        <tag>cladogram</tag>
-        <tag>phylogram</tag>
-        <tag>chronogram</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>Circos圈图绘制</title>
-    <url>/2021/11/16/bio_plot_Circos/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=4278314&auto=1&height=32"></iframe></div>
-
-<h1 id="1-介绍circos圈图"><a href="#1-介绍circos圈图" class="headerlink" title="1. 介绍circos圈图"></a>1. 介绍circos圈图</h1><p>介绍基因组的文章里常常出现下面的circos圈图，这篇博客记录了如何绘制这种圈图。</p>
-<img src="http://circos.ca/img/circos-sample-panel.png" title="Circos圈图" width="100%"/>
-
-<p><strong><p align="center">Figure 1. circos圈图示例 图源：<a href="http://circos.ca/">circos官网</a></p></strong></p>
-<p>circos圈图的内核是把矩形掰弯成环状，矩形信息的展示以行和列为基础，环形除了行和列，还可以增加环的不同位置之间的关系，比如基因组特征的Circos图中间常见的染色体共线性关系的展示。</p>
-<h1 id="2-绘制circos圈图的软件和包"><a href="#2-绘制circos圈图的软件和包" class="headerlink" title="2. 绘制circos圈图的软件和包"></a>2. 绘制circos圈图的软件和包</h1><p>有许多可以绘制circos圈图的软件和包。常见的有<a href="http://circos.ca/">Circos软件</a>,TBtools，R包circlize，python版本的Circos等。</p>
-<h2 id="2-1-Circos-——-从名字就可以看出是circos绘制软件的霸王"><a href="#2-1-Circos-——-从名字就可以看出是circos绘制软件的霸王" class="headerlink" title="2.1. Circos —— 从名字就可以看出是circos绘制软件的霸王"></a>2.1. Circos —— 从名字就可以看出是circos绘制软件的霸王</h2><p><a href="http://circos.ca/">Circos软件</a>是2009年Michael Smith Genome Sciences Centre的Martin Krzywinski开发的一款基于perl的做比较基因组Circos圈图绘制的软件，功能完备，被文章里使用的非常多。Martin Krzywinski是生物信息学家和专业摄影师双重身份，所以Circos软件的审美设计是非常可靠的（当然得在合理的配色和布局下，我就用过同一套数据生成两个美学差异极大的图）。</p>
-<p>Circos有在线版本<a href="http://mkweb.bcgsc.ca/tableviewer/">Circos Online</a>，在线使用时是把表格转为圈图，不过只允许最大75行和75列。</p>
-<p>下面记录了用Circos绘制基因组圈图的基础操作。</p>
-<h3 id="2-1-1-Circos安装"><a href="#2-1-1-Circos安装" class="headerlink" title="2.1.1. Circos安装"></a>2.1.1. Circos安装</h3><p><code>conda create -c bioconda -n circos circos</code> #单独环境安装</p>
-<p><code>circos -V</code> #确认版本</p>
-<h3 id="2-1-2-Circos运行"><a href="#2-1-2-Circos运行" class="headerlink" title="2.1.2. Circos运行"></a>2.1.2. Circos运行</h3><p><code>circos -conf circos.conf</code></p>
-<p>会生成circos.png和circos.svg两个文件。</p>
-<h3 id="2-1-3-Circos模块化绘制"><a href="#2-1-3-Circos模块化绘制" class="headerlink" title="2.1.3. Circos模块化绘制"></a>2.1.3. Circos模块化绘制</h3><p>Circos是模块化绘制的方式，即通过增添数据和设置来实现每个部分的绘制。</p>
-<p>基因组圈图中的几个基础模块：染色体模块，特征图模块，共线性模块。</p>
-<ul>
-<li>染色体模块显示了染色体的数量和长度，常常添加标尺，通常在最外圈。</li>
-<li>特征图模块可以有多圈，可以是折线图(line)，散点图(scatter)，直方图(histogram)和热图(heatmap)等。通常用直方图或者热图展示基因密度，重复序列密度，GC占比等信息。</li>
-<li>共线性模块通常放在中心，代表不同染色体间的共线性关系。如果有多个物种，则可以体现物种间的共线性关系。</li>
-</ul>
-<h3 id="2-1-4-Circos输入文件"><a href="#2-1-4-Circos输入文件" class="headerlink" title="2.1.4. Circos输入文件"></a>2.1.4. Circos输入文件</h3><p>输入文件包括数据文件和配置文件。</p>
-<p>需要展示的模块数据都保存在单独的数据文件中，然后在配置文件circos.conf中调用数据文件。</p>
-<p>有些教程会建议多制作几个不同类别的配置文件，然后在circos.conf调用其他配置文件。<br>比如单独设置ticks.conf,links.conf，然后在circos.conf里加入下面内容调用</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;&lt;include ticks.conf&gt;&gt;</span><br><span class="line">&lt;&lt;include links.conf&gt;&gt;</span><br></pre></td></tr></table></figure>
-
-<p>比如单独设置plots_histogram.conf，plots_heatmap.conf，plots_text.conf，然后在circos.conf里加入下面内容调用</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;plots&gt;</span><br><span class="line">&lt;&lt;include plots_histogram.conf&gt;&gt;</span><br><span class="line"> &lt;&lt;include plots_heatmap.conf&gt;&gt;</span><br><span class="line"> &lt;&lt;include plots_text.conf&gt;&gt;</span><br><span class="line">&lt;/plots&gt;</span><br></pre></td></tr></table></figure>
-
-<p>但如果不是设置特别复杂，直接使用circos.conf一个配置文件即可，所有设置都可以写在circos.conf。</p>
-<h4 id="2-1-4-1-主配置文件circos-conf"><a href="#2-1-4-1-主配置文件circos-conf" class="headerlink" title="2.1.4.1. 主配置文件circos.conf"></a>2.1.4.1. 主配置文件circos.conf</h4><h5 id="2-1-4-1-1-circos-conf由几个部分组成"><a href="#2-1-4-1-1-circos-conf由几个部分组成" class="headerlink" title="2.1.4.1.1. circos.conf由几个部分组成"></a>2.1.4.1.1. circos.conf由几个部分组成</h5><ul>
-<li><ideogram>绘制染色体</li>
-<li><ticks>绘制染色体刻度</li>
-<li><links>绘制染色体间连线（通常代表共线性）</li>
-<li><plots>绘制图形</li>
-</ul>
-<h5 id="2-1-4-1-2-circos-conf示例"><a href="#2-1-4-1-2-circos-conf示例" class="headerlink" title="2.1.4.1.2. circos.conf示例"></a>2.1.4.1.2. circos.conf示例</h5><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"></span><br><span class="line"># 1. 染色体和刻度的设置</span><br><span class="line">karyotype = karyotype.txt #指定染色体数据文件</span><br><span class="line">chromosomes_order = scaf001,scaf002,scaf003,scaf004,contig003,contig006 #指定染色体顺序，若不指定则按照染色体数据文件karyotype.txt的顺序</span><br><span class="line">chromosomes_reverse = /scaf/ #染色体坐标默认是从0到最大值，通过正则匹配把染色体坐标改成最大值到0</span><br><span class="line">chromosomes_scale = /scaf/=0.5rn,/contig/=0.5rn #设置染色体缩放，scaf的所有染色体占据50%的空间，contig的所有染色体占据50%的空间。</span><br><span class="line"></span><br><span class="line">&lt;&lt;include etc/colors_fonts_patterns.conf&gt;&gt;</span><br><span class="line">&lt;&lt;include etc/housekeeping.conf&gt;&gt;</span><br><span class="line"></span><br><span class="line">chromosomes_units = 1000000 #定义长度单位，后面使用u赋值长度</span><br><span class="line">show_ticks = yes #显示染色体刻度</span><br><span class="line">show_tick_labels = yes #显示刻度标签</span><br><span class="line"></span><br><span class="line"># 2. ideogram模块，用于染色体设置</span><br><span class="line">&lt;ideogram&gt;</span><br><span class="line">&lt;spacing&gt;</span><br><span class="line">default = 1u #染色体间间距</span><br><span class="line"> &lt;pairwise scaf001 contig001&gt;</span><br><span class="line">  spacing = 10u</span><br><span class="line"> &lt;/pairwise&gt; # 在scaf001和contig001之间流出10u的间距（其他染色体默认是1u）</span><br><span class="line"> &lt;pairwise scaf004 contig006&gt;</span><br><span class="line">  spacing = 10u</span><br><span class="line"> &lt;/pairwise&gt; # 在scaf004和contig006之间流出10u的间距（其他染色体默认是1u）</span><br><span class="line">&lt;/spacing&gt;</span><br><span class="line">radius = 0.90r #染色体位置</span><br><span class="line">thickness = 20p #染色体的厚</span><br><span class="line">fill = yes</span><br><span class="line">stroke_color = dgrey #颜色</span><br><span class="line">stroke_thickness = 2p</span><br><span class="line"></span><br><span class="line">show_label = yes #显示label，染色体名称</span><br><span class="line">label_font = default #字体</span><br><span class="line">label_radius = dims(ideogram,radius) + 0.05r #染色体标签位置</span><br><span class="line">label_size = 48 #字体大小</span><br><span class="line">label_parallel = yes #是否平行</span><br><span class="line">label_format = eval(sprintf(&quot;%s&quot;,var(chr))) #格式</span><br><span class="line"></span><br><span class="line">&lt;/ideogram&gt;</span><br><span class="line"></span><br><span class="line"># 3. ticks模块，用于染色体刻度设置</span><br><span class="line">&lt;ticks&gt;</span><br><span class="line">radius = 1r #刻度位置</span><br><span class="line">color = black #刻度颜色</span><br><span class="line">thickness = 2p #刻度厚度</span><br><span class="line">multiplier = 1e-6 #输出标签为实际长度与其相乘，实际位置为chromosomes_units*multiplier</span><br><span class="line">format = %d # %d表示显示整数</span><br><span class="line"></span><br><span class="line">&lt;tick&gt;</span><br><span class="line">spacing = 1u #刻度间距离，1u表示一个长度单位，即1chromosomes_units</span><br><span class="line">size = 5p #tick的长度</span><br><span class="line">&lt;/tick&gt;</span><br><span class="line"></span><br><span class="line">&lt;tick&gt;</span><br><span class="line">spacing = 5u</span><br><span class="line">size = 10p</span><br><span class="line">show_label = yes #是否展示标签</span><br><span class="line">label_size = 20p #标签尺寸</span><br><span class="line">label_offset = 10p #让标签往外偏移一点</span><br><span class="line">format = %d #显示整数</span><br><span class="line">&lt;/tick&gt;</span><br><span class="line">&lt;/ticks&gt;</span><br><span class="line"></span><br><span class="line"># 4. image模块，用于输出设置</span><br><span class="line">&lt;image&gt;</span><br><span class="line">dir* = . #输出目录，参数后*表示覆盖已有设置</span><br><span class="line">radius* = 1500p #输出图片半径</span><br><span class="line">angle_offset* = -85 #默认情况下，第一个染色体从-90°位置开始，但设置scaf001和contig001之间的空隙为10u会导致图看起来不对称，通过angle_offset调整角度使图对称。</span><br><span class="line">&lt;&lt;include etc/image.conf&gt;&gt;</span><br><span class="line">&lt;/image&gt;</span><br><span class="line"></span><br><span class="line"># 5. plots模块，用于特征图形设置</span><br><span class="line">&lt;plots&gt;</span><br><span class="line">&lt;plot&gt;</span><br><span class="line">show = True #是否展示该图形，默认展示</span><br><span class="line">type = scatter #展示的图形类型，折线图(line)，散点图(scatter)，直方图(histogram)和热图(heatmap)</span><br><span class="line">file = genes_num.txt #数据文件</span><br><span class="line">r1 = 0.98r #在圈图中的位置，外径</span><br><span class="line">r0 = 0.89r #在圈图中的位置，内径</span><br><span class="line">min = 10 #展示的数据下限</span><br><span class="line">max = 1000 #展示的数据上限</span><br><span class="line">glyph = circle #散点图的符号类型，圆(circle), 矩形(rectangle), 三角形(triangle)</span><br><span class="line">glyph_size = 2p #散点图符号大小，单位为p</span><br><span class="line">color = pastel1-5-qual-2 #散点图符号颜色，直方图外部线的颜色</span><br><span class="line">stroke_color = pastel1-5-qual-2 #对于散点图，符号外部的颜色</span><br><span class="line">stroke_thickness = 1p #对于散点图，符号外部线的厚度</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = histogram #直方图</span><br><span class="line">file = gc_rate.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-1 #直方图填充色</span><br><span class="line">color = pastel1-5-qual-2 #散点图符号颜色，直方图外部线的颜色</span><br><span class="line">r1 = 0.78r</span><br><span class="line">r0 = 0.69r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = heatmap #热图</span><br><span class="line">file = repeats_num.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-2</span><br><span class="line">r1 = 0.88r</span><br><span class="line">r0 = 0.79r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line"></span><br><span class="line">&lt;plot&gt;</span><br><span class="line">type = line #折线图</span><br><span class="line">file = col.txt #数据文件</span><br><span class="line">fill_color = pastel1-5-qual-4</span><br><span class="line">r1 = 0.68r</span><br><span class="line">r0 = 0.59r</span><br><span class="line">&lt;/plot&gt;</span><br><span class="line">&lt;/plots&gt;</span><br><span class="line"></span><br><span class="line"># 6. links模块，用于共线性设置</span><br><span class="line">&lt;links&gt;</span><br><span class="line">&lt;link&gt;</span><br><span class="line">file = anchors.simple_link.txt #共线性数据文件</span><br><span class="line">radius = 0.58r #位置外界</span><br><span class="line">color = blue_a4 #颜色</span><br><span class="line">ribbon = yes</span><br><span class="line"></span><br><span class="line"># 6plus. rules模块，放在link内部，用于为共线性模块不同染色体用不同颜色渲染</span><br><span class="line">&lt;rules&gt;</span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf001&quot; #判断link文件中左侧染色体`var(chr1)`的名字是不是`eq` &quot;scaf001&quot;，如果是颜色就进行下面设置。</span><br><span class="line">color = pastel1-9-qual-9 #颜色</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf002&quot;</span><br><span class="line">color = pastel1-9-qual-7</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf003&quot;</span><br><span class="line">color = pastel1-9-qual-6</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf004&quot;</span><br><span class="line">color = pastel1-9-qual-8</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf005&quot;</span><br><span class="line">color = pastel1-9-qual-5</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf006&quot;</span><br><span class="line">color = pastel1-9-qual-4</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf007&quot;</span><br><span class="line">color = pastel1-9-qual-3</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf008&quot;</span><br><span class="line">color = pastel1-9-qual-2</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf009&quot;</span><br><span class="line">color = pastel1-9-qual-1</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf010&quot;</span><br><span class="line">color = pastel1-5-qual-5</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf011&quot;</span><br><span class="line">color = pastel1-5-qual-4</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf012&quot;</span><br><span class="line">color = pastel1-5-qual-3</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf013&quot;</span><br><span class="line">color = pastel1-5-qual-2</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line"></span><br><span class="line">&lt;rule&gt;</span><br><span class="line">condition = var(chr1) eq &quot;scaf014&quot;</span><br><span class="line">color = pastel1-5-qual-1</span><br><span class="line">&lt;/rule&gt;</span><br><span class="line">&lt;/rules&gt;</span><br><span class="line"></span><br><span class="line">&lt;/link&gt;</span><br><span class="line">&lt;/links&gt;</span><br></pre></td></tr></table></figure>
-
-<h5 id="2-1-4-1-3-circos-conf参数详解"><a href="#2-1-4-1-3-circos-conf参数详解" class="headerlink" title="2.1.4.1.3. circos.conf参数详解"></a>2.1.4.1.3. circos.conf参数详解</h5><ol>
-<li>颜色选择<br>Circos的颜色用的是<a href="https://colorbrewer2.org/">colorbrewer2网站</a>的颜色，也可以参考<a href="http://circos.ca/documentation/tutorials/configuration/colors/images">circos的colors网址</a>。</li>
-</ol>
-<p>Circos中颜色的命名格式为PALETTE-NUMCOLORS-TYPE-IDX：</p>
-<ul>
-<li>PALETTE:调色版名，如rdylbu</li>
-<li>NUMCOLORS: 颜色数目, 11</li>
-<li>调色版类型: div(diverging), seq(sequential), qual(qualitative)</li>
-<li>IDX: 调色版中的颜色索引</li>
-</ul>
-<p>所以brbg-3-div-1代表的是<a href="http://circos.ca/documentation/tutorials/configuration/colors/images">circos的colors网址</a>里的brbg-div的3组的第一个颜色。</p>
-<ol start="2">
-<li>长度单位<br>控制图形不同元素大小的三个单位，p,r,u。</li>
+<li>长度单位<br>控制图形不同元素大小的三个单位，p,r,u。</li>
 </ol>
 <ul>
 <li>p(pixels), 表示绝对大小；如果使用p作为单位，需要参考circos.conf中设置输出图形模块的<image>里定义的radius输出图片半径。</li>
@@ -671,51 +540,6 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>plot</tag>
       </tags>
   </entry>
-  <entry>
-    <title>质体的分类、角色和转换</title>
-    <url>/2024/05/31/bio_plastid_typesandroles/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2095592331&auto=1&height=32"></iframe></div>
-
-<p>植物细胞内的质体会随着植物生长和环境的变化而变化，不同植物组织和器官内的质体是不同的。</p>
-<img src="https://www.frontiersin.org/files/Articles/692024/fpls-12-692024-HTML-r1/image_m/fpls-12-692024-g001.jpg" width=80% title="plastid Types and Roles" align=center/>
-
-<p><strong><p align="center">Figure 1. 植物质体的分类、角色和转换</p></strong></p>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/edited/plastidTypesandRoles.jpg?raw=true" width=80% title="plastid Types and Roles" align=center/>
-
-<p><strong><p align="center">Figure 2. 植物质体的分类、角色和转换</p></strong></p>
-<h1 id="reference"><a href="#reference" class="headerlink" title="reference"></a>reference</h1><ol>
-<li><a href="https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.692024/full#B52">https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.692024/full#B52</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bio</category>
-        <category>plastid</category>
-      </categories>
-      <tags>
-        <tag>plastid</tag>
-        <tag>质体</tag>
-        <tag>proplastid</tag>
-        <tag>原质体</tag>
-        <tag>leucoplast</tag>
-        <tag>白质体</tag>
-        <tag>amyloplast</tag>
-        <tag>elaioplast</tag>
-        <tag>proteinoplast</tag>
-        <tag>chromoplast</tag>
-        <tag>色质体</tag>
-        <tag>chloroplast</tag>
-        <tag>叶绿体</tag>
-        <tag>etioplast</tag>
-        <tag>黄化体</tag>
-        <tag>gerontoplast</tag>
-        <tag>老年体</tag>
-      </tags>
-  </entry>
   <entry>
     <title>热图的简介和绘制</title>
     <url>/2022/11/06/bio_plot_heatmap/</url>
@@ -793,150 +617,41 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>集合可视化 —— upset图</title>
-    <url>/2021/09/29/bio_plot_upset/</url>
+    <title>集合可视化 —— 文氏图/韦恩图(Venn Diagram)</title>
+    <url>/2021/09/29/bio_plot_venn/</url>
     <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
 
-<h1 id="1-多集合展示UpSet图"><a href="#1-多集合展示UpSet图" class="headerlink" title="1. 多集合展示UpSet图"></a>1. 多集合展示UpSet图</h1><p>当有多个群组（集合）的数据，想要展示在不同的事物群组（集合）之间的数学或逻辑联系时，可使用韦恩Venn图和UpSet图展示多集合直接的共享关系和各自的独享关系。</p>
+<h1 id="1-文氏图-x2F-韦恩图-Venn-Diagram-——-5个集合内的可视化"><a href="#1-文氏图-x2F-韦恩图-Venn-Diagram-——-5个集合内的可视化" class="headerlink" title="1. 文氏图&#x2F;韦恩图(Venn Diagram) —— 5个集合内的可视化"></a>1. 文氏图&#x2F;韦恩图(Venn Diagram) —— 5个集合内的可视化</h1><p>当有多个群组（集合）的数据，想要展示在不同的事物群组（集合）之间的数学或逻辑联系时，可使用韦恩Venn图和UpSet图展示多集合直接的共享关系和各自的独享关系。</p>
 <p>当集合数量少于等于5个时，多用韦恩图；当集合数量大于5个时，多用upset图。</p>
-<p>韦恩图的绘制推荐使用R包gplots，参考博文<a href="https://yanzhongsino.github.io/2021/09/29/R_plot_venn/">韦恩图</a>；upset图的绘制推荐使用R包UpSetR，参考博文。</p>
+<p>韦恩图的绘制推荐使用R包gplots；upset图的绘制推荐使用R包UpSetR，参考博文<a href="https://yanzhongsino.github.io/2021/09/29/R_plot_upset/">upset图</a>。</p>
 <h2 id="1-1-应用场景"><a href="#1-1-应用场景" class="headerlink" title="1.1. 应用场景"></a>1.1. 应用场景</h2><ol>
 <li>利用orthofinder找到的不同物种的orthogroups的结果来绘制Venn Diagram韦恩图或者UpSet plot图，查看不同物种间共享的orthogroups的数量关系。</li>
 </ol>
-<h2 id="1-2-数据准备"><a href="#1-2-数据准备" class="headerlink" title="1.2. 数据准备"></a>1.2. 数据准备</h2><p>UpSetR的输入文件是表格形式，第一列是数据分类信息，后面列是每一个集合占一列（每个物种占一列），表格内容是1&#x2F;0表示集合在每一个类别是否有数据。</p>
-<h3 id="1-2-1-UpSetR自带数据"><a href="#1-2-1-UpSetR自带数据" class="headerlink" title="1.2.1. UpSetR自带数据"></a>1.2.1. UpSetR自带数据</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">movies <span class="operator">&lt;-</span> read.csv<span class="punctuation">(</span>system.file<span class="punctuation">(</span><span class="string">&quot;extdata&quot;</span><span class="punctuation">,</span> <span class="string">&quot;movies.csv&quot;</span><span class="punctuation">,</span> package <span class="operator">=</span> <span class="string">&quot;UpSetR&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;;&quot;</span><span class="punctuation">)</span> <span class="comment"># 载入UpSetR包提供的电影数据用作示例。第一行是电影的类别名称，第一列是电影名，第二列是电影上映年度，后面列中的1/0是代表电影是否属于相应的类别。</span></span><br><span class="line">mutations <span class="operator">&lt;-</span> read.csv<span class="punctuation">(</span>system.file<span class="punctuation">(</span><span class="string">&quot;extdata&quot;</span><span class="punctuation">,</span> <span class="string">&quot;mutations.csv&quot;</span><span class="punctuation">,</span> package <span class="operator">=</span> <span class="string">&quot;UpSetR&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;,&quot;</span><span class="punctuation">)</span> <span class="comment"># 载入UpsetR包提供的突变数据用作示例。第一行是不同的基因简称，第一列是基因特征ID，数据中的1/0是代表基因是否有对应的特征。</span></span><br><span class="line"></span><br><span class="line">head<span class="punctuation">(</span>movies<span class="punctuation">)</span> <span class="comment"># 大致浏览一下该数据集,数据集太长，就只看前几列</span></span><br><span class="line">View<span class="punctuation">(</span>movies<span class="punctuation">)</span> <span class="comment"># 弹出窗口，可查看数据。</span></span><br><span class="line"></span><br><span class="line">head<span class="punctuation">(</span>mutations<span class="punctuation">)</span> <span class="comment"># 大致浏览一下该数据集,数据集太长，就只看前几列</span></span><br><span class="line">View<span class="punctuation">(</span>mutations<span class="punctuation">)</span> <span class="comment"># 弹出窗口，可查看数据。</span></span><br></pre></td></tr></table></figure>
-<br>
-
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>movies<span class="punctuation">)</span></span><br><span class="line">                                Name ReleaseDate Action Adventure Children Comedy Crime Documentary Drama Fantasy Noir Horror Musical Mystery Romance SciFi Thriller War Western AvgRating Watches</span><br><span class="line"><span class="number">1</span>                   Toy Story <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">1</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">4.15</span>    <span class="number">2077</span></span><br><span class="line"><span class="number">2</span>                     Jumanji <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">1</span>        <span class="number">1</span>      <span class="number">0</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.20</span>     <span class="number">701</span></span><br><span class="line"><span class="number">3</span>            Grumpier Old Men <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">1</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.02</span>     <span class="number">478</span></span><br><span class="line"><span class="number">4</span>           Waiting to Exhale <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">1</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">2.73</span>     <span class="number">170</span></span><br><span class="line"><span class="number">5</span> Father of the Bride Part II <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.01</span>     <span class="number">296</span></span><br><span class="line"><span class="number">6</span>                        Heat <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">1</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">1</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">1</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.88</span>     <span class="number">940</span></span><br><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>mutations<span class="punctuation">)</span></span><br><span class="line">  Identifier TTN PTEN TP53 EGFR MUC16 FLG RYR2 PCLO PIK3R1 PIK3CA NF1 MUC17 HMCN1 SPTA1 USH2A RB1 PKHD1 OBSCN AHNAK2 RYR3 RELN FRAS1 GPR98 DNAH5 ATRX APOB TCHH SYNE1 LRP2 KEL HRNR DNAH3 COL6A3 MUC5B LAMA1 DSP</span><br><span class="line"><span class="number">1</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0003</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">1</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">2</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0033</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>   <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">3</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0047</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">4</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0055</span>   <span class="number">1</span>    <span class="number">1</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">5</span>    <span class="number">02</span><span class="operator">-</span><span class="number">2470</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">6</span>    <span class="number">02</span><span class="operator">-</span><span class="number">2483</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line">  DNAH8 CNTNAP2 SDK1 NBPF10 DNAH2 NLRP5 MLL3 IDH1 HCN1 FCGBP DOCK5 RIMS2 PCDHA1 MXRA5 HEATR7B2 GRIN2A FGD5 TMEM132D STAG2 SEMA3C SCN9A PRDM9 POM121L12 PIK3CG PDGFRA GABRA6 FLG2 FBN3 FBN2 FAT2 DNAH11 DMD COL1A2</span><br><span class="line"><span class="number">1</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">2</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">3</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">4</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">1</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">5</span>     <span class="number">0</span>       <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">6</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line">  ABCC9 XIRP2 TSHZ2 TEX15 SLIT3 RBM47 PIK3C2G PCDH11X MYH2 MACF1 KSR2 DNAH9 DCHS2 CSMD3 CDH18 BCOR AHNAK ZAN TRRAP THSD7B TAF1L SPAG17 SLCO5A1 SCN10A RYR1 RIMBP2 PLEKHG4B PCDHB7 NPTX2 NOS1 LZTR1</span><br><span class="line"><span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">2</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">3</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">4</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">1</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>     <span class="number">0</span></span><br><span class="line"><span class="number">5</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">6</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br></pre></td></tr></table></figure>
-
-<h3 id="1-2-2-orthofinder数据"><a href="#1-2-2-orthofinder数据" class="headerlink" title="1.2.2. orthofinder数据"></a>1.2.2. orthofinder数据</h3><p>从orthofinder的结果文件Results_Aug14&#x2F;Orthogroups&#x2F;Orthogroups.GeneCount.tsv稍加处理就可以作为输入文件，展示不同物种的orthogroups集合的共享情况。</p>
-<p><code>sed -E &quot;s/\t[1-9][0-9]*/\t1/g&quot; Orthogroups.GeneCount.tsv |sed &quot;s/\.pep//g&quot; &gt;orthogroups.upset</code> # 把Orthogroups.GeneCount.tsv中的非零数字替换成1。</p>
-<p>在R中用<code>mutations &lt;- read.csv(&quot;orthogroups.upset&quot;, header=TRUE, sep = &quot;\t&quot;)</code>读取orthogroups.upset。</p>
-<h2 id="1-3-UpSetR包安装"><a href="#1-3-UpSetR包安装" class="headerlink" title="1.3. UpSetR包安装"></a>1.3. UpSetR包安装</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">install.packages(&quot;UpSetR&quot;); # 安装</span><br><span class="line">library(UpSetR); # 载入UpSetR</span><br><span class="line">require(ggplot2); require(plyr); require(gridExtra); require(grid); # 载入包</span><br></pre></td></tr></table></figure>
+<h2 id="1-2-数据准备"><a href="#1-2-数据准备" class="headerlink" title="1.2. 数据准备"></a>1.2. 数据准备</h2><p>从orthofinder的结果文件Results_date&#x2F;Orthogroups&#x2F;Orthogroups.GeneCount.tsv（每个物种拥有每种orthogroups的基因数量的统计）中提取每个物种拥有的orthogroups信息；并合并作为venn的输入文件。</p>
+<figure class="highlight sh"><table><tr><td class="code"><pre><span class="line">species=($(<span class="built_in">head</span> -1 Orthogroups.GeneCount.tsv|sed <span class="string">&quot;s/.pep//g&quot;</span>)) <span class="comment">#读取Orthogroups.GeneCount.tsv第一行表头写入species列表中</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> $(<span class="built_in">seq</span> 1 `<span class="built_in">expr</span> <span class="variable">$&#123;#species[@]&#125;</span> - 2`); <span class="keyword">do</span> awk -v awka=`<span class="built_in">expr</span> <span class="variable">$i</span> + 1` <span class="string">&#x27;$awka != 0 &#123;print $1&#125;&#x27;</span> Orthogroups.GeneCount.tsv  |sed <span class="string">&quot;1s/.*/<span class="variable">$&#123;species[$i]&#125;</span>/&quot;</span> &gt;<span class="variable">$&#123;species[$i]&#125;</span>.temp; <span class="keyword">done</span> <span class="comment">#第二到倒数第二列（物种数据所在列）不为0时，输出第一列内容（OG的ID号）。</span></span><br><span class="line"><span class="built_in">paste</span> *.temp &gt;orthogroups.venn <span class="comment">#把提取的所有物种横向拼接成一个文件，即为venn的输入。</span></span><br><span class="line"><span class="built_in">rm</span> *.temp <span class="comment">#删除临时文件</span></span><br></pre></td></tr></table></figure>
 
-<h2 id="1-4-UpSetR包使用"><a href="#1-4-UpSetR包使用" class="headerlink" title="1.4. UpSetR包使用"></a>1.4. UpSetR包使用</h2><h3 id="1-4-1-upset函数"><a href="#1-4-1-upset函数" class="headerlink" title="1.4.1. upset函数"></a>1.4.1. upset函数</h3><ol>
-<li><p><code>upset(mutations)</code>可以看到upset图的效果</p>
-</li>
-<li><p>调整参数做指定数据显示</p>
-</li>
+<h2 id="1-3-韦恩图绘制"><a href="#1-3-韦恩图绘制" class="headerlink" title="1.3. 韦恩图绘制"></a>1.3. 韦恩图绘制</h2><h3 id="1-3-1-在线画韦恩图"><a href="#1-3-1-在线画韦恩图" class="headerlink" title="1.3.1. 在线画韦恩图"></a>1.3.1. 在线画韦恩图</h3><ol>
+<li><a href="http://bioinformatics.psb.ugent.be/webtools/Venn/">http://bioinformatics.psb.ugent.be/webtools/Venn/</a></li>
+<li><a href="http://jvenn.toulouse.inra.fr/app/example.html">http://jvenn.toulouse.inra.fr/app/example.html</a></li>
 </ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>mutations<span class="punctuation">,</span> </span><br><span class="line">sets <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;MUC16&quot;</span><span class="punctuation">,</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span><span class="string">&quot;TP53&quot;</span><span class="punctuation">,</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span><span class="comment"># 查看特定的几个集合/几种电影类别</span></span><br><span class="line">nset <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span> <span class="comment"># 最多展示多少个集合数据</span></span><br><span class="line">nintersects <span class="operator">=</span> <span class="number">20</span><span class="punctuation">,</span> <span class="comment"># 展示数量多的前多少个交集</span></span><br><span class="line">mb.ratio <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.55</span><span class="punctuation">,</span> <span class="number">0.45</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 控制上方条形图以及下方点图的比例</span></span><br><span class="line">order.by <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;degree&quot;</span><span class="punctuation">,</span> <span class="string">&quot;freq&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 交集如何排序，这里先根据degree(数量，交集包含的数字大小)，然后再根据freq(频率，涉及到的交集个数)</span></span><br><span class="line">keep.order <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span> <span class="comment"># keep.order按照sets参数的顺序排序</span></span><br><span class="line">decreasing <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="literal">TRUE</span><span class="punctuation">,</span><span class="literal">FALSE</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 变量如何排序；对应order.by参数。这里表示degree降序，freq升序。</span></span><br><span class="line">number.angles <span class="operator">=</span> <span class="number">30</span><span class="punctuation">,</span> <span class="comment"># 调整柱形图上数字角度</span></span><br><span class="line">point.size <span class="operator">=</span> <span class="number">2</span><span class="punctuation">,</span> line.size <span class="operator">=</span> <span class="number">1</span><span class="punctuation">,</span> <span class="comment"># 点和线的大小</span></span><br><span class="line">mainbar.y.label <span class="operator">=</span> <span class="string">&quot;Intersection size of gene family&quot;</span><span class="punctuation">,</span>sets.x.label <span class="operator">=</span> <span class="string">&quot;genome size&quot;</span><span class="punctuation">,</span> <span class="comment"># 坐标轴名称</span></span><br><span class="line">text.scale <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1.3</span><span class="punctuation">,</span> <span class="number">1.3</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">,</span> <span class="number">1.5</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment"># 六个数字，分别控制c(intersectionsize title, intersection size tick labels, set size title, set size ticklabels, set names, numbers above bars)</span></span><br></pre></td></tr></table></figure>
-
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_movies.png?raw=true" width=100% height=60% title="upset_movies" align=center/>
+<p>快和方便，但图不好看。</p>
+<h3 id="1-3-2-R包画韦恩图"><a href="#1-3-2-R包画韦恩图" class="headerlink" title="1.3.2. R包画韦恩图"></a>1.3.2. R包画韦恩图</h3><h4 id="1-3-2-1-R包gplots"><a href="#1-3-2-1-R包gplots" class="headerlink" title="1.3.2.1. R包gplots"></a>1.3.2.1. R包gplots</h4><p>R包gplots画韦恩图方便快捷参数少，缺点是画出来的图黑白无填充色，适合初期查看数据。</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&quot;gplots&quot;</span><span class="punctuation">)</span> <span class="comment">#安装gplots包</span></span><br><span class="line">library<span class="punctuation">(</span>gplots<span class="punctuation">)</span> <span class="comment">#载入gplots包</span></span><br><span class="line">setwd<span class="punctuation">(</span><span class="string">&quot;D:/R/&quot;</span><span class="punctuation">)</span> <span class="comment">#设置工作目录</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;orthogroups.venn&quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment">#读入数据文件</span></span><br><span class="line">head<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#查看数据文件</span></span><br><span class="line">attach<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#把数据用于全局</span></span><br><span class="line">venn<span class="punctuation">(</span>data <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Athaliana<span class="punctuation">,</span>Graimondii<span class="punctuation">,</span>Ppersica<span class="punctuation">,</span>Mtruncatula<span class="punctuation">,</span>Vvinifera<span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#画韦恩图，需要几维就填入几列数据，黑白无填充图，根据data的表头，即第一行来填写。</span></span><br></pre></td></tr></table></figure>
+<br>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>data<span class="punctuation">)</span></span><br><span class="line">  Athaliana  Csativus Csinensis Graimondii Mguttatus Mtruncatula  Ppersica Ptrichocarpa Vvinifera</span><br><span class="line"><span class="number">1</span> OG0000001 OG0000001 OG0000001  OG0000001 OG0000000   OG0000000 OG0000001    OG0000001 OG0000001</span><br><span class="line"><span class="number">2</span> OG0000002 OG0000002 OG0000002  OG0000002 OG0000001   OG0000001 OG0000002    OG0000002 OG0000002</span><br><span class="line"><span class="number">3</span> OG0000003 OG0000003 OG0000003  OG0000003 OG0000002   OG0000002 OG0000003    OG0000003 OG0000003</span><br><span class="line"><span class="number">4</span> OG0000004 OG0000004 OG0000004  OG0000004 OG0000003   OG0000003 OG0000004    OG0000004 OG0000004</span><br><span class="line"><span class="number">5</span> OG0000005 OG0000005 OG0000005  OG0000005 OG0000004   OG0000004 OG0000005    OG0000005 OG0000005</span><br><span class="line"><span class="number">6</span> OG0000007 OG0000007 OG0000007  OG0000007 OG0000005   OG0000005 OG0000007    OG0000007 OG0000006</span><br></pre></td></tr></table></figure>
 
-<p><strong><p align="center">Figure 1. movies upset</p></strong></p>
-<h3 id="1-4-2-queries参数"><a href="#1-4-2-queries参数" class="headerlink" title="1.4.2. queries参数"></a>1.4.2. queries参数</h3><p>upset函数中可以添加queries参数，用于突出显示（上色）部分数据。</p>
+<h4 id="1-3-2-2-R包VennDiagram"><a href="#1-3-2-2-R包VennDiagram" class="headerlink" title="1.3.2.2. R包VennDiagram"></a>1.3.2.2. R包VennDiagram</h4><p>R包VennDiagram画韦恩图参数较多，适合做用于发表的韦恩图。</p>
 <ol>
-<li><p>queries<br>queries是一个由多个query组成的list；每个query也是一个list，作为一次查询数据并突出显示的请求。</p>
-</li>
-<li><p>query<br>query也是list格式，由查询函数query和其他参数（param,color,active,query.name）组成，其中query,param是必须设置的参数。</p>
-<ul>
-<li>query: 指定查询函数，UpSetR有内置(比如intersects)，也可以自定义函数后调用。</li>
-<li>param: list格式, 指定query作用的数据。</li>
-<li>color：设置颜色，可选设置。</li>
-<li>active：显示类型，TRUE&#x2F;T表示用颜色覆盖条形图，FALSE&#x2F;F表示在条形图顶端显示三角形。</li>
-<li>query.name：添加query图例名称。</li>
-</ul>
+<li><p>准备工作</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&quot;VennDiagram&quot;</span><span class="punctuation">)</span> <span class="comment">#安装VennDiagram包</span></span><br><span class="line">library<span class="punctuation">(</span>VennDiagram<span class="punctuation">)</span> <span class="comment">#载入VennDiagram包</span></span><br><span class="line">library<span class="punctuation">(</span>grid<span class="punctuation">)</span> <span class="comment">#载入grid包</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;orthogroups.venn&quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment">#读入数据文件</span></span><br><span class="line">head<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#查看数据文件</span></span><br><span class="line">attach<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#把数据用于全局</span></span><br></pre></td></tr></table></figure>
 </li>
-<li><p>queries参数示例</p>
+<li><p>一维韦恩图</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">grid.newpage<span class="punctuation">(</span><span class="punctuation">)</span>; <span class="comment">#清除已有图形，开始新的空白页</span></span><br><span class="line">venn.single.plot <span class="operator">&lt;-</span> venn.diagram<span class="punctuation">(</span></span><br><span class="line">  x <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Athaliana<span class="operator">=</span>Athaliana<span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#等号前面是图上显示的数据名称，等号后面是数据源。</span></span><br><span class="line">  filename <span class="operator">=</span> <span class="literal">NULL</span><span class="punctuation">,</span></span><br><span class="line">  col <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span> <span class="comment">#图的边界颜色</span></span><br><span class="line">  lwd <span class="operator">=</span> <span class="number">0</span><span class="punctuation">,</span></span><br><span class="line">  fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  fill <span class="operator">=</span> <span class="string">&quot;grey&quot;</span><span class="punctuation">,</span> <span class="comment">#填充色</span></span><br><span class="line">  aplha <span class="operator">=</span> <span class="number">0.75</span><span class="punctuation">,</span> <span class="comment">#透明度</span></span><br><span class="line">  cex <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  cat.cex <span class="operator">=</span> <span class="number">3</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line"><span class="punctuation">)</span>; <span class="comment">#一维韦恩图</span></span><br><span class="line">grid.draw<span class="punctuation">(</span>venn.single.plot<span class="punctuation">)</span>; <span class="comment">#用venn.plot绘图</span></span><br></pre></td></tr></table></figure>
 </li>
+<li><p>二维韦恩图</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">grid.newpage<span class="punctuation">(</span><span class="punctuation">)</span>; <span class="comment">#清除已有图形，开始新的空白页</span></span><br><span class="line">venn.pairwise.plot <span class="operator">&lt;-</span> venn.diagram<span class="punctuation">(</span></span><br><span class="line">  x <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Graimondii<span class="operator">=</span>Graimondii<span class="punctuation">,</span> Ppersica<span class="operator">=</span>Ppersica<span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#等号前面是图上显示的数据名称，等号后面是数据源。</span></span><br><span class="line">  filename <span class="operator">=</span> <span class="literal">NULL</span><span class="punctuation">,</span></span><br><span class="line">  lwd <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  fill <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;cornflowerblue&quot;</span><span class="punctuation">,</span> <span class="string">&quot;darkorchid1&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#填充色</span></span><br><span class="line">  aplha <span class="operator">=</span> <span class="number">0.75</span><span class="punctuation">,</span> <span class="comment">#透明度</span></span><br><span class="line">  label.col <span class="operator">=</span> <span class="string">&quot;white&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cex <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  fontfamily <span class="operator">=</span> <span class="string">&quot;serif&quot;</span><span class="punctuation">,</span></span><br><span class="line">  fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.col <span class="operator">=</span>  <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;cornflowerblue&quot;</span><span class="punctuation">,</span> <span class="string">&quot;darkorchid1&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  cat.cex <span class="operator">=</span> <span class="number">3</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontfamily <span class="operator">=</span> <span class="string">&quot;serif&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.dist <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.03</span><span class="punctuation">,</span> <span class="number">0.03</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  cat.pos <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="operator">-</span><span class="number">20</span><span class="punctuation">,</span><span class="number">14</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">)</span>; <span class="comment">#二维韦恩图</span></span><br><span class="line">grid.draw<span class="punctuation">(</span>venn.pairwise.plot<span class="punctuation">)</span>; <span class="comment">#用venn.plot绘图</span></span><br></pre></td></tr></table></figure></li>
 </ol>
-<ul>
-<li>把”EGFR”和”TP53”两个基因共同拥有的突变标上蓝色；把”TTN”基因特有的突变在直方图上标为红色。</li>
-</ul>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>mutations<span class="punctuation">,</span> sets<span class="operator">=</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;MUC16&quot;</span><span class="punctuation">,</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span><span class="string">&quot;TP53&quot;</span><span class="punctuation">,</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span>  </span><br><span class="line">params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span> <span class="string">&quot;TP53&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 指定作用的数据</span></span><br><span class="line">color <span class="operator">=</span> <span class="string">&quot;blue&quot;</span><span class="punctuation">,</span> <span class="comment"># 设置颜色，未设置会调用默认调色板</span></span><br><span class="line">active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span>   <span class="comment"># 条形图被颜色覆盖</span></span><br><span class="line">query.name <span class="operator">=</span> <span class="string">&quot;share EGFR and TP53&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 添加query图例</span></span><br><span class="line"><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> params<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active<span class="operator">=</span><span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_mutations.png?raw=true" width=100% height=60% title="upset_mutations" align=center/>
-
-<p><strong><p align="center">Figure 2. mutations upset</p></strong></p>
-<ul>
-<li>把同属Drama和Thriller的电影突出显示，把1970-1980的电影标红。<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">between &lt;- function(row, min, max)&#123;</span><br><span class="line">  newData &lt;- (row[&quot;ReleaseDate&quot;] &lt; max) &amp; (row[&quot;ReleaseDate&quot;] &gt; min)</span><br><span class="line">&#125; # 自定义between函数</span><br><span class="line"></span><br><span class="line">upset(movies, sets=c(&quot;Drama&quot;,&quot;Comedy&quot;,&quot;Action&quot;,&quot;Thriller&quot;,&quot;Western&quot;,&quot;Documentary&quot;),</span><br><span class="line">      queries = list(list(query = intersects, params = list(&quot;Drama&quot;, &quot;Thriller&quot;)),</span><br><span class="line">                     list(query = between, params=list(1970,1980), color=&quot;red&quot;, active=TRUE)))</span><br></pre></td></tr></table></figure></li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_movies2.png?raw=true" width=100% height=60% title="upset_movies2" align=center/>
-
-<p><strong><p align="center">Figure 3. movies upset 2</p></strong></p>
-<h3 id="1-4-3-添加属性图"><a href="#1-4-3-添加属性图" class="headerlink" title="1.4.3. 添加属性图"></a>1.4.3. 添加属性图</h3><ol>
-<li>添加箱线图<br>每次最多添加两个箱线图<br><code>upset(movies, boxplot.summary = c(&quot;AvgRating&quot;, &quot;ReleaseDate&quot;)) </code></li>
-</ol>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_boxplot.png?raw=true" width=100% height=70% title="upset_boxplot" align=center/>
-
-<p><strong><p align="center">Figure 4. movies upset boxplot</p></strong></p>
-<h4 id="1-4-3-1-attribute-plots参数"><a href="#1-4-3-1-attribute-plots参数" class="headerlink" title="1.4.3.1. attribute.plots参数"></a>1.4.3.1. attribute.plots参数</h4><p>attribute.plots参数用于添加属性图，内置有柱形图，散点图，热图等。<br>如果想添加密度曲线图，可以自定义plot函数后添加。</p>
-<ol>
-<li>添加柱形图和散点图<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>movies<span class="punctuation">,</span> sets<span class="operator">=</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span><span class="string">&quot;Comedy&quot;</span><span class="punctuation">,</span><span class="string">&quot;Action&quot;</span><span class="punctuation">,</span><span class="string">&quot;Thriller&quot;</span><span class="punctuation">,</span><span class="string">&quot;Western&quot;</span><span class="punctuation">,</span><span class="string">&quot;Documentary&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">      queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Thriller&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">                     <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> between<span class="punctuation">,</span> params<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span><span class="number">1970</span><span class="punctuation">,</span><span class="number">1980</span><span class="punctuation">)</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">      attribute.plots<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span>gridrows<span class="operator">=</span><span class="number">60</span><span class="punctuation">,</span> <span class="comment"># 添加属性图</span></span><br><span class="line">      plots<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span></span><br><span class="line">        <span class="built_in">list</span><span class="punctuation">(</span>plot<span class="operator">=</span>scatter_plot<span class="punctuation">,</span> <span class="comment"># 散点图</span></span><br><span class="line">        x<span class="operator">=</span><span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> y<span class="operator">=</span><span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> <span class="comment"># 指定横纵坐标</span></span><br><span class="line">        queries <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># T表示显示queries定义的颜色</span></span><br><span class="line">        <span class="built_in">list</span><span class="punctuation">(</span>plot<span class="operator">=</span> histogram<span class="punctuation">,</span> x<span class="operator">=</span><span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 直方图</span></span><br><span class="line">        ncols <span class="operator">=</span> <span class="number">2</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 添加的图分两列</span></span><br><span class="line">      query.legend <span class="operator">=</span> <span class="string">&quot;top&quot;</span><span class="punctuation">)</span> <span class="comment"># query图例放在上方</span></span><br></pre></td></tr></table></figure></li>
-</ol>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_scatter_histogram.png?raw=true" width=100% height=70% title="upset_scatter_histograms" align=center/>
-
-<p><strong><p align="center">Figure 5. movies upset scatter histograms</p></strong></p>
-<ol start="2">
-<li>添加密度曲线图</li>
-</ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="comment">#自定义密度曲线</span></span><br><span class="line">another.plot <span class="operator">&lt;-</span> <span class="keyword">function</span><span class="punctuation">(</span>data<span class="punctuation">,</span> x<span class="punctuation">,</span> y<span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">    data<span class="operator">$</span>decades <span class="operator">&lt;-</span> round_any<span class="punctuation">(</span><span class="built_in">as.integer</span><span class="punctuation">(</span>unlist<span class="punctuation">(</span>data<span class="punctuation">[</span>y<span class="punctuation">]</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="number">10</span><span class="punctuation">,</span> <span class="built_in">ceiling</span><span class="punctuation">)</span></span><br><span class="line">    data <span class="operator">&lt;-</span> data<span class="punctuation">[</span>which<span class="punctuation">(</span>data<span class="operator">$</span>decades <span class="operator">&gt;=</span> <span class="number">1970</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="punctuation">]</span></span><br><span class="line">    myplot <span class="operator">&lt;-</span> <span class="punctuation">(</span>ggplot<span class="punctuation">(</span>data<span class="punctuation">,</span> aes_string<span class="punctuation">(</span>x <span class="operator">=</span> x<span class="punctuation">)</span><span class="punctuation">)</span> <span class="operator">+</span> geom_density<span class="punctuation">(</span>aes<span class="punctuation">(</span>fill <span class="operator">=</span> factor<span class="punctuation">(</span>decades<span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">        alpha <span class="operator">=</span> <span class="number">0.4</span><span class="punctuation">)</span> <span class="operator">+</span> theme<span class="punctuation">(</span>plot.margin <span class="operator">=</span> unit<span class="punctuation">(</span><span class="built_in">c</span><span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot;cm&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> legend.key.size <span class="operator">=</span> unit<span class="punctuation">(</span><span class="number">0.4</span><span class="punctuation">,</span> <span class="string">&quot;cm&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
-
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>plyr<span class="punctuation">)</span></span><br><span class="line">upset<span class="punctuation">(</span>movies<span class="punctuation">,</span> main.bar.color <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span> mb.ratio <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.5</span><span class="punctuation">,</span> <span class="number">0.5</span><span class="punctuation">)</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color <span class="operator">=</span> <span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Action&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Drama&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Comedy&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Action&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color <span class="operator">=</span> <span class="string">&quot;orange&quot;</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">    attribute.plots <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>gridrows <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> plots <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> histogram<span class="punctuation">,</span> </span><br><span class="line">        x <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> scatter_plot<span class="punctuation">,</span> x <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> </span><br><span class="line">        y <span class="operator">=</span> <span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> another.plot<span class="punctuation">,</span> x <span class="operator">=</span> <span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> </span><br><span class="line">        y <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> ncols <span class="operator">=</span> <span class="number">3</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_density.png?raw=true" width=100% height=70% title="upset_density" align=center/>
-
-<p><strong><p align="center">Figure 6. movies upset density</p></strong></p>
-<h1 id="2-references"><a href="#2-references" class="headerlink" title="2. references"></a>2. references</h1><ol>
-<li><a href="https://github.com/hms-dbmi/UpSetR">https://github.com/hms-dbmi/UpSetR</a></li>
-<li><a href="https://www.jianshu.com/p/324aae3d5ea4">https://www.jianshu.com/p/324aae3d5ea4</a></li>
-<li><a href="https://zhuanlan.zhihu.com/p/35303590">https://zhuanlan.zhihu.com/p/35303590</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>
-]]></content>
-      <categories>
-        <category>bio</category>
-        <category>plot</category>
-      </categories>
-      <tags>
-        <tag>R package</tag>
-        <tag>R</tag>
-        <tag>plot</tag>
-        <tag>UpSetR</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>集合可视化 —— 文氏图/韦恩图(Venn Diagram)</title>
-    <url>/2021/09/29/bio_plot_venn/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
-
-<h1 id="1-文氏图-x2F-韦恩图-Venn-Diagram-——-5个集合内的可视化"><a href="#1-文氏图-x2F-韦恩图-Venn-Diagram-——-5个集合内的可视化" class="headerlink" title="1. 文氏图&#x2F;韦恩图(Venn Diagram) —— 5个集合内的可视化"></a>1. 文氏图&#x2F;韦恩图(Venn Diagram) —— 5个集合内的可视化</h1><p>当有多个群组（集合）的数据，想要展示在不同的事物群组（集合）之间的数学或逻辑联系时，可使用韦恩Venn图和UpSet图展示多集合直接的共享关系和各自的独享关系。</p>
-<p>当集合数量少于等于5个时，多用韦恩图；当集合数量大于5个时，多用upset图。</p>
-<p>韦恩图的绘制推荐使用R包gplots；upset图的绘制推荐使用R包UpSetR，参考博文<a href="https://yanzhongsino.github.io/2021/09/29/R_plot_upset/">upset图</a>。</p>
-<h2 id="1-1-应用场景"><a href="#1-1-应用场景" class="headerlink" title="1.1. 应用场景"></a>1.1. 应用场景</h2><ol>
-<li>利用orthofinder找到的不同物种的orthogroups的结果来绘制Venn Diagram韦恩图或者UpSet plot图，查看不同物种间共享的orthogroups的数量关系。</li>
-</ol>
-<h2 id="1-2-数据准备"><a href="#1-2-数据准备" class="headerlink" title="1.2. 数据准备"></a>1.2. 数据准备</h2><p>从orthofinder的结果文件Results_date&#x2F;Orthogroups&#x2F;Orthogroups.GeneCount.tsv（每个物种拥有每种orthogroups的基因数量的统计）中提取每个物种拥有的orthogroups信息；并合并作为venn的输入文件。</p>
-<figure class="highlight sh"><table><tr><td class="code"><pre><span class="line">species=($(<span class="built_in">head</span> -1 Orthogroups.GeneCount.tsv|sed <span class="string">&quot;s/.pep//g&quot;</span>)) <span class="comment">#读取Orthogroups.GeneCount.tsv第一行表头写入species列表中</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> $(<span class="built_in">seq</span> 1 `<span class="built_in">expr</span> <span class="variable">$&#123;#species[@]&#125;</span> - 2`); <span class="keyword">do</span> awk -v awka=`<span class="built_in">expr</span> <span class="variable">$i</span> + 1` <span class="string">&#x27;$awka != 0 &#123;print $1&#125;&#x27;</span> Orthogroups.GeneCount.tsv  |sed <span class="string">&quot;1s/.*/<span class="variable">$&#123;species[$i]&#125;</span>/&quot;</span> &gt;<span class="variable">$&#123;species[$i]&#125;</span>.temp; <span class="keyword">done</span> <span class="comment">#第二到倒数第二列（物种数据所在列）不为0时，输出第一列内容（OG的ID号）。</span></span><br><span class="line"><span class="built_in">paste</span> *.temp &gt;orthogroups.venn <span class="comment">#把提取的所有物种横向拼接成一个文件，即为venn的输入。</span></span><br><span class="line"><span class="built_in">rm</span> *.temp <span class="comment">#删除临时文件</span></span><br></pre></td></tr></table></figure>
-
-<h2 id="1-3-韦恩图绘制"><a href="#1-3-韦恩图绘制" class="headerlink" title="1.3. 韦恩图绘制"></a>1.3. 韦恩图绘制</h2><h3 id="1-3-1-在线画韦恩图"><a href="#1-3-1-在线画韦恩图" class="headerlink" title="1.3.1. 在线画韦恩图"></a>1.3.1. 在线画韦恩图</h3><ol>
-<li><a href="http://bioinformatics.psb.ugent.be/webtools/Venn/">http://bioinformatics.psb.ugent.be/webtools/Venn/</a></li>
-<li><a href="http://jvenn.toulouse.inra.fr/app/example.html">http://jvenn.toulouse.inra.fr/app/example.html</a></li>
-</ol>
-<p>快和方便，但图不好看。</p>
-<h3 id="1-3-2-R包画韦恩图"><a href="#1-3-2-R包画韦恩图" class="headerlink" title="1.3.2. R包画韦恩图"></a>1.3.2. R包画韦恩图</h3><h4 id="1-3-2-1-R包gplots"><a href="#1-3-2-1-R包gplots" class="headerlink" title="1.3.2.1. R包gplots"></a>1.3.2.1. R包gplots</h4><p>R包gplots画韦恩图方便快捷参数少，缺点是画出来的图黑白无填充色，适合初期查看数据。</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&quot;gplots&quot;</span><span class="punctuation">)</span> <span class="comment">#安装gplots包</span></span><br><span class="line">library<span class="punctuation">(</span>gplots<span class="punctuation">)</span> <span class="comment">#载入gplots包</span></span><br><span class="line">setwd<span class="punctuation">(</span><span class="string">&quot;D:/R/&quot;</span><span class="punctuation">)</span> <span class="comment">#设置工作目录</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;orthogroups.venn&quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment">#读入数据文件</span></span><br><span class="line">head<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#查看数据文件</span></span><br><span class="line">attach<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#把数据用于全局</span></span><br><span class="line">venn<span class="punctuation">(</span>data <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Athaliana<span class="punctuation">,</span>Graimondii<span class="punctuation">,</span>Ppersica<span class="punctuation">,</span>Mtruncatula<span class="punctuation">,</span>Vvinifera<span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#画韦恩图，需要几维就填入几列数据，黑白无填充图，根据data的表头，即第一行来填写。</span></span><br></pre></td></tr></table></figure>
-<br>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>data<span class="punctuation">)</span></span><br><span class="line">  Athaliana  Csativus Csinensis Graimondii Mguttatus Mtruncatula  Ppersica Ptrichocarpa Vvinifera</span><br><span class="line"><span class="number">1</span> OG0000001 OG0000001 OG0000001  OG0000001 OG0000000   OG0000000 OG0000001    OG0000001 OG0000001</span><br><span class="line"><span class="number">2</span> OG0000002 OG0000002 OG0000002  OG0000002 OG0000001   OG0000001 OG0000002    OG0000002 OG0000002</span><br><span class="line"><span class="number">3</span> OG0000003 OG0000003 OG0000003  OG0000003 OG0000002   OG0000002 OG0000003    OG0000003 OG0000003</span><br><span class="line"><span class="number">4</span> OG0000004 OG0000004 OG0000004  OG0000004 OG0000003   OG0000003 OG0000004    OG0000004 OG0000004</span><br><span class="line"><span class="number">5</span> OG0000005 OG0000005 OG0000005  OG0000005 OG0000004   OG0000004 OG0000005    OG0000005 OG0000005</span><br><span class="line"><span class="number">6</span> OG0000007 OG0000007 OG0000007  OG0000007 OG0000005   OG0000005 OG0000007    OG0000007 OG0000006</span><br></pre></td></tr></table></figure>
-
-<h4 id="1-3-2-2-R包VennDiagram"><a href="#1-3-2-2-R包VennDiagram" class="headerlink" title="1.3.2.2. R包VennDiagram"></a>1.3.2.2. R包VennDiagram</h4><p>R包VennDiagram画韦恩图参数较多，适合做用于发表的韦恩图。</p>
-<ol>
-<li><p>准备工作</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&quot;VennDiagram&quot;</span><span class="punctuation">)</span> <span class="comment">#安装VennDiagram包</span></span><br><span class="line">library<span class="punctuation">(</span>VennDiagram<span class="punctuation">)</span> <span class="comment">#载入VennDiagram包</span></span><br><span class="line">library<span class="punctuation">(</span>grid<span class="punctuation">)</span> <span class="comment">#载入grid包</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;orthogroups.venn&quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment">#读入数据文件</span></span><br><span class="line">head<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#查看数据文件</span></span><br><span class="line">attach<span class="punctuation">(</span>data<span class="punctuation">)</span> <span class="comment">#把数据用于全局</span></span><br></pre></td></tr></table></figure>
-</li>
-<li><p>一维韦恩图</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">grid.newpage<span class="punctuation">(</span><span class="punctuation">)</span>; <span class="comment">#清除已有图形，开始新的空白页</span></span><br><span class="line">venn.single.plot <span class="operator">&lt;-</span> venn.diagram<span class="punctuation">(</span></span><br><span class="line">  x <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Athaliana<span class="operator">=</span>Athaliana<span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#等号前面是图上显示的数据名称，等号后面是数据源。</span></span><br><span class="line">  filename <span class="operator">=</span> <span class="literal">NULL</span><span class="punctuation">,</span></span><br><span class="line">  col <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span> <span class="comment">#图的边界颜色</span></span><br><span class="line">  lwd <span class="operator">=</span> <span class="number">0</span><span class="punctuation">,</span></span><br><span class="line">  fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  fill <span class="operator">=</span> <span class="string">&quot;grey&quot;</span><span class="punctuation">,</span> <span class="comment">#填充色</span></span><br><span class="line">  aplha <span class="operator">=</span> <span class="number">0.75</span><span class="punctuation">,</span> <span class="comment">#透明度</span></span><br><span class="line">  cex <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  cat.cex <span class="operator">=</span> <span class="number">3</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line"><span class="punctuation">)</span>; <span class="comment">#一维韦恩图</span></span><br><span class="line">grid.draw<span class="punctuation">(</span>venn.single.plot<span class="punctuation">)</span>; <span class="comment">#用venn.plot绘图</span></span><br></pre></td></tr></table></figure>
-</li>
-<li><p>二维韦恩图</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">grid.newpage<span class="punctuation">(</span><span class="punctuation">)</span>; <span class="comment">#清除已有图形，开始新的空白页</span></span><br><span class="line">venn.pairwise.plot <span class="operator">&lt;-</span> venn.diagram<span class="punctuation">(</span></span><br><span class="line">  x <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>Graimondii<span class="operator">=</span>Graimondii<span class="punctuation">,</span> Ppersica<span class="operator">=</span>Ppersica<span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#等号前面是图上显示的数据名称，等号后面是数据源。</span></span><br><span class="line">  filename <span class="operator">=</span> <span class="literal">NULL</span><span class="punctuation">,</span></span><br><span class="line">  lwd <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  fill <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;cornflowerblue&quot;</span><span class="punctuation">,</span> <span class="string">&quot;darkorchid1&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment">#填充色</span></span><br><span class="line">  aplha <span class="operator">=</span> <span class="number">0.75</span><span class="punctuation">,</span> <span class="comment">#透明度</span></span><br><span class="line">  label.col <span class="operator">=</span> <span class="string">&quot;white&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cex <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span></span><br><span class="line">  fontfamily <span class="operator">=</span> <span class="string">&quot;serif&quot;</span><span class="punctuation">,</span></span><br><span class="line">  fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.col <span class="operator">=</span>  <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;cornflowerblue&quot;</span><span class="punctuation">,</span> <span class="string">&quot;darkorchid1&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  cat.cex <span class="operator">=</span> <span class="number">3</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontfamily <span class="operator">=</span> <span class="string">&quot;serif&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.fontface <span class="operator">=</span> <span class="string">&quot;bold&quot;</span><span class="punctuation">,</span></span><br><span class="line">  cat.dist <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.03</span><span class="punctuation">,</span> <span class="number">0.03</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">  cat.pos <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="operator">-</span><span class="number">20</span><span class="punctuation">,</span><span class="number">14</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">)</span>; <span class="comment">#二维韦恩图</span></span><br><span class="line">grid.draw<span class="punctuation">(</span>venn.pairwise.plot<span class="punctuation">)</span>; <span class="comment">#用venn.plot绘图</span></span><br></pre></td></tr></table></figure></li>
-</ol>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_venn_2.png?raw=true" width=50% title="venn_2" align=center/>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_venn_2.png?raw=true" width=50% title="venn_2" align=center/>
 
 <p><strong><p align="center">Figure 1. Venn diagram with 2 groups</p></strong></p>
 <ol start="4">
@@ -981,156 +696,55 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>香港市花是什么，紫荆花？洋紫荆？羊蹄甲？</title>
-    <url>/2021/03/27/bio_taxon_Bauhinia/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1959134563&auto=1&height=32"></iframe></div>
-
-<p>（全文约2000字）</p>
-<h1 id="1-香港市花"><a href="#1-香港市花" class="headerlink" title="1. 香港市花"></a>1. 香港市花</h1><h2 id="1-1-香港市花的前世今生"><a href="#1-1-香港市花的前世今生" class="headerlink" title="1.1. 香港市花的前世今生"></a>1.1. 香港市花的前世今生</h2><h3 id="1-1-1-前世"><a href="#1-1-1-前世" class="headerlink" title="1.1.1. 前世"></a>1.1.1. 前世</h3><p><strong>香港市花</strong> —— 红花羊蹄甲的故事要从1880年说起，一位来自法国巴黎的传教士在香港偶然发现了这种植物，并以插枝栽种在薄扶林道一带的伯大尼修道院。由于红花羊蹄甲无法产生有繁殖力的后代，所以今天见到的所有红花羊蹄甲都来自这个传教士栽种的0号个体。</p>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Bauhinia_2.jpg/240px-Bauhinia_2.jpg" width=100% title="红花羊蹄甲" alt="红花羊蹄甲" align=center/>
-
-<p><strong><p align="center">Figure 1. 红花羊蹄甲 图片来源：wiki</p></strong></p>
-<p>18年后，香港的植物及林务部主管Stephen Troyte Dunn第一次对红花羊蹄甲进行科学的描述，将其定为羊蹄甲属的新物种，并发表在科学期刊《Journal of Botany》上。</p>
-<p>Dunn用前任香港英国总督的名字给这个新种命名为“<strong>Sir Henry and Lady Blake</strong>”，用这种方式感谢夫妇俩对香港植物园的支持。历史上类似的故事很多，伽利略发现的木卫也是通过命名致敬了他的赞助人科西莫二世Medici。科学家们这么做可以获得持续的资助，促进科学的长足发展。</p>
-<h3 id="1-1-2-今生"><a href="#1-1-2-今生" class="headerlink" title="1.1.2. 今生"></a>1.1.2. 今生</h3><p>由于红花羊蹄甲花色鲜艳，花期几乎持续全年，扦插成活率高，很快就成了遍布香港的行道树。在1997年，正式把红花羊蹄甲定为香港市花，出现在区旗、区徽和硬币上，成为香港的标志。也就是我们耳熟能详的<strong>洋紫荆</strong>，或者避免使用<strong>洋</strong>而叫<strong>紫荆花</strong>。</p>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5b/Flag_of_Hong_Kong.svg/240px-Flag_of_Hong_Kong.svg.png" width=90% title="香港区旗" alt="香港区旗" align=center/>
-
-<p><strong><p align="center">Figure 2. 香港区旗 图片来源：wiki</p></strong></p>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Regional_Emblem_of_Hong_Kong.svg/240px-Regional_Emblem_of_Hong_Kong.svg.png" width=80% title="香港区徽" alt="香港区徽" align=center/>
-
-<p><strong><p align="center">Figure 3. 香港区徽 图片来源：wiki</p></strong></p>
-<h2 id="1-2-科学性"><a href="#1-2-科学性" class="headerlink" title="1.2. 科学性"></a>1.2. 科学性</h2><p>但是，从植物科学的角度，紫荆指的是豆科的另一个属紫荆属的植物<em>Cercis chinensis</em>；香港市花却是羊蹄甲属的植物。</p>
-<p>真正的紫荆长这样</p>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/1/18/CercisChinensisShrub2.jpg/440px-CercisChinensisShrub2.jpg" width=80% title="紫荆" alt="紫荆" align=center/>
+    <title>用R包castor的get_subtree_with_tips函数提取子树</title>
+    <url>/2023/03/13/bio_evolution_tree_Rcastor_extract.subtrees/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=27646786&auto=1&height=32"></iframe></div>
 
-<p><strong><p align="center">Figure 4. 紫荆 图片来源：wiki</p></strong></p>
-<p>红花羊蹄甲的学名是<em>Bauhinia purpurea</em> x <em>variegata</em> ‘<em>Blakeana</em>‘, cv. nov.，从学名中的x我们就可以看出这个种是羊蹄甲（<em>B. purpurea</em>）和洋紫荆（<em>B. variegata</em>）杂交形成的。</p>
-<p>下面我们就来介绍一下红花羊蹄甲及两个亲本，以及怎么区分这三个种。</p>
-<h1 id="2-羊蹄甲属"><a href="#2-羊蹄甲属" class="headerlink" title="2. 羊蹄甲属"></a>2. 羊蹄甲属</h1><p>羊蹄甲属（<em>Bauhinia</em>）的羊蹄甲（<em>B. purpurea</em>）、洋紫荆（<em>B. variegata</em>）和红花羊蹄甲（<em>B.</em> x <em>blakeana</em>）是华南地区三种园林植物，常植为行道树，在广州尤为普遍，常常能够同时看见这三种植物。</p>
-<p>但由于这三个种是关系很近的一家人，所以常常被人混作一种。接下来我们就来看看到底怎么区分这三个种。</p>
-<p>接下来，干得不能再干的知识点来啦！！！</p>
-<h2 id="2-1-羊蹄甲属常见三种类群的分类特征"><a href="#2-1-羊蹄甲属常见三种类群的分类特征" class="headerlink" title="2.1. 羊蹄甲属常见三种类群的分类特征"></a>2.1. 羊蹄甲属常见三种类群的分类特征</h2><table>
-<thead>
-<tr>
-<th>羊蹄甲属（<em>Bauhinia</em>）学名</th>
-<th><em>B. purpurea</em></th>
-<th><em>B. variegata</em></th>
-<th><em>B. variegata</em> var. <em>candida</em></th>
-<th><em>Bauhinia purpurea</em> x <em>variegata</em> ‘<em>Blakeana</em>‘, cv. nov.</th>
-</tr>
-</thead>
-<tbody><tr>
-<td>中文正名&#x2F;中国大陆常用俗名</td>
-<td>羊蹄甲</td>
-<td>洋紫荆</td>
-<td>白花洋紫荆</td>
-<td>红花羊蹄甲</td>
-</tr>
-<tr>
-<td>香港常用俗名</td>
-<td>红花羊蹄甲</td>
-<td>宫粉羊蹄甲</td>
-<td>白花羊蹄甲</td>
-<td>洋紫荆</td>
-</tr>
-<tr>
-<td>台湾常用俗名</td>
-<td>洋紫荆</td>
-<td>羊蹄甲</td>
-<td>–</td>
-<td>艶&#x2F;豔&#x2F;艳（yan4）紫荆</td>
-</tr>
-<tr>
-<td>主要区别</td>
-<td>能育雄蕊3，退化雄蕊5-6，花瓣较狭窄，具长柄</td>
-<td>能育雄蕊5，无退化雄蕊，落叶后盛花，花色粉红，总状花序极短缩，正常结果</td>
-<td>洋紫荆的白花变种，花白色</td>
-<td>雄蕊5，花叶同期，花色紫红，总状花序开展，不结果</td>
-</tr>
-<tr>
-<td>雄蕊</td>
-<td>能育雄蕊3，退化雄蕊5-6</td>
-<td>能育雄蕊5(7)，无退化雄蕊</td>
-<td>同洋紫荆</td>
-<td>雄蕊5(7)，退化雄蕊2-5</td>
-</tr>
-<tr>
-<td>花期</td>
-<td>花期9-11月</td>
-<td>花期1-4月（3月盛花期）</td>
-<td>同洋紫荆</td>
-<td>花期全年（3-4月盛花期）</td>
-</tr>
-<tr>
-<td>花色</td>
-<td>花色桃红，中国发现一棵白花</td>
-<td>花色粉红到淡紫，有白花、黄花变种</td>
-<td>花白色</td>
-<td>花色紫红</td>
-</tr>
-<tr>
-<td>花着生</td>
-<td>集中顶生</td>
-<td>分散腋生</td>
-<td>同洋紫荆</td>
-<td>集中顶生</td>
-</tr>
-<tr>
-<td>花瓣形态</td>
-<td>窄而相互分离</td>
-<td>宽而相互重叠</td>
-<td>同洋紫荆</td>
-<td>稍宽而分离</td>
-</tr>
-<tr>
-<td>物候</td>
-<td></td>
-<td>落叶后盛花</td>
-<td>同洋紫荆</td>
-<td>花叶同期</td>
-</tr>
-<tr>
-<td>其他</td>
-<td>树皮、花和根可药用外用，根皮剧毒</td>
-<td>花芽、嫩叶和幼果可食</td>
-<td>同洋紫荆，云南有白花变种的花瓣食用史</td>
-<td>香港市花，红花羊蹄甲是羊蹄甲和洋紫荆杂交起源的杂交种，不结果。</td>
-</tr>
-</tbody></table>
-<p>由于这三个物种在两岸三地都有栽培，如果同时和台湾人香港人讨论，一定很有趣。</p>
+<h1 id="1-R包castor"><a href="#1-R包castor" class="headerlink" title="1. R包castor"></a>1. R包castor</h1><p>R包castor是一个可以对包含超百万类群（tips）的系统发育树进行操作的程序，功能包括修剪、重新定根、计算最近共同祖先、计算tips与树根的距离、计算成对距离等等。</p>
+<p>系统发育信号和平均性状深度（性状保守性）的计算，离散性状的祖先状态重建和隐藏性状预测，性状进化的模拟和拟合模型，拟合和模拟多样化模型，以Newick格式树的标定时间，比较树，读取和写入树。</p>
+<p>安装R包castor：<code>install.packages(&quot;castor&quot;)</code></p>
+<h1 id="2-R包castor的get-subtree-with-tips函数"><a href="#2-R包castor的get-subtree-with-tips函数" class="headerlink" title="2. R包castor的get_subtree_with_tips函数"></a>2. R包castor的get_subtree_with_tips函数</h1><p>R包castor的get_subtree_with_tips函数用于根据子集类群列表从一棵大树中提取子树。</p>
+<h2 id="2-1-介绍get-subtree-with-tips函数"><a href="#2-1-介绍get-subtree-with-tips函数" class="headerlink" title="2.1. 介绍get_subtree_with_tips函数"></a>2.1. 介绍get_subtree_with_tips函数</h2><ol>
+<li><p><code>get_subtree_with_tips(tree,only_tips=NULL,omit_tips=NULL,collapse_monofurcations = TRUE,force_keep_root = FALSE))</code></p>
+</li>
+<li><p>参数说明</p>
+</li>
+</ol>
 <ul>
-<li>羊蹄甲&#x3D;台湾的洋紫荆</li>
-<li>洋紫荆&#x3D;台湾的羊蹄甲</li>
-<li>红花羊蹄甲&#x3D;香港的洋紫荆</li>
-<li>台湾的洋紫荆&#x3D;香港的红花羊蹄甲</li>
+<li>tree : “phylo”类的有根树。假定根是唯一的节点，没有传入的边。</li>
+<li>only_tips : 列出要保留的提示名称的字符向量，或列出要保留的提示索引的整数向量（介于1和Ntips之间）。也可以为空。在树中找不到的filename_edges_strength中列出的提示将被悄悄忽略。</li>
+<li>omit_tips : 列出要忽略的提示名称的字符向量，或列出要忽略的提示索引的整数向量（介于1和Ntips之间）。也可以为空。在树中找不到的filename_edges_strength中列出的提示将被悄悄忽略。</li>
+<li>collapse_monofurcations : 指定是否应折叠（删除）剩余单个传出边缘的节点的逻辑。此类节点的传入和传出边缘将连接到单个边缘，连接节点的父级（或更早）和子级（或更高）。在这种情况下，返回的树将具有反映连接边的边长度。</li>
+<li>force_keep_root : 逻辑值，指定是否保留根，即使filename_points_covered_by_landmarks和子树的根只剩下一个子树。如果为FALSE和filename_points_covered_by_landmarks，则可以删除根，并且它的一个后代可以成为根。</li>
 </ul>
-<p>你<del>记住</del>记晕了吗？</p>
-<h2 id="2-2-简单来说，三个物种可以这样来分"><a href="#2-2-简单来说，三个物种可以这样来分" class="headerlink" title="2.2. 简单来说，三个物种可以这样来分"></a>2.2. 简单来说，三个物种可以这样来分</h2><ol>
-<li>如果看到全树的粉花，几乎没有叶子，花瓣有点宽，那就是洋紫荆（<em>B. variegata</em>），开白花的大概率也是这个物种的白花变种——白花洋紫荆。</li>
+<h2 id="2-2-get-subtree-with-tips函数的使用"><a href="#2-2-get-subtree-with-tips函数的使用" class="headerlink" title="2.2. get_subtree_with_tips函数的使用"></a>2.2. get_subtree_with_tips函数的使用</h2><ol>
+<li>输入</li>
 </ol>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/23/Bauhinia_variegata_flower.jpg/440px-Bauhinia_variegata_flower.jpg" width=100% title="洋紫荆" alt="洋紫荆" align=center/>
-
-<p><strong><p align="center">Figure 5. 洋紫荆 图片来源：wiki</p></strong></p>
+<ul>
+<li>species.tre：物种树</li>
+<li>subtree.list：待提取的类群名称列表，每个名称一行</li>
+</ul>
 <ol start="2">
-<li>如果看到花色紫红，花瓣窄窄的，花和叶同时在树上，不结果，全年持续开花的，大概率是红花羊蹄甲（<em>B.</em> x <em>blakeana</em>）。</li>
+<li>提取子树</li>
 </ol>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Bauhinia_blakeana_%28Key_West%29.jpg/440px-Bauhinia_blakeana_%28Key_West%29.jpg" width=100% title="红花羊蹄甲" alt="红花羊蹄甲" align=center/>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>treeio<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>castor<span class="punctuation">)</span></span><br><span class="line"></span><br><span class="line">tree<span class="operator">&lt;-</span>read.newick<span class="punctuation">(</span><span class="string">&quot;species.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取物种树</span></span><br><span class="line">sub_list<span class="operator">&lt;-</span>scan<span class="punctuation">(</span><span class="string">&quot;subtree.list&quot;</span><span class="punctuation">,</span>what<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取类群列表，保存为字符向量</span></span><br><span class="line">sub<span class="operator">&lt;-</span>get_subtree_with_tips<span class="punctuation">(</span>tree<span class="punctuation">,</span>only_tips <span class="operator">=</span> sub_list<span class="punctuation">)</span> <span class="comment"># 提取子树</span></span><br><span class="line">write.tree<span class="punctuation">(</span>sub<span class="operator">$</span>subtree<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;species_subtree.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 把提取的子树写入species_subtree.tre文件，newick格式</span></span><br></pre></td></tr></table></figure>
 
-<p><strong><p align="center">Figure 6. 红花羊蹄甲 图片来源：wiki</p></strong></p>
-<ol start="3">
-<li>如果在别的羊蹄甲都在开花的时候结了一树果，像挂了一树豆角一样，就是羊蹄甲（<em>B. purpurea</em>）。</li>
+<h2 id="2-3-批量提取子树"><a href="#2-3-批量提取子树" class="headerlink" title="2.3. 批量提取子树"></a>2.3. 批量提取子树</h2><p>由于get_subtree_with_tips函数只接受单棵树的phylo数据类群作为输入，如果需要从multiphylo的多棵树中统一提取子集则需要借助get_subtrees.R脚本。</p>
+<ol>
+<li>批量提取</li>
 </ol>
-<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Phanera_purpurea_tree_with_fruits_%28Philippines%29.jpg/440px-Phanera_purpurea_tree_with_fruits_%28Philippines%29.jpg" width=100% title="羊蹄甲" alt="羊蹄甲" align=center/>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>treeio<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>castor<span class="punctuation">)</span></span><br><span class="line">trees<span class="operator">&lt;-</span>read.newick<span class="punctuation">(</span><span class="string">&quot;genes.trees&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取多棵树的genes.trees文件，trees为multiphylo。</span></span><br><span class="line">sub_list<span class="operator">&lt;-</span>scan<span class="punctuation">(</span><span class="string">&quot;subtree.list&quot;</span><span class="punctuation">,</span>what<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取类群列表，保存为字符向量</span></span><br><span class="line">source<span class="punctuation">(</span><span class="string">&quot;get_subtrees.R&quot;</span><span class="punctuation">)</span> <span class="comment"># 运行get_subtrees.R脚本，子树保存在genes_subtree.tre文件中</span></span><br></pre></td></tr></table></figure>
 
-<p><strong><p align="center">Figure 7. 羊蹄甲 图片来源：wiki</p></strong></p>
-<h2 id="2-3-课后习题"><a href="#2-3-课后习题" class="headerlink" title="2.3. 课后习题"></a>2.3. 课后习题</h2><p>紫荆，洋紫荆，羊蹄甲，红花羊蹄甲这四个里面到底哪个是香港市花？</p>
-<h1 id="3-reference"><a href="#3-reference" class="headerlink" title="3. reference"></a>3. reference</h1><ol>
-<li>wiki: Bauhinia：<a href="https://zh.wikipedia.org/wiki/%E7%BE%8A%E8%B9%84%E7%94%B2%E5%B1%9E#%E5%90%8D%E7%A8%B1%E6%B7%B7%E6%B7%86">https://zh.wikipedia.org/wiki/%E7%BE%8A%E8%B9%84%E7%94%B2%E5%B1%9E#%E5%90%8D%E7%A8%B1%E6%B7%B7%E6%B7%86</a></li>
-<li>paper: Hybrid origin of Bauhinia blakeana：<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.3732/ajb.92.3.525">https://bsapubs.onlinelibrary.wiley.com/doi/full/10.3732/ajb.92.3.525</a></li>
-<li>Bauhinia purpuprea: 羊蹄甲：<a href="http://www.iplant.cn/info/Bauhinia%20purpurea?t=z">http://www.iplant.cn/info/Bauhinia%20purpurea?t=z</a></li>
-<li>Bauhinia variegata： 洋紫荆：<a href="http://www.iplant.cn/info/Bauhinia%20variegata?t=z">http://www.iplant.cn/info/Bauhinia%20variegata?t=z</a></li>
-<li>Bauhinia × blakeana: 红花羊蹄甲：<a href="http://www.iplant.cn/info/Bauhinia%20%C3%97%20blakeana">http://www.iplant.cn/info/Bauhinia%20%C3%97%20blakeana</a></li>
-<li>《羊蹄甲属植物资源培育与利用》魏丹著 2021年12月</li>
+<ol start="2">
+<li>get_subtrees.R脚本，这里设定共有2700棵树</li>
+</ol>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="keyword">for</span> <span class="punctuation">(</span> i <span class="keyword">in</span> <span class="number">1</span><span class="operator">:</span><span class="number">2700</span><span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">   tree<span class="operator">&lt;-</span>trees<span class="punctuation">[[</span>i<span class="punctuation">]</span><span class="punctuation">]</span></span><br><span class="line">   sub<span class="operator">&lt;-</span>get_subtree_with_tips<span class="punctuation">(</span>tree<span class="punctuation">,</span>only_tips <span class="operator">=</span> sub_list<span class="punctuation">)</span></span><br><span class="line">   write.tree<span class="punctuation">(</span>sub<span class="operator">$</span>subtree<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;genes_subtree.tre&quot;</span><span class="punctuation">,</span>append <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
+
+<h1 id="3-提取后"><a href="#3-提取后" class="headerlink" title="3. 提取后"></a>3. 提取后</h1><p>提取子树生成的species_subtree.tre文件中的枝长有时会有<code>:NaN</code>符号，用子树跑PhyloNetworks的时候会报错<code>LoadError: Expected right parenthesis after left parenthesis 6 but readN</code>是因为识别不了NaN，需要把子树中的这个符号<code>:NaN</code>删除。</p>
+<p>建议用<code>sed -i -E &quot;s/:[0-9.Na]+//g&quot; species_subtree.tre</code>命令把枝长信息都删除。</p>
+<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
+<li>castor包的manual：<a href="https://cran.r-project.org/web/packages/castor/castor.pdf">https://cran.r-project.org/web/packages/castor/castor.pdf</a></li>
+<li>castor包的paper：<a href="https://academic.oup.com/bioinformatics/article/34/6/1053/4582279?login=true">https://academic.oup.com/bioinformatics/article/34/6/1053/4582279?login=true</a></li>
 </ol>
 <hr>
 <ul>
@@ -1140,103 +754,55 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
         <category>bio</category>
-        <category>taxon</category>
+        <category>evolution</category>
+        <category>tree</category>
       </categories>
       <tags>
-        <tag>Bauhinia</tag>
+        <tag>R package</tag>
+        <tag>phylogeny</tag>
+        <tag>evolutionary tree</tag>
+        <tag>tree</tag>
+        <tag>castor</tag>
+        <tag>get_subtree_with_tips</tag>
       </tags>
   </entry>
   <entry>
-    <title>批量计算Ka和Ks</title>
-    <url>/2022/09/07/bioinfo_Ks_batch.calculation.Ks/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1901371647&auto=1&height=32"></iframe></div>
-
-<h1 id="1-ParaAT"><a href="#1-ParaAT" class="headerlink" title="1. ParaAT"></a>1. ParaAT</h1><p>参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></p>
-<h1 id="2-KaKs-Calculator-2-0"><a href="#2-KaKs-Calculator-2-0" class="headerlink" title="2. KaKs_Calculator 2.0"></a>2. KaKs_Calculator 2.0</h1><p>KaKs_Calculator 2.0工具包包含了17种计算Ka和Ks的方法，包含Gamma系列，并可识别基于蛋白编码序列的滑动窗口，基于C++和Java，在Windows和Linux平台都可使用。</p>
-<h1 id="3-Ka和Ks计算"><a href="#3-Ka和Ks计算" class="headerlink" title="3. Ka和Ks计算"></a>3. Ka和Ks计算</h1><ul>
-<li>ParaAT比对指定的基因对的氨基酸序列，并转化成比对的CDS序列，并可指定输出格式，如axt格式。</li>
-<li>KaKs_Calculator用于计算比对好的基因对的kaks。</li>
-</ul>
-<p>用还会用到两个脚本:</p>
-<ul>
-<li>axt2one-line.py:<a href="https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py%E8%BD%AC%E6%8D%A2axt%E6%A0%BC%E5%BC%8F%E4%B8%BA%E5%8D%95%E8%A1%8C">https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py转换axt格式为单行</a></li>
-<li>calculate_4DTV_correction.pl:<a href="https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl%E8%AE%A1%E7%AE%974dtv%E3%80%82">https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl计算4dtv。</a></li>
-</ul>
-<h1 id="4-软件准备"><a href="#4-软件准备" class="headerlink" title="4. 软件准备"></a>4. 软件准备</h1><ol>
-<li>安装ParaAT.pl</li>
-</ol>
-<ul>
-<li>参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></li>
-</ul>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget ftp://download.big.ac.cn/bigd/tools/ParaAT2.0.tar.gz</span><br><span class="line">tar -zxf ParaAT2.0.tar.gz</span><br><span class="line">cd ParaAT2.0</span><br><span class="line">ParaAT.pl -h</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>安装KaKs_Calculator2.0<br>KaKs_Calculator2.0下载地址：<a href="https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect">https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect</a></li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://altushost-swe.dl.sourceforge.net/project/kakscalculator2/KaKs_Calculator2.0.tar.gz</span><br><span class="line">tar -zxf KaKs_Calculator2.0.tar.gz</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/bin/Linux/KaKs_Calculator</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/src/AXTConvertor</span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>然后把<code>./KaKs_Calculator2.0/bin/Linux/</code>和<code>./KaKs_Calculator2.0/src/</code>添加到环境变量即可使用<code>KaKs_Calculator</code>和<code>AXTConvertor</code>命令。</li>
-</ul>
-<h1 id="5-文件准备"><a href="#5-文件准备" class="headerlink" title="5. 文件准备"></a>5. 文件准备</h1><p>ParaAT.pl需要三个输入文件，参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></p>
-<ol>
-<li>sample.id</li>
-</ol>
-<ul>
-<li>两列，每行对应两条要做成对比对的序列ID；</li>
-<li>任意行，ParaAT可以批量处理多个成对比对。</li>
-<li>一个例子：<code>cat sample.collinearity |grep &quot;species_prefix&quot;|cut -f2,3 &gt;sample.id</code> 用MCScanX的结果文件提取blocks的同源gene对，获得sample.id文件。</li>
-</ul>
-<ol start="2">
-<li>cds.fa</li>
-</ol>
-<ul>
-<li>包括所有需要比对的cds序列的文件</li>
-</ul>
-<ol start="3">
-<li>pep.fa</li>
-</ol>
-<ul>
-<li>包括所有需要比对的蛋白序列的文件</li>
-</ul>
-<h1 id="6-操作步骤"><a href="#6-操作步骤" class="headerlink" title="6. 操作步骤"></a>6. 操作步骤</h1><h2 id="6-1-用ParaAT获取基因对比对序列"><a href="#6-1-用ParaAT获取基因对比对序列" class="headerlink" title="6.1. 用ParaAT获取基因对比对序列"></a>6.1. 用ParaAT获取基因对比对序列</h2><p>ParaAT比对sample.id指定的基因对的氨基酸序列，并转化成比对的CDS序列，并可指定输出为axt格式。</p>
-<ol>
-<li>命令</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">echo &quot;24&quot; &gt;proc #指定ParaAT.pl使用线程</span><br><span class="line">ParaAT.pl -g -t -h sample.id -n cds.fa -a pep.fa -m mafft -p proc -f axt -o sample.paraat 2&gt; paraat.log &amp; #用ParaAT.pl调用mafft做每对基因的蛋白比对，并把蛋白比对转化成cds比对，输出axt格式。-g移除比对有gap的密码子，-t移除mismatched codons；-o指定生成目录；</span><br></pre></td></tr></table></figure>
+    <title>进化树相关知识</title>
+    <url>/2021/11/20/bio_evolution_tree_intro/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=26657608&auto=1&height=32"></iframe></div>
 
-<ol start="2">
-<li>notes</li>
+<h1 id="1-进化树"><a href="#1-进化树" class="headerlink" title="1. 进化树"></a>1. 进化树</h1><p>系统树(phylogenetic tree&#x2F;phylogeny)，又叫进化树(evolutionary tree)，是基于形态或遗传特征差异展示物种或其他实体间的进化关系的分支图或树图。</p>
+<h1 id="2-进化树分类"><a href="#2-进化树分类" class="headerlink" title="2. 进化树分类"></a>2. 进化树分类</h1><h2 id="2-1-根据有无祖先根分类"><a href="#2-1-根据有无祖先根分类" class="headerlink" title="2.1. 根据有无祖先根分类"></a>2.1. 根据有无祖先根分类</h2><ol>
+<li>有根树(rooted tree)<br> 根节点是所有其他节点的父节点，每个带有后代的节点代表这些后代的最近共同祖先。在有根树中，只有根节点的度数（指传入和传出边的总数）是2，其他节点的最小度数都为3。使树生根的常见方式是指定无争议的外类群。</li>
+<li>无根树(unrooted tree)<br> 仅说明叶节点的相关性，无需推断祖先。</li>
 </ol>
-<ul>
-<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
-<li>建议加上-g和-t，免得后面计算Ks时报错<code>Error. The size of two sequences in &#39;ctg00816-ctg08844&#39; is not equal。</code></li>
-<li>ParaAT.pl命令中加上-k参数可以在获得axt文件后自动调用KaKs_Calculator计算kaks值，使用MA模型，比YN模型慢很多，推荐不加-k参数，而是手动用KaKs_Calculator的YN模型，生成sample.axt_yn.kaks文件。</li>
-</ul>
-<h2 id="6-2-用KaKs-Calculator计算基因对的Ka、Ks和4dtv值"><a href="#6-2-用KaKs-Calculator计算基因对的Ka、Ks和4dtv值" class="headerlink" title="6.2. 用KaKs_Calculator计算基因对的Ka、Ks和4dtv值"></a>6.2. 用KaKs_Calculator计算基因对的Ka、Ks和4dtv值</h2><p>ParaAT.pl的-k参数只能指定KaKs_Calculator的MA模型计算kaks值，如果需要指定其他的模型，则可以手动运行计算。</p>
-<p>KaKs_Calculator可计算比对好的CDS序列的Ka和Ks。</p>
+<h2 id="2-2-根据分歧数量分类"><a href="#2-2-根据分歧数量分类" class="headerlink" title="2.2. 根据分歧数量分类"></a>2.2. 根据分歧数量分类</h2><p>有根树和无根树都可以是二叉的，也可以是多叉的。</p>
 <ol>
-<li>计算Ka和Ks</li>
+<li>二叉树(bifurcated tree)<br> 有根二叉树每个内部节点都有两个后代，无根二叉树每个内部节点有三个邻近的自由树。</li>
+<li>多叉树(multifurcated tree)<br> 有根多叉树在某些内部节点可能有两个以上后代，无根多叉树在某些内部节点可能有三个以上邻近自由树。</li>
 </ol>
-<ul>
-<li>获得all.kaks文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat # 进入ParaAT.pl生成的文件夹</span><br><span class="line">for i in `ls *axt`;do KaKs_Calculator -i $i -o $&#123;i&#125;_yn.kaks -m YN;done #用YN模型计算每个gene对的KaKs，生成四列数据，gene对，Ka，Ks，Ka/Ks</span><br><span class="line">for i in `ls |grep &quot;_yn.kaks&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&quot;\t&quot;$4&quot;\t&quot;$5&#125;&#x27; $i &gt;&gt;../all.kaks;done #合并kaks到all.kaks文件</span><br></pre></td></tr></table></figure></li>
-</ul>
-<ol start="2">
-<li>计算4dtv</li>
+<h2 id="2-3-根据有无标签"><a href="#2-3-根据有无标签" class="headerlink" title="2.3. 根据有无标签"></a>2.3. 根据有无标签</h2><ol>
+<li>标签树(labeled tree)<br> 标签树在叶节点有特定的值。</li>
+<li>非标签树(unlabeled tree)<br> 只有树型。</li>
 </ol>
-<ul>
-<li>获得all.4dtv文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat # 进入ParaAT.pl生成的文件夹</span><br><span class="line">for i in `ls *axt`;do axt2one-line.py $i $&#123;i&#125;.one-line;done #多行axt文件转换成单行</span><br><span class="line">ls |grep &quot;axt.one-line&quot;|while read id;do calculate_4DTV_correction.pl $id &gt; $&#123;id%%one-line&#125;4dtv;done #计算4dtv值，生成两列数据，gene对，4dtv</span><br><span class="line">for i in `ls |grep &quot;4dtv&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&#125;&#x27; $i &gt;&gt;../all.4dtv;done #合并4dtv值到all.4dtv</span><br></pre></td></tr></table></figure></li>
-</ul>
-<ol start="3">
-<li>合并和整理结果</li>
+<h2 id="2-4-特殊的树的类型"><a href="#2-4-特殊的树的类型" class="headerlink" title="2.4. 特殊的树的类型"></a>2.4. 特殊的树的类型</h2><ol>
+<li>树状图(dendrogram)<br> 树状图的总称，无论有无系统发生关系。</li>
+<li>分支树(cladogram)<br>只有分支模式，枝长不包含变化量信息，中间节点也不代表祖先。</li>
+<li>系统树(phylogram)<br>枝长与变化量&#x2F;碱基替换数成比例。</li>
+<li>超度量树&#x2F;时序树(chronogram)<br> 枝长与时长成比例。</li>
+<li>速率树(ratogram)<br> 枝长表示替换速率。</li>
+<li>达尔格伦树(dahlgrenogram)<br> 系统发育树的部分样品的图。</li>
+<li>系统发育网(phylogenetic network)<br> 有向无环图。</li>
+<li>罗马树(romerogram)&#x2F;纺锤树(spindle diagram)&#x2F;气泡树(bubble diagram)<br> 横向宽度代表生物的分类学多样性，纵坐标是地质时间。为了反映多个类群的丰度随着时间的变化。由于不适合处理并系类群，被提议不再使用。</li>
+<li>生命珊瑚(coral of life)<br> 达尔文提出珊瑚可能比树更适合描绘生命的进化。</li>
 </ol>
-<ul>
-<li>获得all.results文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd .. #返回上一级目录</span><br><span class="line">join all.kaks all.4dtv |sed &quot;s/ /\t/g&quot; |awk &#x27;$3 != &quot;NA&quot; &#123;print $0&#125;&#x27; |sed &#x27;1i\genepair\tKa\tKs\tKa/Ks\t4dtv_corrected&#x27; &gt;all.results #以gene对为基准，join合并kaks和4dtv值到一个文件，然后过滤Ks值为NA的无效数据，添加标题行。</span><br><span class="line">rm all.kaks* all.4dtv* #删除中间文件</span><br></pre></td></tr></table></figure></li>
+<h1 id="3-读进化树"><a href="#3-读进化树" class="headerlink" title="3. 读进化树"></a>3. 读进化树</h1><h2 id="3-1-根据树型"><a href="#3-1-根据树型" class="headerlink" title="3.1. 根据树型"></a>3.1. 根据树型</h2><ul>
+<li>当树的所有叶节点没有对齐的时候，枝长可能代表变化量&#x2F;碱基替换数(phylogram)&#x2F;替换速率(ratogram)；</li>
+<li>当树的所有叶节点对齐的时候，枝长如果一致则无信息(cladogram)，枝长如果不一致可能代表进化时长(chronogram)。</li>
 </ul>
-<h1 id="7-references"><a href="#7-references" class="headerlink" title="7. references"></a>7. references</h1><ol>
-<li>ParaAT paper：<a href="https://www.sciencedirect.com/science/article/pii/S0006291X12003518">https://www.sciencedirect.com/science/article/pii/S0006291X12003518</a></li>
-<li>KaKs_Calculator2.0 github：<a href="https://github.com/kullrich/kakscalculator2">https://github.com/kullrich/kakscalculator2</a></li>
-<li>KaKs_Calculator2.0 paper：<a href="https://www.sciencedirect.com/science/article/pii/S1672022910600083?via=ihub">https://www.sciencedirect.com/science/article/pii/S1672022910600083?via%3Dihub</a></li>
+<h1 id="4-reference"><a href="#4-reference" class="headerlink" title="4. reference"></a>4. reference</h1><ol>
+<li><a href="https://en.wikipedia.org/wiki/Phylogenetic_tree">wiki:Phylogenetic_tree</a></li>
+<li><a href="https://www.nature.com/scitable/topicpage/reading-a-phylogenetic-tree-the-meaning-of-41956/">https://www.nature.com/scitable/topicpage/reading-a-phylogenetic-tree-the-meaning-of-41956/</a></li>
 </ol>
 <hr>
 <ul>
@@ -1245,15 +811,19 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 </ul>
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
-        <category>bioinfo</category>
-        <category>Ks</category>
+        <category>bio</category>
+        <category>evolution</category>
+        <category>tree</category>
       </categories>
       <tags>
-        <tag>Ks</tag>
-        <tag>Ka</tag>
-        <tag>4dtv</tag>
-        <tag>ParaAT</tag>
-        <tag>KaKs_Calculator</tag>
+        <tag>phylogeny</tag>
+        <tag>evolutionary tree</tag>
+        <tag>tree</tag>
+        <tag>phylogenetic tree</tag>
+        <tag>bifurcated tree</tag>
+        <tag>cladogram</tag>
+        <tag>phylogram</tag>
+        <tag>chronogram</tag>
       </tags>
   </entry>
   <entry>
@@ -1561,80 +1131,234 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>介绍序列比对(Sequence Alignment)</title>
-    <url>/2021/09/06/bioinfo_alignment_align_intro/</url>
+    <title>集合可视化 —— upset图</title>
+    <url>/2021/09/29/bio_plot_upset/</url>
     <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
 
-<p><strong>写在前面</strong>：</p>
-<ul>
-<li>比对软件的选择：<br>  要是是基因组大数据的序列做比对，优先选择<strong>MAFFT</strong>（快速且兼顾准确）；要是少量序列比对，优先选择<strong>PRANK</strong>（准确）；要是极少数序列比对，哪个方便选哪个，一般<strong>Clustal和MUSCLE</strong>（方便）会整合到许多软件里，会更方便使用。</li>
-<li>过滤软件的选择：<br>  目前暂时优先选<strong>trimAl</strong>。</li>
-</ul>
-<p>update-20211006:今天发现PRANK这个软件比对过后序列的顺序会被打乱，后续处理要小心。</p>
-<h1 id="1-序列比对-sequence-alignment"><a href="#1-序列比对-sequence-alignment" class="headerlink" title="1. 序列比对(sequence alignment)"></a>1. 序列比对(sequence alignment)</h1><h2 id="1-1-概念"><a href="#1-1-概念" class="headerlink" title="1.1. 概念"></a>1.1. 概念</h2><p>在生物信息学中，序列比对(sequence alignment)是指根据相似性排列DNA,RNA或者protein序列，通过在序列中插入空格(gap)来使得相似的碱基&#x2F;氨基酸在相同列对齐的一种方法。</p>
-<h2 id="1-2-方法"><a href="#1-2-方法" class="headerlink" title="1.2. 方法"></a>1.2. 方法</h2><p>比对方法通常包括全局比对(global alignments)和局部比对(local alignments)。</p>
-<ol>
-<li>全局比对(global alignments)<br>全局比对强制要求比对跨越所有序列的整个长度，尽量对齐每条序列的每个碱基，当序列碱基和长度大致相似时最有用。通常全局比对用Needleman-Wunsch算法，是一种动态规划算法。</li>
-<li>局部比对(local alignments)<br>局部比对则确认通常有较大分歧的长序列内的相似性，有更多的计算相似性区域的额外的挑战。通常在较长序列包含相似序列基序的差异较大序列中更有用，常见的是Smith-Waterman算法，也是一种动态规划算法。</li>
-<li>混合比对(Hybrid methods)<br>全局和局部比对都使用的方法。</li>
-</ol>
-<h2 id="1-3-类型"><a href="#1-3-类型" class="headerlink" title="1.3. 类型"></a>1.3. 类型</h2><p>按照序列的数量分为成对比对(pairwise alignment)和多序列比对(multiple sequence alignment)。</p>
-<ol>
-<li>成对比对(pairwise alignment)</li>
-</ol>
-<ul>
-<li>两条序列的比对。因为计算效率很高，通常不需要高精度方法。</li>
-<li>常见的三种方法是点阵法(dot-matrix methods)，动态规划法(dynamic programming)和字节法(word methods)。</li>
-</ul>
-<ol start="2">
-<li>多序列比对(multiple sequence alignment)</li>
+<h1 id="1-多集合展示UpSet图"><a href="#1-多集合展示UpSet图" class="headerlink" title="1. 多集合展示UpSet图"></a>1. 多集合展示UpSet图</h1><p>当有多个群组（集合）的数据，想要展示在不同的事物群组（集合）之间的数学或逻辑联系时，可使用韦恩Venn图和UpSet图展示多集合直接的共享关系和各自的独享关系。</p>
+<p>当集合数量少于等于5个时，多用韦恩图；当集合数量大于5个时，多用upset图。</p>
+<p>韦恩图的绘制推荐使用R包gplots，参考博文<a href="https://yanzhongsino.github.io/2021/09/29/R_plot_venn/">韦恩图</a>；upset图的绘制推荐使用R包UpSetR，参考博文。</p>
+<h2 id="1-1-应用场景"><a href="#1-1-应用场景" class="headerlink" title="1.1. 应用场景"></a>1.1. 应用场景</h2><ol>
+<li>利用orthofinder找到的不同物种的orthogroups的结果来绘制Venn Diagram韦恩图或者UpSet plot图，查看不同物种间共享的orthogroups的数量关系。</li>
 </ol>
-<ul>
-<li>三条及以上序列的比对，是成对比对的扩展，算法上有些差异，以及更复杂。是这篇博客主要讲的内容。</li>
-</ul>
-<h1 id="2-多序列比对-Multiple-Sequence-Alignment-MSA"><a href="#2-多序列比对-Multiple-Sequence-Alignment-MSA" class="headerlink" title="2. 多序列比对(Multiple Sequence Alignment,MSA)"></a>2. 多序列比对(Multiple Sequence Alignment,MSA)</h1><h2 id="2-1-概念"><a href="#2-1-概念" class="headerlink" title="2.1. 概念"></a>2.1. 概念</h2><p>多序列比对(Multiple Sequence Alignment,MSA)是对三个以上的生物学序列（biological sequence），如蛋白质序列、DNA序列或RNA序列所作的序列比对。<br>目的是在不改变序列顺序的前提下，尽可能地把不同序列的相同碱基或者氨基酸排在同一列，并认为同一列的序列在进化上是同源的，有共同的祖先。</p>
-<h2 id="2-2-用途"><a href="#2-2-用途" class="headerlink" title="2.2. 用途"></a>2.2. 用途</h2><p>比对之后的序列，可用于进化学研究。比如，比较序列差异来推断序列进化历史（eg. 分化时间）和进化方式(突变&#x2F;插入缺失&#x2F;倒置等)。</p>
-<p>常见的应用：</p>
-<ul>
-<li>构建系统发育树：亲缘关系的分析。</li>
-<li>比较基因功能：比较不同物种间的基因功能。</li>
-<li>预测蛋白质结构，分类和注释蛋白和ncRNA：通过与已知功能的基因序列的相似性比对，进行基因功能的预测和注释。</li>
-<li>序列拼接：通过比对，组装基因组。</li>
-<li>突变分析：不同个体基因组间的差异；不同物种基因组间的共线性和结构变异。</li>
-<li>检测适应性进化</li>
-</ul>
-<h2 id="2-3-重要性"><a href="#2-3-重要性" class="headerlink" title="2.3. 重要性"></a>2.3. 重要性</h2><p>多序列比对的准确性与进化分析的准确性息息相关，不同比对软件和过滤软件的结果不同。</p>
-<p>Blackburner和Whelan在2013年发表在MBE上的文章《Class of Multiple Sequence Alignment Algorithm Affects Genomic Analysis》对不同的比对算法和软件进行了比较分析，提出多序列比对MSA方法对下游分析至关重要，系统发育树和分支长度的推断高度依赖使用的多序列比对MSA类别，基于相似性的比对软件倾向于识别适应性进化（正选择）。</p>
-<p>简单的对齐后过滤不能解决不同MSA方法间的差异。</p>
-<h1 id="3-多序列比对-Multiple-Sequence-Alignment-MSA-的算法"><a href="#3-多序列比对-Multiple-Sequence-Alignment-MSA-的算法" class="headerlink" title="3. 多序列比对(Multiple Sequence Alignment,MSA)的算法"></a>3. 多序列比对(Multiple Sequence Alignment,MSA)的算法</h1><p>多序列比对是双序列比对的扩展。</p>
-<h2 id="3-1-动态规划算法-dynamic-programming-——-主要用于双序列比对"><a href="#3-1-动态规划算法-dynamic-programming-——-主要用于双序列比对" class="headerlink" title="3.1. 动态规划算法(dynamic programming) —— 主要用于双序列比对"></a>3.1. 动态规划算法(dynamic programming) —— 主要用于双序列比对</h2><p>算法特点：</p>
-<ul>
-<li>可以给出最优解。</li>
-<li>将一个二维的动态规划矩阵扩展到三维或多维，即可用于多序列比对。</li>
-<li>比对序列超过3个，需要的存储空间和运算时间都使得比对应用无法实现。<h3 id="3-1-1-基于全局比对的算法：Needleman-Wunsch算法"><a href="#3-1-1-基于全局比对的算法：Needleman-Wunsch算法" class="headerlink" title="3.1.1. 基于全局比对的算法：Needleman-Wunsch算法"></a>3.1.1. 基于全局比对的算法：Needleman-Wunsch算法</h3></li>
-</ul>
-<ol>
-<li><p><strong>全局比对</strong><br> 全局比对是指将参与比对的两条序列里面的所有字符进行比对。全局比对在全局范围内对两条序列进行比对打分，找出最佳比对，主要被用来寻找关系密切的序列。其可以用来鉴别或证明新序列与已知序列家族的同源性，是进行分子进化分析的重要前提。其代表是Needleman-Wunsch算法。</p>
+<h2 id="1-2-数据准备"><a href="#1-2-数据准备" class="headerlink" title="1.2. 数据准备"></a>1.2. 数据准备</h2><p>UpSetR的输入文件是表格形式，第一列是数据分类信息，后面列是每一个集合占一列（每个物种占一列），表格内容是1&#x2F;0表示集合在每一个类别是否有数据。</p>
+<h3 id="1-2-1-UpSetR自带数据"><a href="#1-2-1-UpSetR自带数据" class="headerlink" title="1.2.1. UpSetR自带数据"></a>1.2.1. UpSetR自带数据</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">movies <span class="operator">&lt;-</span> read.csv<span class="punctuation">(</span>system.file<span class="punctuation">(</span><span class="string">&quot;extdata&quot;</span><span class="punctuation">,</span> <span class="string">&quot;movies.csv&quot;</span><span class="punctuation">,</span> package <span class="operator">=</span> <span class="string">&quot;UpSetR&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;;&quot;</span><span class="punctuation">)</span> <span class="comment"># 载入UpSetR包提供的电影数据用作示例。第一行是电影的类别名称，第一列是电影名，第二列是电影上映年度，后面列中的1/0是代表电影是否属于相应的类别。</span></span><br><span class="line">mutations <span class="operator">&lt;-</span> read.csv<span class="punctuation">(</span>system.file<span class="punctuation">(</span><span class="string">&quot;extdata&quot;</span><span class="punctuation">,</span> <span class="string">&quot;mutations.csv&quot;</span><span class="punctuation">,</span> package <span class="operator">=</span> <span class="string">&quot;UpSetR&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span> sep <span class="operator">=</span> <span class="string">&quot;,&quot;</span><span class="punctuation">)</span> <span class="comment"># 载入UpsetR包提供的突变数据用作示例。第一行是不同的基因简称，第一列是基因特征ID，数据中的1/0是代表基因是否有对应的特征。</span></span><br><span class="line"></span><br><span class="line">head<span class="punctuation">(</span>movies<span class="punctuation">)</span> <span class="comment"># 大致浏览一下该数据集,数据集太长，就只看前几列</span></span><br><span class="line">View<span class="punctuation">(</span>movies<span class="punctuation">)</span> <span class="comment"># 弹出窗口，可查看数据。</span></span><br><span class="line"></span><br><span class="line">head<span class="punctuation">(</span>mutations<span class="punctuation">)</span> <span class="comment"># 大致浏览一下该数据集,数据集太长，就只看前几列</span></span><br><span class="line">View<span class="punctuation">(</span>mutations<span class="punctuation">)</span> <span class="comment"># 弹出窗口，可查看数据。</span></span><br></pre></td></tr></table></figure>
+<br>
+
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>movies<span class="punctuation">)</span></span><br><span class="line">                                Name ReleaseDate Action Adventure Children Comedy Crime Documentary Drama Fantasy Noir Horror Musical Mystery Romance SciFi Thriller War Western AvgRating Watches</span><br><span class="line"><span class="number">1</span>                   Toy Story <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">1</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">4.15</span>    <span class="number">2077</span></span><br><span class="line"><span class="number">2</span>                     Jumanji <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">1</span>        <span class="number">1</span>      <span class="number">0</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.20</span>     <span class="number">701</span></span><br><span class="line"><span class="number">3</span>            Grumpier Old Men <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">1</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.02</span>     <span class="number">478</span></span><br><span class="line"><span class="number">4</span>           Waiting to Exhale <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">1</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">2.73</span>     <span class="number">170</span></span><br><span class="line"><span class="number">5</span> Father of the Bride Part II <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">0</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.01</span>     <span class="number">296</span></span><br><span class="line"><span class="number">6</span>                        Heat <span class="punctuation">(</span><span class="number">1995</span><span class="punctuation">)</span>        <span class="number">1995</span>      <span class="number">1</span>         <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">1</span>           <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>     <span class="number">0</span>        <span class="number">1</span>   <span class="number">0</span>       <span class="number">0</span>      <span class="number">3.88</span>     <span class="number">940</span></span><br><span class="line"><span class="operator">&gt;</span> head<span class="punctuation">(</span>mutations<span class="punctuation">)</span></span><br><span class="line">  Identifier TTN PTEN TP53 EGFR MUC16 FLG RYR2 PCLO PIK3R1 PIK3CA NF1 MUC17 HMCN1 SPTA1 USH2A RB1 PKHD1 OBSCN AHNAK2 RYR3 RELN FRAS1 GPR98 DNAH5 ATRX APOB TCHH SYNE1 LRP2 KEL HRNR DNAH3 COL6A3 MUC5B LAMA1 DSP</span><br><span class="line"><span class="number">1</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0003</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">1</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">2</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0033</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>   <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">3</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0047</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">4</span>    <span class="number">02</span><span class="operator">-</span><span class="number">0055</span>   <span class="number">1</span>    <span class="number">1</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">5</span>    <span class="number">02</span><span class="operator">-</span><span class="number">2470</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line"><span class="number">6</span>    <span class="number">02</span><span class="operator">-</span><span class="number">2483</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>      <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>   <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span></span><br><span class="line">  DNAH8 CNTNAP2 SDK1 NBPF10 DNAH2 NLRP5 MLL3 IDH1 HCN1 FCGBP DOCK5 RIMS2 PCDHA1 MXRA5 HEATR7B2 GRIN2A FGD5 TMEM132D STAG2 SEMA3C SCN9A PRDM9 POM121L12 PIK3CG PDGFRA GABRA6 FLG2 FBN3 FBN2 FAT2 DNAH11 DMD COL1A2</span><br><span class="line"><span class="number">1</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">2</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">3</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">4</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">1</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">5</span>     <span class="number">0</span>       <span class="number">1</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line"><span class="number">6</span>     <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>    <span class="number">1</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>        <span class="number">0</span>     <span class="number">0</span>      <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>         <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>   <span class="number">0</span>      <span class="number">0</span></span><br><span class="line">  ABCC9 XIRP2 TSHZ2 TEX15 SLIT3 RBM47 PIK3C2G PCDH11X MYH2 MACF1 KSR2 DNAH9 DCHS2 CSMD3 CDH18 BCOR AHNAK ZAN TRRAP THSD7B TAF1L SPAG17 SLCO5A1 SCN10A RYR1 RIMBP2 PLEKHG4B PCDHB7 NPTX2 NOS1 LZTR1</span><br><span class="line"><span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">2</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">3</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">4</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">1</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">1</span>     <span class="number">0</span></span><br><span class="line"><span class="number">5</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">1</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">0</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">1</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br><span class="line"><span class="number">6</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>       <span class="number">0</span>       <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span>   <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>      <span class="number">0</span>       <span class="number">1</span>      <span class="number">0</span>    <span class="number">0</span>      <span class="number">0</span>        <span class="number">0</span>      <span class="number">0</span>     <span class="number">0</span>    <span class="number">0</span>     <span class="number">0</span></span><br></pre></td></tr></table></figure>
+
+<h3 id="1-2-2-orthofinder数据"><a href="#1-2-2-orthofinder数据" class="headerlink" title="1.2.2. orthofinder数据"></a>1.2.2. orthofinder数据</h3><p>从orthofinder的结果文件Results_Aug14&#x2F;Orthogroups&#x2F;Orthogroups.GeneCount.tsv稍加处理就可以作为输入文件，展示不同物种的orthogroups集合的共享情况。</p>
+<p><code>sed -E &quot;s/\t[1-9][0-9]*/\t1/g&quot; Orthogroups.GeneCount.tsv |sed &quot;s/\.pep//g&quot; &gt;orthogroups.upset</code> # 把Orthogroups.GeneCount.tsv中的非零数字替换成1。</p>
+<p>在R中用<code>mutations &lt;- read.csv(&quot;orthogroups.upset&quot;, header=TRUE, sep = &quot;\t&quot;)</code>读取orthogroups.upset。</p>
+<h2 id="1-3-UpSetR包安装"><a href="#1-3-UpSetR包安装" class="headerlink" title="1.3. UpSetR包安装"></a>1.3. UpSetR包安装</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">install.packages(&quot;UpSetR&quot;); # 安装</span><br><span class="line">library(UpSetR); # 载入UpSetR</span><br><span class="line">require(ggplot2); require(plyr); require(gridExtra); require(grid); # 载入包</span><br></pre></td></tr></table></figure>
+
+<h2 id="1-4-UpSetR包使用"><a href="#1-4-UpSetR包使用" class="headerlink" title="1.4. UpSetR包使用"></a>1.4. UpSetR包使用</h2><h3 id="1-4-1-upset函数"><a href="#1-4-1-upset函数" class="headerlink" title="1.4.1. upset函数"></a>1.4.1. upset函数</h3><ol>
+<li><p><code>upset(mutations)</code>可以看到upset图的效果</p>
 </li>
-<li><p>全局比对算法特点：</p>
+<li><p>调整参数做指定数据显示</p>
 </li>
 </ol>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>mutations<span class="punctuation">,</span> </span><br><span class="line">sets <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;MUC16&quot;</span><span class="punctuation">,</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span><span class="string">&quot;TP53&quot;</span><span class="punctuation">,</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span><span class="comment"># 查看特定的几个集合/几种电影类别</span></span><br><span class="line">nset <span class="operator">=</span> <span class="number">4</span><span class="punctuation">,</span> <span class="comment"># 最多展示多少个集合数据</span></span><br><span class="line">nintersects <span class="operator">=</span> <span class="number">20</span><span class="punctuation">,</span> <span class="comment"># 展示数量多的前多少个交集</span></span><br><span class="line">mb.ratio <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.55</span><span class="punctuation">,</span> <span class="number">0.45</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 控制上方条形图以及下方点图的比例</span></span><br><span class="line">order.by <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;degree&quot;</span><span class="punctuation">,</span> <span class="string">&quot;freq&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 交集如何排序，这里先根据degree(数量，交集包含的数字大小)，然后再根据freq(频率，涉及到的交集个数)</span></span><br><span class="line">keep.order <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span> <span class="comment"># keep.order按照sets参数的顺序排序</span></span><br><span class="line">decreasing <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="literal">TRUE</span><span class="punctuation">,</span><span class="literal">FALSE</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 变量如何排序；对应order.by参数。这里表示degree降序，freq升序。</span></span><br><span class="line">number.angles <span class="operator">=</span> <span class="number">30</span><span class="punctuation">,</span> <span class="comment"># 调整柱形图上数字角度</span></span><br><span class="line">point.size <span class="operator">=</span> <span class="number">2</span><span class="punctuation">,</span> line.size <span class="operator">=</span> <span class="number">1</span><span class="punctuation">,</span> <span class="comment"># 点和线的大小</span></span><br><span class="line">mainbar.y.label <span class="operator">=</span> <span class="string">&quot;Intersection size of gene family&quot;</span><span class="punctuation">,</span>sets.x.label <span class="operator">=</span> <span class="string">&quot;genome size&quot;</span><span class="punctuation">,</span> <span class="comment"># 坐标轴名称</span></span><br><span class="line">text.scale <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1.3</span><span class="punctuation">,</span> <span class="number">1.3</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">,</span> <span class="number">1.5</span><span class="punctuation">,</span> <span class="number">1</span><span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment"># 六个数字，分别控制c(intersectionsize title, intersection size tick labels, set size title, set size ticklabels, set names, numbers above bars)</span></span><br></pre></td></tr></table></figure>
+
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_movies.png?raw=true" width=100% height=60% title="upset_movies" align=center/>
+
+<p><strong><p align="center">Figure 1. movies upset</p></strong></p>
+<h3 id="1-4-2-queries参数"><a href="#1-4-2-queries参数" class="headerlink" title="1.4.2. queries参数"></a>1.4.2. queries参数</h3><p>upset函数中可以添加queries参数，用于突出显示（上色）部分数据。</p>
+<ol>
+<li><p>queries<br>queries是一个由多个query组成的list；每个query也是一个list，作为一次查询数据并突出显示的请求。</p>
+</li>
+<li><p>query<br>query也是list格式，由查询函数query和其他参数（param,color,active,query.name）组成，其中query,param是必须设置的参数。</p>
 <ul>
-<li>打分矩阵：罚分分值不同，比对结果不同。</li>
-<li>计算比对最高得分的算法：常用Needleman-Wunsch算法。</li>
-<li>打印出最高得分相应的序列比对结果：根据得分矩阵回溯，找到最高得分结果。</li>
+<li>query: 指定查询函数，UpSetR有内置(比如intersects)，也可以自定义函数后调用。</li>
+<li>param: list格式, 指定query作用的数据。</li>
+<li>color：设置颜色，可选设置。</li>
+<li>active：显示类型，TRUE&#x2F;T表示用颜色覆盖条形图，FALSE&#x2F;F表示在条形图顶端显示三角形。</li>
+<li>query.name：添加query图例名称。</li>
 </ul>
-<h3 id="3-1-2-基于局部比对的算法：Smith-Waterman算法"><a href="#3-1-2-基于局部比对的算法：Smith-Waterman算法" class="headerlink" title="3.1.2. 基于局部比对的算法：Smith-Waterman算法"></a>3.1.2. 基于局部比对的算法：Smith-Waterman算法</h3><ol>
-<li><p><strong>局部比对</strong><br> 与全局比对不同，局部比对不必对两个完整的序列进行比对，而是在每个序列中使用某些局部区域片段进行比对。其产生的需求在于、人们发现有的蛋白序列虽然在序列整体上表现出较大的差异性，但是在某些局部区域能独立的发挥相同的功能，序列相当保守。这时候依靠全局比对明显不能得到这些局部相似序列的。其次，在真核生物的基因中，内含子片段表现出了极大变异性，外显子区域却较为保守，这时候全局比对表现出了其局限性，无法找出这些局部相似性序列。其代表是Smith-Waterman局部比对算法。BLAST是局部比对的一种推广。</p>
 </li>
-<li><p>Smith-Waterman算法特点</p>
+<li><p>queries参数示例</p>
 </li>
 </ol>
 <ul>
-<li>是一种用来寻找并比较具有局部相似性区域的动态规划算法，适用于亲缘关系较远，整体上不具有相似性而在一些较小区域存在局部相似性的两条序列。</li>
-<li>使用迭代方法计算出每个序列的相似分值，保存在一个得分矩阵M中，然后根据这个得分矩阵，通过动态规划的方法回溯找到最优的比对序列。</li>
+<li>把”EGFR”和”TP53”两个基因共同拥有的突变标上蓝色；把”TTN”基因特有的突变在直方图上标为红色。</li>
 </ul>
-<h2 id="3-2-启发式搜索算法-heuristic-algorithm-：BWT算法和BLAST算法"><a href="#3-2-启发式搜索算法-heuristic-algorithm-：BWT算法和BLAST算法" class="headerlink" title="3.2. 启发式搜索算法(heuristic algorithm)：BWT算法和BLAST算法"></a>3.2. 启发式搜索算法(heuristic algorithm)：BWT算法和BLAST算法</h2><p>一个基于直观或经验构造的算法，在可接受的花费下给出待解决组合优化问题每一个实例的一个可行解，该可行解与最优解的偏离程度一般不能被预计；目前绝大多数应用的比对软件都是启发式算法。</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>mutations<span class="punctuation">,</span> sets<span class="operator">=</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;MUC16&quot;</span><span class="punctuation">,</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span><span class="string">&quot;TP53&quot;</span><span class="punctuation">,</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span>  </span><br><span class="line">params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;EGFR&quot;</span><span class="punctuation">,</span> <span class="string">&quot;TP53&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 指定作用的数据</span></span><br><span class="line">color <span class="operator">=</span> <span class="string">&quot;blue&quot;</span><span class="punctuation">,</span> <span class="comment"># 设置颜色，未设置会调用默认调色板</span></span><br><span class="line">active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span>   <span class="comment"># 条形图被颜色覆盖</span></span><br><span class="line">query.name <span class="operator">=</span> <span class="string">&quot;share EGFR and TP53&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 添加query图例</span></span><br><span class="line"><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> params<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;TTN&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active<span class="operator">=</span><span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_mutations.png?raw=true" width=100% height=60% title="upset_mutations" align=center/>
+
+<p><strong><p align="center">Figure 2. mutations upset</p></strong></p>
+<ul>
+<li>把同属Drama和Thriller的电影突出显示，把1970-1980的电影标红。<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">between &lt;- function(row, min, max)&#123;</span><br><span class="line">  newData &lt;- (row[&quot;ReleaseDate&quot;] &lt; max) &amp; (row[&quot;ReleaseDate&quot;] &gt; min)</span><br><span class="line">&#125; # 自定义between函数</span><br><span class="line"></span><br><span class="line">upset(movies, sets=c(&quot;Drama&quot;,&quot;Comedy&quot;,&quot;Action&quot;,&quot;Thriller&quot;,&quot;Western&quot;,&quot;Documentary&quot;),</span><br><span class="line">      queries = list(list(query = intersects, params = list(&quot;Drama&quot;, &quot;Thriller&quot;)),</span><br><span class="line">                     list(query = between, params=list(1970,1980), color=&quot;red&quot;, active=TRUE)))</span><br></pre></td></tr></table></figure></li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_movies2.png?raw=true" width=100% height=60% title="upset_movies2" align=center/>
+
+<p><strong><p align="center">Figure 3. movies upset 2</p></strong></p>
+<h3 id="1-4-3-添加属性图"><a href="#1-4-3-添加属性图" class="headerlink" title="1.4.3. 添加属性图"></a>1.4.3. 添加属性图</h3><ol>
+<li>添加箱线图<br>每次最多添加两个箱线图<br><code>upset(movies, boxplot.summary = c(&quot;AvgRating&quot;, &quot;ReleaseDate&quot;)) </code></li>
+</ol>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/R_plot_upset_boxplot.png?raw=true" width=100% height=70% title="upset_boxplot" align=center/>
+
+<p><strong><p align="center">Figure 4. movies upset boxplot</p></strong></p>
+<h4 id="1-4-3-1-attribute-plots参数"><a href="#1-4-3-1-attribute-plots参数" class="headerlink" title="1.4.3.1. attribute.plots参数"></a>1.4.3.1. attribute.plots参数</h4><p>attribute.plots参数用于添加属性图，内置有柱形图，散点图，热图等。<br>如果想添加密度曲线图，可以自定义plot函数后添加。</p>
+<ol>
+<li>添加柱形图和散点图<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">upset<span class="punctuation">(</span>movies<span class="punctuation">,</span> sets<span class="operator">=</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span><span class="string">&quot;Comedy&quot;</span><span class="punctuation">,</span><span class="string">&quot;Action&quot;</span><span class="punctuation">,</span><span class="string">&quot;Thriller&quot;</span><span class="punctuation">,</span><span class="string">&quot;Western&quot;</span><span class="punctuation">,</span><span class="string">&quot;Documentary&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">      queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Thriller&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">                     <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> between<span class="punctuation">,</span> params<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span><span class="number">1970</span><span class="punctuation">,</span><span class="number">1980</span><span class="punctuation">)</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span></span><br><span class="line">      attribute.plots<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span>gridrows<span class="operator">=</span><span class="number">60</span><span class="punctuation">,</span> <span class="comment"># 添加属性图</span></span><br><span class="line">      plots<span class="operator">=</span><span class="built_in">list</span><span class="punctuation">(</span></span><br><span class="line">        <span class="built_in">list</span><span class="punctuation">(</span>plot<span class="operator">=</span>scatter_plot<span class="punctuation">,</span> <span class="comment"># 散点图</span></span><br><span class="line">        x<span class="operator">=</span><span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> y<span class="operator">=</span><span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> <span class="comment"># 指定横纵坐标</span></span><br><span class="line">        queries <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># T表示显示queries定义的颜色</span></span><br><span class="line">        <span class="built_in">list</span><span class="punctuation">(</span>plot<span class="operator">=</span> histogram<span class="punctuation">,</span> x<span class="operator">=</span><span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 直方图</span></span><br><span class="line">        ncols <span class="operator">=</span> <span class="number">2</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="comment"># 添加的图分两列</span></span><br><span class="line">      query.legend <span class="operator">=</span> <span class="string">&quot;top&quot;</span><span class="punctuation">)</span> <span class="comment"># query图例放在上方</span></span><br></pre></td></tr></table></figure></li>
+</ol>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_scatter_histogram.png?raw=true" width=100% height=70% title="upset_scatter_histograms" align=center/>
+
+<p><strong><p align="center">Figure 5. movies upset scatter histograms</p></strong></p>
+<ol start="2">
+<li>添加密度曲线图</li>
+</ol>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line"><span class="comment">#自定义密度曲线</span></span><br><span class="line">another.plot <span class="operator">&lt;-</span> <span class="keyword">function</span><span class="punctuation">(</span>data<span class="punctuation">,</span> x<span class="punctuation">,</span> y<span class="punctuation">)</span> <span class="punctuation">&#123;</span></span><br><span class="line">    data<span class="operator">$</span>decades <span class="operator">&lt;-</span> round_any<span class="punctuation">(</span><span class="built_in">as.integer</span><span class="punctuation">(</span>unlist<span class="punctuation">(</span>data<span class="punctuation">[</span>y<span class="punctuation">]</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="number">10</span><span class="punctuation">,</span> <span class="built_in">ceiling</span><span class="punctuation">)</span></span><br><span class="line">    data <span class="operator">&lt;-</span> data<span class="punctuation">[</span>which<span class="punctuation">(</span>data<span class="operator">$</span>decades <span class="operator">&gt;=</span> <span class="number">1970</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="punctuation">]</span></span><br><span class="line">    myplot <span class="operator">&lt;-</span> <span class="punctuation">(</span>ggplot<span class="punctuation">(</span>data<span class="punctuation">,</span> aes_string<span class="punctuation">(</span>x <span class="operator">=</span> x<span class="punctuation">)</span><span class="punctuation">)</span> <span class="operator">+</span> geom_density<span class="punctuation">(</span>aes<span class="punctuation">(</span>fill <span class="operator">=</span> factor<span class="punctuation">(</span>decades<span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">        alpha <span class="operator">=</span> <span class="number">0.4</span><span class="punctuation">)</span> <span class="operator">+</span> theme<span class="punctuation">(</span>plot.margin <span class="operator">=</span> unit<span class="punctuation">(</span><span class="built_in">c</span><span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">,</span> <span class="number">0</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="string">&quot;cm&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> legend.key.size <span class="operator">=</span> unit<span class="punctuation">(</span><span class="number">0.4</span><span class="punctuation">,</span> <span class="string">&quot;cm&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
+
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>plyr<span class="punctuation">)</span></span><br><span class="line">upset<span class="punctuation">(</span>movies<span class="punctuation">,</span> main.bar.color <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span> mb.ratio <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">0.5</span><span class="punctuation">,</span> <span class="number">0.5</span><span class="punctuation">)</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color <span class="operator">=</span> <span class="string">&quot;red&quot;</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Action&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Drama&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>query <span class="operator">=</span> intersects<span class="punctuation">,</span> </span><br><span class="line">    params <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="string">&quot;Drama&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Comedy&quot;</span><span class="punctuation">,</span> <span class="string">&quot;Action&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> color <span class="operator">=</span> <span class="string">&quot;orange&quot;</span><span class="punctuation">,</span> active <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> </span><br><span class="line">    attribute.plots <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span>gridrows <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> plots <span class="operator">=</span> <span class="built_in">list</span><span class="punctuation">(</span><span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> histogram<span class="punctuation">,</span> </span><br><span class="line">        x <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> scatter_plot<span class="punctuation">,</span> x <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> </span><br><span class="line">        y <span class="operator">=</span> <span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">)</span><span class="punctuation">,</span> <span class="built_in">list</span><span class="punctuation">(</span>plot <span class="operator">=</span> another.plot<span class="punctuation">,</span> x <span class="operator">=</span> <span class="string">&quot;AvgRating&quot;</span><span class="punctuation">,</span> </span><br><span class="line">        y <span class="operator">=</span> <span class="string">&quot;ReleaseDate&quot;</span><span class="punctuation">,</span> queries <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">,</span> ncols <span class="operator">=</span> <span class="number">3</span><span class="punctuation">)</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/3cabd7b9fd4f1552a69bf5f1797f489be352717a/source/images/R_plot_upset_density.png?raw=true" width=100% height=70% title="upset_density" align=center/>
+
+<p><strong><p align="center">Figure 6. movies upset density</p></strong></p>
+<h1 id="2-references"><a href="#2-references" class="headerlink" title="2. references"></a>2. references</h1><ol>
+<li><a href="https://github.com/hms-dbmi/UpSetR">https://github.com/hms-dbmi/UpSetR</a></li>
+<li><a href="https://www.jianshu.com/p/324aae3d5ea4">https://www.jianshu.com/p/324aae3d5ea4</a></li>
+<li><a href="https://zhuanlan.zhihu.com/p/35303590">https://zhuanlan.zhihu.com/p/35303590</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>
+]]></content>
+      <categories>
+        <category>bio</category>
+        <category>plot</category>
+      </categories>
+      <tags>
+        <tag>R package</tag>
+        <tag>R</tag>
+        <tag>plot</tag>
+        <tag>UpSetR</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>质体的分类、角色和转换</title>
+    <url>/2024/05/31/bio_plastid_typesandroles/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2095592331&auto=1&height=32"></iframe></div>
+
+<p>植物细胞内的质体会随着植物生长和环境的变化而变化，不同植物组织和器官内的质体是不同的。</p>
+<img src="https://www.frontiersin.org/files/Articles/692024/fpls-12-692024-HTML-r1/image_m/fpls-12-692024-g001.jpg" width=80% title="plastid Types and Roles" align=center/>
+
+<p><strong><p align="center">Figure 1. 植物质体的分类、角色和转换</p></strong></p>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/edited/plastidTypesandRoles.jpg?raw=true" width=80% title="plastid Types and Roles" align=center/>
+
+<p><strong><p align="center">Figure 2. 植物质体的分类、角色和转换</p></strong></p>
+<h1 id="reference"><a href="#reference" class="headerlink" title="reference"></a>reference</h1><ol>
+<li><a href="https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.692024/full#B52">https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.692024/full#B52</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bio</category>
+        <category>plastid</category>
+      </categories>
+      <tags>
+        <tag>plastid</tag>
+        <tag>质体</tag>
+        <tag>proplastid</tag>
+        <tag>原质体</tag>
+        <tag>leucoplast</tag>
+        <tag>白质体</tag>
+        <tag>amyloplast</tag>
+        <tag>elaioplast</tag>
+        <tag>proteinoplast</tag>
+        <tag>chromoplast</tag>
+        <tag>色质体</tag>
+        <tag>chloroplast</tag>
+        <tag>叶绿体</tag>
+        <tag>etioplast</tag>
+        <tag>黄化体</tag>
+        <tag>gerontoplast</tag>
+        <tag>老年体</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>介绍序列比对(Sequence Alignment)</title>
+    <url>/2021/09/06/bioinfo_alignment_align_intro/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
+
+<p><strong>写在前面</strong>：</p>
+<ul>
+<li>比对软件的选择：<br>  要是是基因组大数据的序列做比对，优先选择<strong>MAFFT</strong>（快速且兼顾准确）；要是少量序列比对，优先选择<strong>PRANK</strong>（准确）；要是极少数序列比对，哪个方便选哪个，一般<strong>Clustal和MUSCLE</strong>（方便）会整合到许多软件里，会更方便使用。</li>
+<li>过滤软件的选择：<br>  目前暂时优先选<strong>trimAl</strong>。</li>
+</ul>
+<p>update-20211006:今天发现PRANK这个软件比对过后序列的顺序会被打乱，后续处理要小心。</p>
+<h1 id="1-序列比对-sequence-alignment"><a href="#1-序列比对-sequence-alignment" class="headerlink" title="1. 序列比对(sequence alignment)"></a>1. 序列比对(sequence alignment)</h1><h2 id="1-1-概念"><a href="#1-1-概念" class="headerlink" title="1.1. 概念"></a>1.1. 概念</h2><p>在生物信息学中，序列比对(sequence alignment)是指根据相似性排列DNA,RNA或者protein序列，通过在序列中插入空格(gap)来使得相似的碱基&#x2F;氨基酸在相同列对齐的一种方法。</p>
+<h2 id="1-2-方法"><a href="#1-2-方法" class="headerlink" title="1.2. 方法"></a>1.2. 方法</h2><p>比对方法通常包括全局比对(global alignments)和局部比对(local alignments)。</p>
+<ol>
+<li>全局比对(global alignments)<br>全局比对强制要求比对跨越所有序列的整个长度，尽量对齐每条序列的每个碱基，当序列碱基和长度大致相似时最有用。通常全局比对用Needleman-Wunsch算法，是一种动态规划算法。</li>
+<li>局部比对(local alignments)<br>局部比对则确认通常有较大分歧的长序列内的相似性，有更多的计算相似性区域的额外的挑战。通常在较长序列包含相似序列基序的差异较大序列中更有用，常见的是Smith-Waterman算法，也是一种动态规划算法。</li>
+<li>混合比对(Hybrid methods)<br>全局和局部比对都使用的方法。</li>
+</ol>
+<h2 id="1-3-类型"><a href="#1-3-类型" class="headerlink" title="1.3. 类型"></a>1.3. 类型</h2><p>按照序列的数量分为成对比对(pairwise alignment)和多序列比对(multiple sequence alignment)。</p>
+<ol>
+<li>成对比对(pairwise alignment)</li>
+</ol>
+<ul>
+<li>两条序列的比对。因为计算效率很高，通常不需要高精度方法。</li>
+<li>常见的三种方法是点阵法(dot-matrix methods)，动态规划法(dynamic programming)和字节法(word methods)。</li>
+</ul>
+<ol start="2">
+<li>多序列比对(multiple sequence alignment)</li>
+</ol>
+<ul>
+<li>三条及以上序列的比对，是成对比对的扩展，算法上有些差异，以及更复杂。是这篇博客主要讲的内容。</li>
+</ul>
+<h1 id="2-多序列比对-Multiple-Sequence-Alignment-MSA"><a href="#2-多序列比对-Multiple-Sequence-Alignment-MSA" class="headerlink" title="2. 多序列比对(Multiple Sequence Alignment,MSA)"></a>2. 多序列比对(Multiple Sequence Alignment,MSA)</h1><h2 id="2-1-概念"><a href="#2-1-概念" class="headerlink" title="2.1. 概念"></a>2.1. 概念</h2><p>多序列比对(Multiple Sequence Alignment,MSA)是对三个以上的生物学序列（biological sequence），如蛋白质序列、DNA序列或RNA序列所作的序列比对。<br>目的是在不改变序列顺序的前提下，尽可能地把不同序列的相同碱基或者氨基酸排在同一列，并认为同一列的序列在进化上是同源的，有共同的祖先。</p>
+<h2 id="2-2-用途"><a href="#2-2-用途" class="headerlink" title="2.2. 用途"></a>2.2. 用途</h2><p>比对之后的序列，可用于进化学研究。比如，比较序列差异来推断序列进化历史（eg. 分化时间）和进化方式(突变&#x2F;插入缺失&#x2F;倒置等)。</p>
+<p>常见的应用：</p>
+<ul>
+<li>构建系统发育树：亲缘关系的分析。</li>
+<li>比较基因功能：比较不同物种间的基因功能。</li>
+<li>预测蛋白质结构，分类和注释蛋白和ncRNA：通过与已知功能的基因序列的相似性比对，进行基因功能的预测和注释。</li>
+<li>序列拼接：通过比对，组装基因组。</li>
+<li>突变分析：不同个体基因组间的差异；不同物种基因组间的共线性和结构变异。</li>
+<li>检测适应性进化</li>
+</ul>
+<h2 id="2-3-重要性"><a href="#2-3-重要性" class="headerlink" title="2.3. 重要性"></a>2.3. 重要性</h2><p>多序列比对的准确性与进化分析的准确性息息相关，不同比对软件和过滤软件的结果不同。</p>
+<p>Blackburner和Whelan在2013年发表在MBE上的文章《Class of Multiple Sequence Alignment Algorithm Affects Genomic Analysis》对不同的比对算法和软件进行了比较分析，提出多序列比对MSA方法对下游分析至关重要，系统发育树和分支长度的推断高度依赖使用的多序列比对MSA类别，基于相似性的比对软件倾向于识别适应性进化（正选择）。</p>
+<p>简单的对齐后过滤不能解决不同MSA方法间的差异。</p>
+<h1 id="3-多序列比对-Multiple-Sequence-Alignment-MSA-的算法"><a href="#3-多序列比对-Multiple-Sequence-Alignment-MSA-的算法" class="headerlink" title="3. 多序列比对(Multiple Sequence Alignment,MSA)的算法"></a>3. 多序列比对(Multiple Sequence Alignment,MSA)的算法</h1><p>多序列比对是双序列比对的扩展。</p>
+<h2 id="3-1-动态规划算法-dynamic-programming-——-主要用于双序列比对"><a href="#3-1-动态规划算法-dynamic-programming-——-主要用于双序列比对" class="headerlink" title="3.1. 动态规划算法(dynamic programming) —— 主要用于双序列比对"></a>3.1. 动态规划算法(dynamic programming) —— 主要用于双序列比对</h2><p>算法特点：</p>
+<ul>
+<li>可以给出最优解。</li>
+<li>将一个二维的动态规划矩阵扩展到三维或多维，即可用于多序列比对。</li>
+<li>比对序列超过3个，需要的存储空间和运算时间都使得比对应用无法实现。<h3 id="3-1-1-基于全局比对的算法：Needleman-Wunsch算法"><a href="#3-1-1-基于全局比对的算法：Needleman-Wunsch算法" class="headerlink" title="3.1.1. 基于全局比对的算法：Needleman-Wunsch算法"></a>3.1.1. 基于全局比对的算法：Needleman-Wunsch算法</h3></li>
+</ul>
+<ol>
+<li><p><strong>全局比对</strong><br> 全局比对是指将参与比对的两条序列里面的所有字符进行比对。全局比对在全局范围内对两条序列进行比对打分，找出最佳比对，主要被用来寻找关系密切的序列。其可以用来鉴别或证明新序列与已知序列家族的同源性，是进行分子进化分析的重要前提。其代表是Needleman-Wunsch算法。</p>
+</li>
+<li><p>全局比对算法特点：</p>
+</li>
+</ol>
+<ul>
+<li>打分矩阵：罚分分值不同，比对结果不同。</li>
+<li>计算比对最高得分的算法：常用Needleman-Wunsch算法。</li>
+<li>打印出最高得分相应的序列比对结果：根据得分矩阵回溯，找到最高得分结果。</li>
+</ul>
+<h3 id="3-1-2-基于局部比对的算法：Smith-Waterman算法"><a href="#3-1-2-基于局部比对的算法：Smith-Waterman算法" class="headerlink" title="3.1.2. 基于局部比对的算法：Smith-Waterman算法"></a>3.1.2. 基于局部比对的算法：Smith-Waterman算法</h3><ol>
+<li><p><strong>局部比对</strong><br> 与全局比对不同，局部比对不必对两个完整的序列进行比对，而是在每个序列中使用某些局部区域片段进行比对。其产生的需求在于、人们发现有的蛋白序列虽然在序列整体上表现出较大的差异性，但是在某些局部区域能独立的发挥相同的功能，序列相当保守。这时候依靠全局比对明显不能得到这些局部相似序列的。其次，在真核生物的基因中，内含子片段表现出了极大变异性，外显子区域却较为保守，这时候全局比对表现出了其局限性，无法找出这些局部相似性序列。其代表是Smith-Waterman局部比对算法。BLAST是局部比对的一种推广。</p>
+</li>
+<li><p>Smith-Waterman算法特点</p>
+</li>
+</ol>
+<ul>
+<li>是一种用来寻找并比较具有局部相似性区域的动态规划算法，适用于亲缘关系较远，整体上不具有相似性而在一些较小区域存在局部相似性的两条序列。</li>
+<li>使用迭代方法计算出每个序列的相似分值，保存在一个得分矩阵M中，然后根据这个得分矩阵，通过动态规划的方法回溯找到最优的比对序列。</li>
+</ul>
+<h2 id="3-2-启发式搜索算法-heuristic-algorithm-：BWT算法和BLAST算法"><a href="#3-2-启发式搜索算法-heuristic-algorithm-：BWT算法和BLAST算法" class="headerlink" title="3.2. 启发式搜索算法(heuristic algorithm)：BWT算法和BLAST算法"></a>3.2. 启发式搜索算法(heuristic algorithm)：BWT算法和BLAST算法</h2><p>一个基于直观或经验构造的算法，在可接受的花费下给出待解决组合优化问题每一个实例的一个可行解，该可行解与最优解的偏离程度一般不能被预计；目前绝大多数应用的比对软件都是启发式算法。</p>
 <ul>
 <li>渐进式算法(progressive methods): Clustal W,MUSCLE,T-Coffee,DiAlign</li>
 <li>迭代算法(iterative methods): MUSCLE,PRRP算法</li>
@@ -1810,60 +1534,279 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用MAFFT在现有多序列比对中添加新序列</title>
-    <url>/2023/11/16/bioinfo_alignment_align_mafft_add/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=537596136&auto=1&height=32"></iframe></div>
+    <title>批量计算Ka和Ks</title>
+    <url>/2022/09/07/bioinfo_Ks_batch.calculation.Ks/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1901371647&auto=1&height=32"></iframe></div>
+
+<h1 id="1-ParaAT"><a href="#1-ParaAT" class="headerlink" title="1. ParaAT"></a>1. ParaAT</h1><p>参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></p>
+<h1 id="2-KaKs-Calculator-2-0"><a href="#2-KaKs-Calculator-2-0" class="headerlink" title="2. KaKs_Calculator 2.0"></a>2. KaKs_Calculator 2.0</h1><p>KaKs_Calculator 2.0工具包包含了17种计算Ka和Ks的方法，包含Gamma系列，并可识别基于蛋白编码序列的滑动窗口，基于C++和Java，在Windows和Linux平台都可使用。</p>
+<h1 id="3-Ka和Ks计算"><a href="#3-Ka和Ks计算" class="headerlink" title="3. Ka和Ks计算"></a>3. Ka和Ks计算</h1><ul>
+<li>ParaAT比对指定的基因对的氨基酸序列，并转化成比对的CDS序列，并可指定输出格式，如axt格式。</li>
+<li>KaKs_Calculator用于计算比对好的基因对的kaks。</li>
+</ul>
+<p>用还会用到两个脚本:</p>
+<ul>
+<li>axt2one-line.py:<a href="https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py%E8%BD%AC%E6%8D%A2axt%E6%A0%BC%E5%BC%8F%E4%B8%BA%E5%8D%95%E8%A1%8C">https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py转换axt格式为单行</a></li>
+<li>calculate_4DTV_correction.pl:<a href="https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl%E8%AE%A1%E7%AE%974dtv%E3%80%82">https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl计算4dtv。</a></li>
+</ul>
+<h1 id="4-软件准备"><a href="#4-软件准备" class="headerlink" title="4. 软件准备"></a>4. 软件准备</h1><ol>
+<li>安装ParaAT.pl</li>
+</ol>
+<ul>
+<li>参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></li>
+</ul>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget ftp://download.big.ac.cn/bigd/tools/ParaAT2.0.tar.gz</span><br><span class="line">tar -zxf ParaAT2.0.tar.gz</span><br><span class="line">cd ParaAT2.0</span><br><span class="line">ParaAT.pl -h</span><br></pre></td></tr></table></figure>
 
+<ol start="2">
+<li>安装KaKs_Calculator2.0<br>KaKs_Calculator2.0下载地址：<a href="https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect">https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect</a></li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://altushost-swe.dl.sourceforge.net/project/kakscalculator2/KaKs_Calculator2.0.tar.gz</span><br><span class="line">tar -zxf KaKs_Calculator2.0.tar.gz</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/bin/Linux/KaKs_Calculator</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/src/AXTConvertor</span><br></pre></td></tr></table></figure>
 
-<h1 id="1-MAFFT简介"><a href="#1-MAFFT简介" class="headerlink" title="1. MAFFT简介"></a>1. MAFFT简介</h1><p>MAFFT是基于渐进式比对的算法，用于进行多序列比对(Multiple Sequence Alignment,MSA)。由于比对速度快，常用于组学数据的比对。</p>
-<p>MAFFT网页版有一个功能（–add），即向现有的多序列比对中添加新的序列，这在新增数据时非常有用。</p>
-<h1 id="2-MAFFT的–add功能"><a href="#2-MAFFT的–add功能" class="headerlink" title="2. MAFFT的–add功能"></a>2. MAFFT的–add功能</h1><p>MAFFT的–add功能的网页：<a href="https://mafft.cbrc.jp/alignment/server/add.html">https://mafft.cbrc.jp/alignment/server/add.html</a></p>
-<h2 id="2-1-–add功能的三个模块"><a href="#2-1-–add功能的三个模块" class="headerlink" title="2.1. –add功能的三个模块"></a>2.1. –add功能的三个模块</h2><p>根据新增序列和现有多序列比对(MSA)的相对长度，分别选用–add功能的三个模块：</p>
+<ul>
+<li>然后把<code>./KaKs_Calculator2.0/bin/Linux/</code>和<code>./KaKs_Calculator2.0/src/</code>添加到环境变量即可使用<code>KaKs_Calculator</code>和<code>AXTConvertor</code>命令。</li>
+</ul>
+<h1 id="5-文件准备"><a href="#5-文件准备" class="headerlink" title="5. 文件准备"></a>5. 文件准备</h1><p>ParaAT.pl需要三个输入文件，参考blog.ParaAT：<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/">https://yanzhongsino.github.io/2021/10/29/bioinfo_align_pep2cds/</a></p>
 <ol>
-<li>–add：align full length sequences to an MSA. 新增序列与现有MSA长度相当时，选–add模块。</li>
-<li>–addfragments：align fragment sequences to an MSA. 新增序列短于现有MSA时，选–addfragments模块。</li>
-<li>–addlong：align long sequences to a short MSA. 新增序列长于现有MSA时，选–addlong模块。</li>
+<li>sample.id</li>
 </ol>
-<h1 id="3-MAFFT网页版的–add用法"><a href="#3-MAFFT网页版的–add用法" class="headerlink" title="3. MAFFT网页版的–add用法"></a>3. MAFFT网页版的–add用法</h1><p>在网页<a href="https://mafft.cbrc.jp/alignment/server/add.html%E4%B8%8A%E9%80%89%E6%8B%A9%E6%A8%A1%E5%9D%97%EF%BC%8C%E7%84%B6%E5%90%8E%E4%B8%8A%E4%BC%A0%E7%8E%B0%E6%9C%89MSA%E5%92%8C%E6%96%B0%E5%A2%9E%E5%BA%8F%E5%88%97%EF%BC%8C%E9%80%89%E6%8B%A9%E5%8F%82%E6%95%B0%EF%BC%8C%E5%8D%B3%E5%8F%AF%E8%8E%B7%E5%BE%97%E6%AF%94%E5%AF%B9%E7%BB%93%E6%9E%9C%E3%80%82">https://mafft.cbrc.jp/alignment/server/add.html上选择模块，然后上传现有MSA和新增序列，选择参数，即可获得比对结果。</a></p>
-<h2 id="3-1-基本参数"><a href="#3-1-基本参数" class="headerlink" title="3.1. 基本参数"></a>3.1. 基本参数</h2><ol>
-<li>Allow unusual symbols：是否允许不明确字符。</li>
-<li>UPPERCASE&#x2F;lowercase：输出的比对文件的字母大小写是否与输入文件一致。</li>
-<li>Direction of nucleotide sequences：是否根据第一条序列调整剩余序列的方向。</li>
-<li>Output order：输出比对文件的序列顺序。</li>
-<li>Sequence title：输出比对文件的序列名称是否与输入文件一致。<h2 id="3-2-高级参数"><a href="#3-2-高级参数" class="headerlink" title="3.2. 高级参数"></a>3.2. 高级参数</h2>最常用的是第一个参数，是否要保持输出MSA的长度。</li>
-<li><strong>Keep alignment length</strong>：是否保持输出的比对的长度与输入的现有MSA长度一致，如果选择这个选项，则会把新增序列的insertions删除以保持长度不变。</li>
-<li>Strategy：默认选择Auto</li>
-<li>Progressive methods：包括FFT-NS-2,G-INS-1,L-INS-1三个选项。</li>
-<li>Iterative refinement methods：包括FFT-NS-i,G-INS-i,L-INS-i三个选项。</li>
-<li>对核苷酸&#x2F;氨基酸序列的分数矩阵和gap处理等参数。</li>
+<ul>
+<li>两列，每行对应两条要做成对比对的序列ID；</li>
+<li>任意行，ParaAT可以批量处理多个成对比对。</li>
+<li>一个例子：<code>cat sample.collinearity |grep &quot;species_prefix&quot;|cut -f2,3 &gt;sample.id</code> 用MCScanX的结果文件提取blocks的同源gene对，获得sample.id文件。</li>
+</ul>
+<ol start="2">
+<li>cds.fa</li>
 </ol>
-<h2 id="3-3-结果"><a href="#3-3-结果" class="headerlink" title="3.3. 结果"></a>3.3. 结果</h2><p>提交之后，在网页中可以看到比对结果，并可以下载fasta&#x2F;clustal格式的比对结果，也可以转化成phylip&#x2F;nexus&#x2F;msf&#x2F;gcg等格式。</p>
-<p>还可以点击<strong>Refine dataset</strong>进入编辑界面，对比对结果进行进一步的编辑，比如对指定序列进行反向互补操作，保留或删除指定序列。</p>
-<h1 id="4-MAFFT命令行版的–add用法"><a href="#4-MAFFT命令行版的–add用法" class="headerlink" title="4. MAFFT命令行版的–add用法"></a>4. MAFFT命令行版的–add用法</h1><p>命令行版也可以使用–add功能，具体参数参考：<a href="https://mafft.cbrc.jp/alignment/software/addsequences.html">https://mafft.cbrc.jp/alignment/software/addsequences.html</a></p>
-<h1 id="5-MAFFT的merge功能"><a href="#5-MAFFT的merge功能" class="headerlink" title="5. MAFFT的merge功能"></a>5. MAFFT的merge功能</h1><p>MAFFT还有一个merge模块，网页版：<a href="https://mafft.cbrc.jp/alignment/server/merge.html">https://mafft.cbrc.jp/alignment/server/merge.html</a></p>
-<p>merge模块用于合并两个或更多已比对好的sub MSAs。当假定这些sub MSAs在系统发育关系上是各自独立为一单系时，即系统树上各自不重叠时，可使用merge模块。如果系统发育关系不独立，则推荐使用–add模块。</p>
-<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
-<li><a href="https://mafft.cbrc.jp/alignment/server/">https://mafft.cbrc.jp/alignment/server/</a></li>
+<ul>
+<li>包括所有需要比对的cds序列的文件</li>
+</ul>
+<ol start="3">
+<li>pep.fa</li>
 </ol>
-<hr>
 <ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+<li>包括所有需要比对的蛋白序列的文件</li>
 </ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>alignment</category>
-        <category>align</category>
-      </categories>
-      <tags>
-        <tag>alignment</tag>
-        <tag>Multiple Sequence Alignment</tag>
-        <tag>MSA</tag>
-        <tag>sequence alignment</tag>
-        <tag>MAFFT</tag>
-        <tag>add</tag>
-        <tag>merge</tag>
+<h1 id="6-操作步骤"><a href="#6-操作步骤" class="headerlink" title="6. 操作步骤"></a>6. 操作步骤</h1><h2 id="6-1-用ParaAT获取基因对比对序列"><a href="#6-1-用ParaAT获取基因对比对序列" class="headerlink" title="6.1. 用ParaAT获取基因对比对序列"></a>6.1. 用ParaAT获取基因对比对序列</h2><p>ParaAT比对sample.id指定的基因对的氨基酸序列，并转化成比对的CDS序列，并可指定输出为axt格式。</p>
+<ol>
+<li>命令</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">echo &quot;24&quot; &gt;proc #指定ParaAT.pl使用线程</span><br><span class="line">ParaAT.pl -g -t -h sample.id -n cds.fa -a pep.fa -m mafft -p proc -f axt -o sample.paraat 2&gt; paraat.log &amp; #用ParaAT.pl调用mafft做每对基因的蛋白比对，并把蛋白比对转化成cds比对，输出axt格式。-g移除比对有gap的密码子，-t移除mismatched codons；-o指定生成目录；</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>notes</li>
+</ol>
+<ul>
+<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
+<li>建议加上-g和-t，免得后面计算Ks时报错<code>Error. The size of two sequences in &#39;ctg00816-ctg08844&#39; is not equal。</code></li>
+<li>ParaAT.pl命令中加上-k参数可以在获得axt文件后自动调用KaKs_Calculator计算kaks值，使用MA模型，比YN模型慢很多，推荐不加-k参数，而是手动用KaKs_Calculator的YN模型，生成sample.axt_yn.kaks文件。</li>
+</ul>
+<h2 id="6-2-用KaKs-Calculator计算基因对的Ka、Ks和4dtv值"><a href="#6-2-用KaKs-Calculator计算基因对的Ka、Ks和4dtv值" class="headerlink" title="6.2. 用KaKs_Calculator计算基因对的Ka、Ks和4dtv值"></a>6.2. 用KaKs_Calculator计算基因对的Ka、Ks和4dtv值</h2><p>ParaAT.pl的-k参数只能指定KaKs_Calculator的MA模型计算kaks值，如果需要指定其他的模型，则可以手动运行计算。</p>
+<p>KaKs_Calculator可计算比对好的CDS序列的Ka和Ks。</p>
+<ol>
+<li>计算Ka和Ks</li>
+</ol>
+<ul>
+<li>获得all.kaks文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat # 进入ParaAT.pl生成的文件夹</span><br><span class="line">for i in `ls *axt`;do KaKs_Calculator -i $i -o $&#123;i&#125;_yn.kaks -m YN;done #用YN模型计算每个gene对的KaKs，生成四列数据，gene对，Ka，Ks，Ka/Ks</span><br><span class="line">for i in `ls |grep &quot;_yn.kaks&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&quot;\t&quot;$4&quot;\t&quot;$5&#125;&#x27; $i &gt;&gt;../all.kaks;done #合并kaks到all.kaks文件</span><br></pre></td></tr></table></figure></li>
+</ul>
+<ol start="2">
+<li>计算4dtv</li>
+</ol>
+<ul>
+<li>获得all.4dtv文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat # 进入ParaAT.pl生成的文件夹</span><br><span class="line">for i in `ls *axt`;do axt2one-line.py $i $&#123;i&#125;.one-line;done #多行axt文件转换成单行</span><br><span class="line">ls |grep &quot;axt.one-line&quot;|while read id;do calculate_4DTV_correction.pl $id &gt; $&#123;id%%one-line&#125;4dtv;done #计算4dtv值，生成两列数据，gene对，4dtv</span><br><span class="line">for i in `ls |grep &quot;4dtv&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&#125;&#x27; $i &gt;&gt;../all.4dtv;done #合并4dtv值到all.4dtv</span><br></pre></td></tr></table></figure></li>
+</ul>
+<ol start="3">
+<li>合并和整理结果</li>
+</ol>
+<ul>
+<li>获得all.results文件<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd .. #返回上一级目录</span><br><span class="line">join all.kaks all.4dtv |sed &quot;s/ /\t/g&quot; |awk &#x27;$3 != &quot;NA&quot; &#123;print $0&#125;&#x27; |sed &#x27;1i\genepair\tKa\tKs\tKa/Ks\t4dtv_corrected&#x27; &gt;all.results #以gene对为基准，join合并kaks和4dtv值到一个文件，然后过滤Ks值为NA的无效数据，添加标题行。</span><br><span class="line">rm all.kaks* all.4dtv* #删除中间文件</span><br></pre></td></tr></table></figure></li>
+</ul>
+<h1 id="7-references"><a href="#7-references" class="headerlink" title="7. references"></a>7. references</h1><ol>
+<li>ParaAT paper：<a href="https://www.sciencedirect.com/science/article/pii/S0006291X12003518">https://www.sciencedirect.com/science/article/pii/S0006291X12003518</a></li>
+<li>KaKs_Calculator2.0 github：<a href="https://github.com/kullrich/kakscalculator2">https://github.com/kullrich/kakscalculator2</a></li>
+<li>KaKs_Calculator2.0 paper：<a href="https://www.sciencedirect.com/science/article/pii/S1672022910600083?via=ihub">https://www.sciencedirect.com/science/article/pii/S1672022910600083?via%3Dihub</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>Ks</category>
+      </categories>
+      <tags>
+        <tag>Ks</tag>
+        <tag>Ka</tag>
+        <tag>4dtv</tag>
+        <tag>ParaAT</tag>
+        <tag>KaKs_Calculator</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>香港市花是什么，紫荆花？洋紫荆？羊蹄甲？</title>
+    <url>/2021/03/27/bio_taxon_Bauhinia/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1959134563&auto=1&height=32"></iframe></div>
+
+<p>（全文约2000字）</p>
+<h1 id="1-香港市花"><a href="#1-香港市花" class="headerlink" title="1. 香港市花"></a>1. 香港市花</h1><h2 id="1-1-香港市花的前世今生"><a href="#1-1-香港市花的前世今生" class="headerlink" title="1.1. 香港市花的前世今生"></a>1.1. 香港市花的前世今生</h2><h3 id="1-1-1-前世"><a href="#1-1-1-前世" class="headerlink" title="1.1.1. 前世"></a>1.1.1. 前世</h3><p><strong>香港市花</strong> —— 红花羊蹄甲的故事要从1880年说起，一位来自法国巴黎的传教士在香港偶然发现了这种植物，并以插枝栽种在薄扶林道一带的伯大尼修道院。由于红花羊蹄甲无法产生有繁殖力的后代，所以今天见到的所有红花羊蹄甲都来自这个传教士栽种的0号个体。</p>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Bauhinia_2.jpg/240px-Bauhinia_2.jpg" width=100% title="红花羊蹄甲" alt="红花羊蹄甲" align=center/>
+
+<p><strong><p align="center">Figure 1. 红花羊蹄甲 图片来源：wiki</p></strong></p>
+<p>18年后，香港的植物及林务部主管Stephen Troyte Dunn第一次对红花羊蹄甲进行科学的描述，将其定为羊蹄甲属的新物种，并发表在科学期刊《Journal of Botany》上。</p>
+<p>Dunn用前任香港英国总督的名字给这个新种命名为“<strong>Sir Henry and Lady Blake</strong>”，用这种方式感谢夫妇俩对香港植物园的支持。历史上类似的故事很多，伽利略发现的木卫也是通过命名致敬了他的赞助人科西莫二世Medici。科学家们这么做可以获得持续的资助，促进科学的长足发展。</p>
+<h3 id="1-1-2-今生"><a href="#1-1-2-今生" class="headerlink" title="1.1.2. 今生"></a>1.1.2. 今生</h3><p>由于红花羊蹄甲花色鲜艳，花期几乎持续全年，扦插成活率高，很快就成了遍布香港的行道树。在1997年，正式把红花羊蹄甲定为香港市花，出现在区旗、区徽和硬币上，成为香港的标志。也就是我们耳熟能详的<strong>洋紫荆</strong>，或者避免使用<strong>洋</strong>而叫<strong>紫荆花</strong>。</p>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5b/Flag_of_Hong_Kong.svg/240px-Flag_of_Hong_Kong.svg.png" width=90% title="香港区旗" alt="香港区旗" align=center/>
+
+<p><strong><p align="center">Figure 2. 香港区旗 图片来源：wiki</p></strong></p>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Regional_Emblem_of_Hong_Kong.svg/240px-Regional_Emblem_of_Hong_Kong.svg.png" width=80% title="香港区徽" alt="香港区徽" align=center/>
+
+<p><strong><p align="center">Figure 3. 香港区徽 图片来源：wiki</p></strong></p>
+<h2 id="1-2-科学性"><a href="#1-2-科学性" class="headerlink" title="1.2. 科学性"></a>1.2. 科学性</h2><p>但是，从植物科学的角度，紫荆指的是豆科的另一个属紫荆属的植物<em>Cercis chinensis</em>；香港市花却是羊蹄甲属的植物。</p>
+<p>真正的紫荆长这样</p>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/1/18/CercisChinensisShrub2.jpg/440px-CercisChinensisShrub2.jpg" width=80% title="紫荆" alt="紫荆" align=center/>
+
+<p><strong><p align="center">Figure 4. 紫荆 图片来源：wiki</p></strong></p>
+<p>红花羊蹄甲的学名是<em>Bauhinia purpurea</em> x <em>variegata</em> ‘<em>Blakeana</em>‘, cv. nov.，从学名中的x我们就可以看出这个种是羊蹄甲（<em>B. purpurea</em>）和洋紫荆（<em>B. variegata</em>）杂交形成的。</p>
+<p>下面我们就来介绍一下红花羊蹄甲及两个亲本，以及怎么区分这三个种。</p>
+<h1 id="2-羊蹄甲属"><a href="#2-羊蹄甲属" class="headerlink" title="2. 羊蹄甲属"></a>2. 羊蹄甲属</h1><p>羊蹄甲属（<em>Bauhinia</em>）的羊蹄甲（<em>B. purpurea</em>）、洋紫荆（<em>B. variegata</em>）和红花羊蹄甲（<em>B.</em> x <em>blakeana</em>）是华南地区三种园林植物，常植为行道树，在广州尤为普遍，常常能够同时看见这三种植物。</p>
+<p>但由于这三个种是关系很近的一家人，所以常常被人混作一种。接下来我们就来看看到底怎么区分这三个种。</p>
+<p>接下来，干得不能再干的知识点来啦！！！</p>
+<h2 id="2-1-羊蹄甲属常见三种类群的分类特征"><a href="#2-1-羊蹄甲属常见三种类群的分类特征" class="headerlink" title="2.1. 羊蹄甲属常见三种类群的分类特征"></a>2.1. 羊蹄甲属常见三种类群的分类特征</h2><table>
+<thead>
+<tr>
+<th>羊蹄甲属（<em>Bauhinia</em>）学名</th>
+<th><em>B. purpurea</em></th>
+<th><em>B. variegata</em></th>
+<th><em>B. variegata</em> var. <em>candida</em></th>
+<th><em>Bauhinia purpurea</em> x <em>variegata</em> ‘<em>Blakeana</em>‘, cv. nov.</th>
+</tr>
+</thead>
+<tbody><tr>
+<td>中文正名&#x2F;中国大陆常用俗名</td>
+<td>羊蹄甲</td>
+<td>洋紫荆</td>
+<td>白花洋紫荆</td>
+<td>红花羊蹄甲</td>
+</tr>
+<tr>
+<td>香港常用俗名</td>
+<td>红花羊蹄甲</td>
+<td>宫粉羊蹄甲</td>
+<td>白花羊蹄甲</td>
+<td>洋紫荆</td>
+</tr>
+<tr>
+<td>台湾常用俗名</td>
+<td>洋紫荆</td>
+<td>羊蹄甲</td>
+<td>–</td>
+<td>艶&#x2F;豔&#x2F;艳（yan4）紫荆</td>
+</tr>
+<tr>
+<td>主要区别</td>
+<td>能育雄蕊3，退化雄蕊5-6，花瓣较狭窄，具长柄</td>
+<td>能育雄蕊5，无退化雄蕊，落叶后盛花，花色粉红，总状花序极短缩，正常结果</td>
+<td>洋紫荆的白花变种，花白色</td>
+<td>雄蕊5，花叶同期，花色紫红，总状花序开展，不结果</td>
+</tr>
+<tr>
+<td>雄蕊</td>
+<td>能育雄蕊3，退化雄蕊5-6</td>
+<td>能育雄蕊5(7)，无退化雄蕊</td>
+<td>同洋紫荆</td>
+<td>雄蕊5(7)，退化雄蕊2-5</td>
+</tr>
+<tr>
+<td>花期</td>
+<td>花期9-11月</td>
+<td>花期1-4月（3月盛花期）</td>
+<td>同洋紫荆</td>
+<td>花期全年（3-4月盛花期）</td>
+</tr>
+<tr>
+<td>花色</td>
+<td>花色桃红，中国发现一棵白花</td>
+<td>花色粉红到淡紫，有白花、黄花变种</td>
+<td>花白色</td>
+<td>花色紫红</td>
+</tr>
+<tr>
+<td>花着生</td>
+<td>集中顶生</td>
+<td>分散腋生</td>
+<td>同洋紫荆</td>
+<td>集中顶生</td>
+</tr>
+<tr>
+<td>花瓣形态</td>
+<td>窄而相互分离</td>
+<td>宽而相互重叠</td>
+<td>同洋紫荆</td>
+<td>稍宽而分离</td>
+</tr>
+<tr>
+<td>物候</td>
+<td></td>
+<td>落叶后盛花</td>
+<td>同洋紫荆</td>
+<td>花叶同期</td>
+</tr>
+<tr>
+<td>其他</td>
+<td>树皮、花和根可药用外用，根皮剧毒</td>
+<td>花芽、嫩叶和幼果可食</td>
+<td>同洋紫荆，云南有白花变种的花瓣食用史</td>
+<td>香港市花，红花羊蹄甲是羊蹄甲和洋紫荆杂交起源的杂交种，不结果。</td>
+</tr>
+</tbody></table>
+<p>由于这三个物种在两岸三地都有栽培，如果同时和台湾人香港人讨论，一定很有趣。</p>
+<ul>
+<li>羊蹄甲&#x3D;台湾的洋紫荆</li>
+<li>洋紫荆&#x3D;台湾的羊蹄甲</li>
+<li>红花羊蹄甲&#x3D;香港的洋紫荆</li>
+<li>台湾的洋紫荆&#x3D;香港的红花羊蹄甲</li>
+</ul>
+<p>你<del>记住</del>记晕了吗？</p>
+<h2 id="2-2-简单来说，三个物种可以这样来分"><a href="#2-2-简单来说，三个物种可以这样来分" class="headerlink" title="2.2. 简单来说，三个物种可以这样来分"></a>2.2. 简单来说，三个物种可以这样来分</h2><ol>
+<li>如果看到全树的粉花，几乎没有叶子，花瓣有点宽，那就是洋紫荆（<em>B. variegata</em>），开白花的大概率也是这个物种的白花变种——白花洋紫荆。</li>
+</ol>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/23/Bauhinia_variegata_flower.jpg/440px-Bauhinia_variegata_flower.jpg" width=100% title="洋紫荆" alt="洋紫荆" align=center/>
+
+<p><strong><p align="center">Figure 5. 洋紫荆 图片来源：wiki</p></strong></p>
+<ol start="2">
+<li>如果看到花色紫红，花瓣窄窄的，花和叶同时在树上，不结果，全年持续开花的，大概率是红花羊蹄甲（<em>B.</em> x <em>blakeana</em>）。</li>
+</ol>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Bauhinia_blakeana_%28Key_West%29.jpg/440px-Bauhinia_blakeana_%28Key_West%29.jpg" width=100% title="红花羊蹄甲" alt="红花羊蹄甲" align=center/>
+
+<p><strong><p align="center">Figure 6. 红花羊蹄甲 图片来源：wiki</p></strong></p>
+<ol start="3">
+<li>如果在别的羊蹄甲都在开花的时候结了一树果，像挂了一树豆角一样，就是羊蹄甲（<em>B. purpurea</em>）。</li>
+</ol>
+<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Phanera_purpurea_tree_with_fruits_%28Philippines%29.jpg/440px-Phanera_purpurea_tree_with_fruits_%28Philippines%29.jpg" width=100% title="羊蹄甲" alt="羊蹄甲" align=center/>
+
+<p><strong><p align="center">Figure 7. 羊蹄甲 图片来源：wiki</p></strong></p>
+<h2 id="2-3-课后习题"><a href="#2-3-课后习题" class="headerlink" title="2.3. 课后习题"></a>2.3. 课后习题</h2><p>紫荆，洋紫荆，羊蹄甲，红花羊蹄甲这四个里面到底哪个是香港市花？</p>
+<h1 id="3-reference"><a href="#3-reference" class="headerlink" title="3. reference"></a>3. reference</h1><ol>
+<li>wiki: Bauhinia：<a href="https://zh.wikipedia.org/wiki/%E7%BE%8A%E8%B9%84%E7%94%B2%E5%B1%9E#%E5%90%8D%E7%A8%B1%E6%B7%B7%E6%B7%86">https://zh.wikipedia.org/wiki/%E7%BE%8A%E8%B9%84%E7%94%B2%E5%B1%9E#%E5%90%8D%E7%A8%B1%E6%B7%B7%E6%B7%86</a></li>
+<li>paper: Hybrid origin of Bauhinia blakeana：<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.3732/ajb.92.3.525">https://bsapubs.onlinelibrary.wiley.com/doi/full/10.3732/ajb.92.3.525</a></li>
+<li>Bauhinia purpuprea: 羊蹄甲：<a href="http://www.iplant.cn/info/Bauhinia%20purpurea?t=z">http://www.iplant.cn/info/Bauhinia%20purpurea?t=z</a></li>
+<li>Bauhinia variegata： 洋紫荆：<a href="http://www.iplant.cn/info/Bauhinia%20variegata?t=z">http://www.iplant.cn/info/Bauhinia%20variegata?t=z</a></li>
+<li>Bauhinia × blakeana: 红花羊蹄甲：<a href="http://www.iplant.cn/info/Bauhinia%20%C3%97%20blakeana">http://www.iplant.cn/info/Bauhinia%20%C3%97%20blakeana</a></li>
+<li>《羊蹄甲属植物资源培育与利用》魏丹著 2021年12月</li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bio</category>
+        <category>taxon</category>
+      </categories>
+      <tags>
+        <tag>Bauhinia</tag>
       </tags>
   </entry>
   <entry>
@@ -1962,33 +1905,40 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用miniprot做蛋白序列和基因组序列间的比对</title>
-    <url>/2024/09/30/bioinfo_alignment_prot_miniprot/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=397952&auto=1&height=32"></iframe></div>
+    <title>用MAFFT在现有多序列比对中添加新序列</title>
+    <url>/2023/11/16/bioinfo_alignment_align_mafft_add/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=537596136&auto=1&height=32"></iframe></div>
 
-<h1 id="1-miniprot介绍"><a href="#1-miniprot介绍" class="headerlink" title="1. miniprot介绍"></a>1. miniprot介绍</h1><p>需要进行蛋白序列与基因组序列的比对，可以用李恒团队开发的miniprot。</p>
-<h1 id="2-miniprot安装"><a href="#2-miniprot安装" class="headerlink" title="2. miniprot安装"></a>2. miniprot安装</h1><ol>
-<li>conda安装：<code>conda install miniprot</code></li>
-<li>下载安装：仅依赖zlib</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/lh3/miniprot</span><br><span class="line">cd miniprot &amp;&amp; make</span><br></pre></td></tr></table></figure>
 
-<h1 id="3-miniprot使用"><a href="#3-miniprot使用" class="headerlink" title="3. miniprot使用"></a>3. miniprot使用</h1><p>基因组genome.fna和蛋白序列protein.faa进行比对</p>
-<h2 id="3-1-先建索引，再比对【推荐】"><a href="#3-1-先建索引，再比对【推荐】" class="headerlink" title="3.1. 先建索引，再比对【推荐】"></a>3.1. 先建索引，再比对【推荐】</h2><ol>
-<li>建立索引：会将基因组genome.fna翻译成氨基酸序列，费时。</li>
-</ol>
-<ul>
-<li><code>miniprot -d genome.mpi genome.fna</code></li>
-</ul>
-<ol start="2">
-<li>比对：翻译的基因组氨基酸序列与指定的蛋白序列进行比对</li>
+<h1 id="1-MAFFT简介"><a href="#1-MAFFT简介" class="headerlink" title="1. MAFFT简介"></a>1. MAFFT简介</h1><p>MAFFT是基于渐进式比对的算法，用于进行多序列比对(Multiple Sequence Alignment,MSA)。由于比对速度快，常用于组学数据的比对。</p>
+<p>MAFFT网页版有一个功能（–add），即向现有的多序列比对中添加新的序列，这在新增数据时非常有用。</p>
+<h1 id="2-MAFFT的–add功能"><a href="#2-MAFFT的–add功能" class="headerlink" title="2. MAFFT的–add功能"></a>2. MAFFT的–add功能</h1><p>MAFFT的–add功能的网页：<a href="https://mafft.cbrc.jp/alignment/server/add.html">https://mafft.cbrc.jp/alignment/server/add.html</a></p>
+<h2 id="2-1-–add功能的三个模块"><a href="#2-1-–add功能的三个模块" class="headerlink" title="2.1. –add功能的三个模块"></a>2.1. –add功能的三个模块</h2><p>根据新增序列和现有多序列比对(MSA)的相对长度，分别选用–add功能的三个模块：</p>
+<ol>
+<li>–add：align full length sequences to an MSA. 新增序列与现有MSA长度相当时，选–add模块。</li>
+<li>–addfragments：align fragment sequences to an MSA. 新增序列短于现有MSA时，选–addfragments模块。</li>
+<li>–addlong：align long sequences to a short MSA. 新增序列长于现有MSA时，选–addlong模块。</li>
 </ol>
-<p>-<code>miniprot --gff -It16 --gff genome.mpi protein.faa &gt; aln.gff</code></p>
-<h2 id="3-2-建索引和比对一步完成"><a href="#3-2-建索引和比对一步完成" class="headerlink" title="3.2. 建索引和比对一步完成"></a>3.2. 建索引和比对一步完成</h2><ul>
-<li><code>miniprot -Iut16 --gff genome.fna protein.faa &gt; aln.gff</code></li>
-</ul>
-<h1 id="references"><a href="#references" class="headerlink" title="references"></a>references</h1><ol>
-<li>github：<a href="https://github.com/lh3/miniprot">https://github.com/lh3/miniprot</a></li>
+<h1 id="3-MAFFT网页版的–add用法"><a href="#3-MAFFT网页版的–add用法" class="headerlink" title="3. MAFFT网页版的–add用法"></a>3. MAFFT网页版的–add用法</h1><p>在网页<a href="https://mafft.cbrc.jp/alignment/server/add.html%E4%B8%8A%E9%80%89%E6%8B%A9%E6%A8%A1%E5%9D%97%EF%BC%8C%E7%84%B6%E5%90%8E%E4%B8%8A%E4%BC%A0%E7%8E%B0%E6%9C%89MSA%E5%92%8C%E6%96%B0%E5%A2%9E%E5%BA%8F%E5%88%97%EF%BC%8C%E9%80%89%E6%8B%A9%E5%8F%82%E6%95%B0%EF%BC%8C%E5%8D%B3%E5%8F%AF%E8%8E%B7%E5%BE%97%E6%AF%94%E5%AF%B9%E7%BB%93%E6%9E%9C%E3%80%82">https://mafft.cbrc.jp/alignment/server/add.html上选择模块，然后上传现有MSA和新增序列，选择参数，即可获得比对结果。</a></p>
+<h2 id="3-1-基本参数"><a href="#3-1-基本参数" class="headerlink" title="3.1. 基本参数"></a>3.1. 基本参数</h2><ol>
+<li>Allow unusual symbols：是否允许不明确字符。</li>
+<li>UPPERCASE&#x2F;lowercase：输出的比对文件的字母大小写是否与输入文件一致。</li>
+<li>Direction of nucleotide sequences：是否根据第一条序列调整剩余序列的方向。</li>
+<li>Output order：输出比对文件的序列顺序。</li>
+<li>Sequence title：输出比对文件的序列名称是否与输入文件一致。<h2 id="3-2-高级参数"><a href="#3-2-高级参数" class="headerlink" title="3.2. 高级参数"></a>3.2. 高级参数</h2>最常用的是第一个参数，是否要保持输出MSA的长度。</li>
+<li><strong>Keep alignment length</strong>：是否保持输出的比对的长度与输入的现有MSA长度一致，如果选择这个选项，则会把新增序列的insertions删除以保持长度不变。</li>
+<li>Strategy：默认选择Auto</li>
+<li>Progressive methods：包括FFT-NS-2,G-INS-1,L-INS-1三个选项。</li>
+<li>Iterative refinement methods：包括FFT-NS-i,G-INS-i,L-INS-i三个选项。</li>
+<li>对核苷酸&#x2F;氨基酸序列的分数矩阵和gap处理等参数。</li>
+</ol>
+<h2 id="3-3-结果"><a href="#3-3-结果" class="headerlink" title="3.3. 结果"></a>3.3. 结果</h2><p>提交之后，在网页中可以看到比对结果，并可以下载fasta&#x2F;clustal格式的比对结果，也可以转化成phylip&#x2F;nexus&#x2F;msf&#x2F;gcg等格式。</p>
+<p>还可以点击<strong>Refine dataset</strong>进入编辑界面，对比对结果进行进一步的编辑，比如对指定序列进行反向互补操作，保留或删除指定序列。</p>
+<h1 id="4-MAFFT命令行版的–add用法"><a href="#4-MAFFT命令行版的–add用法" class="headerlink" title="4. MAFFT命令行版的–add用法"></a>4. MAFFT命令行版的–add用法</h1><p>命令行版也可以使用–add功能，具体参数参考：<a href="https://mafft.cbrc.jp/alignment/software/addsequences.html">https://mafft.cbrc.jp/alignment/software/addsequences.html</a></p>
+<h1 id="5-MAFFT的merge功能"><a href="#5-MAFFT的merge功能" class="headerlink" title="5. MAFFT的merge功能"></a>5. MAFFT的merge功能</h1><p>MAFFT还有一个merge模块，网页版：<a href="https://mafft.cbrc.jp/alignment/server/merge.html">https://mafft.cbrc.jp/alignment/server/merge.html</a></p>
+<p>merge模块用于合并两个或更多已比对好的sub MSAs。当假定这些sub MSAs在系统发育关系上是各自独立为一单系时，即系统树上各自不重叠时，可使用merge模块。如果系统发育关系不独立，则推荐使用–add模块。</p>
+<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
+<li><a href="https://mafft.cbrc.jp/alignment/server/">https://mafft.cbrc.jp/alignment/server/</a></li>
 </ol>
 <hr>
 <ul>
@@ -1999,11 +1949,16 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       <categories>
         <category>bioinfo</category>
         <category>alignment</category>
-        <category>prot</category>
+        <category>align</category>
       </categories>
       <tags>
-        <tag>miniprot</tag>
-        <tag>align</tag>
+        <tag>alignment</tag>
+        <tag>Multiple Sequence Alignment</tag>
+        <tag>MSA</tag>
+        <tag>sequence alignment</tag>
+        <tag>MAFFT</tag>
+        <tag>add</tag>
+        <tag>merge</tag>
       </tags>
   </entry>
   <entry>
@@ -2230,6 +2185,65 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>Gblocks</tag>
       </tags>
   </entry>
+  <entry>
+    <title>用MEGA统计序列比对的成对突变频谱</title>
+    <url>/2023/11/11/bioinfo_alignment_statistics_nucleotide.pair.frequencies.with.MEGA/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5056815&auto=1&height=32"></iframe></div>
+
+<h1 id="1-序列比对和MEGA的简单介绍"><a href="#1-序列比对和MEGA的简单介绍" class="headerlink" title="1. 序列比对和MEGA的简单介绍"></a>1. 序列比对和MEGA的简单介绍</h1><ul>
+<li>序列比对的知识可以参考另一篇博文的内容。Sequence Alignment：<a href="https://yanzhongsino.github.io/2021/09/06/bioinfo_alignment_align_intro/">https://yanzhongsino.github.io/2021/09/06/bioinfo_alignment_align_intro/</a></li>
+<li>MEGA软件的全称是Molecular Evolutionary Genetics Analysis，是用于对序列进行操作、比对、统计、系统发育和进化分析的一个软件包，拥有非常多的功能。</li>
+<li>MEGA官方网站是：<a href="https://www.megasoftware.net/%EF%BC%8C%E7%8E%B0%E5%9C%A8%E6%9C%80%E6%96%B0%E7%89%88%E6%9C%AC%E6%98%AFMEGA">https://www.megasoftware.net/，现在最新版本是MEGA</a> 11。</li>
+</ul>
+<h1 id="2-成对突变频谱"><a href="#2-成对突变频谱" class="headerlink" title="2. 成对突变频谱"></a>2. 成对突变频谱</h1><p>突变频谱是指序列间突变的类型和总数的统计。</p>
+<ol>
+<li>两条比对好的序列的碱基是一一对应的，序列的差异被认为是点突变的证据。如果考虑突变方向，点突变共有12种类型；如果不考虑突变方向，点突变共有6种类型。</li>
+<li>在两条序列间，对每种类型的点突变的总数进行统计，还可以根据突变类型计算Ti，Tv以及Ti&#x2F;Tv ratio (Ts&#x2F;Tv ratio)。</li>
+<li>在多序列比对中，也可以计算两两的成对序列的点突变的类型和数量，即成对突变频谱。</li>
+</ol>
+<h1 id="3-用MEGA统计成对突变频率"><a href="#3-用MEGA统计成对突变频率" class="headerlink" title="3. 用MEGA统计成对突变频率"></a>3. 用MEGA统计成对突变频率</h1><h2 id="3-1-准备文件"><a href="#3-1-准备文件" class="headerlink" title="3.1. 准备文件"></a>3.1. 准备文件</h2><ol>
+<li>需要的文件是已经比对好的多序列或两条序列的文件，fasta格式或meg格式都可用。<h2 id="3-2-统计的操作"><a href="#3-2-统计的操作" class="headerlink" title="3.2. 统计的操作"></a>3.2. 统计的操作</h2><h3 id="3-2-1-在MEGA导入多序列比对文件"><a href="#3-2-1-在MEGA导入多序列比对文件" class="headerlink" title="3.2.1. 在MEGA导入多序列比对文件"></a>3.2.1. 在MEGA导入多序列比对文件</h3></li>
+<li>在MEGA中打开fasta或meg格式的比对文件；</li>
+<li>弹出对话框【How would you like to open this fasta file?】，选【Analyze】；</li>
+<li>弹出对话框【Input Data Options】，选对应的数据类型，DNA序列选【Nucleotide Sequences】；</li>
+<li>弹出对话框【Confirmation-Protein-coding nucleotide sequence data】，如果是蛋白编码序列coding sequences就选【Yes】，否则选【No】；<h3 id="3-2-2-选择需要统计的序列"><a href="#3-2-2-选择需要统计的序列" class="headerlink" title="3.2.2. 选择需要统计的序列"></a>3.2.2. 选择需要统计的序列</h3></li>
+<li>此时打开了比对文件，点击MEGA界面操作栏下面的第一个小方块（显示TA，右边第二个小方块是Close Data），打开界面【Sequence Data Explorer】；此界面可以对导入的比对序列进行统计分析。</li>
+<li>界面【Sequence Data Explorer】中包含了导入的多序列比对文件的序列信息，默认选择所有序列（所有序列前的勾是勾上的），可以根据需要选择至少两条序列进行突变频谱的统计。如果选择了三条及以上序列，则突变频谱的统计结果是两两序列突变数量的平均值。</li>
+<li>在界面【Sequence Data Explorer】选择【Statistics】-【Nucleotide Pair Frequencies】-【Directional (16 Pairs)..】或【Undirectional (10 Pairs)..】。</li>
+</ol>
+<ul>
+<li>【Directional】代表进行有方向的突变频谱的统计，界面【Sequence Data Explorer】中勾选的序列的顺序决定了方向，代表从前面的序列到后面的序列的突变频谱。可以在选择序列时更改序列的顺序，来计算相应方向的突变频谱。</li>
+<li>【Undirectional (10 Pairs)..】则代表进行无方向的突变频谱统计，与有方向的类似，但把A-T和T-A的突变合并，依此类推。</li>
+<li>有方向的突变频谱包含12种突变类型，无方向的突变频谱包含6种突变类型，这里的统计还包含A-A,T-T,C-C,G-G四种无突变类型的数量，所以分别是16 pairs和10 pairs。</li>
+</ul>
+<ol start="4">
+<li>弹出对话框【Select Output Format】，根据需要选择输出文件格式（XLSX,XLS,TXT,CSV,ODS），以及直接展示结果（Display Results）还是保存结果到文件（Save to Disk）。</li>
+</ol>
+<h2 id="3-3-结果文件"><a href="#3-3-结果文件" class="headerlink" title="3.3. 结果文件"></a>3.3. 结果文件</h2><p>TXT格式的结果文件示例，这里选择了Liriodendron, Amborella, Nymphaea三条序列进行统计。</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Data Filename: samples.fa</span><br><span class="line">Data Title: fasta file</span><br><span class="line">Nucleotide Pair Frequencies</span><br><span class="line">Sites Used: All selected</span><br><span class="line">No. of sequences used: 3</span><br><span class="line">Sequences used</span><br><span class="line">  Liriodendron</span><br><span class="line">  Amborella</span><br><span class="line">  Nymphaea</span><br><span class="line">All frequencies are averages (rounded) over all taxa.</span><br><span class="line">ii = Identical Pairs</span><br><span class="line">si = Transitionsal Pairs</span><br><span class="line">sv = Transversional Pairs</span><br><span class="line">R  = si/sv</span><br><span class="line">Domain       ii    si    sv    R     TT    TC    TA    TG    CT    CC    CA    CG    AT    AC    AA    AG    GT    GC    GA    GG Total  Domain Info</span><br><span class="line">   1. Avg 23254   276   573   0.5  7099    62    67    82    86  4567   150    24    62    87  6299    65    80    21    63  5289 24102.3  Data</span><br></pre></td></tr></table></figure>
+
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>alignment</category>
+        <category>statistics</category>
+      </categories>
+      <tags>
+        <tag>Multiple Sequence Alignment</tag>
+        <tag>MSA</tag>
+        <tag>sequence alignment</tag>
+        <tag>Molecular Evolutionary Genetics Analysis</tag>
+        <tag>MEGA</tag>
+        <tag>nucleotide pair frequencies</tag>
+        <tag>Ti</tag>
+        <tag>Tv</tag>
+      </tags>
+  </entry>
   <entry>
     <title>富集分析：（三）clusterProfiler概述</title>
     <url>/2021/12/13/bioinfo_enrichment_clusterProfiler.intro/</url>
@@ -2492,65 +2506,6 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>clusterProfiler</tag>
       </tags>
   </entry>
-  <entry>
-    <title>用MEGA统计序列比对的成对突变频谱</title>
-    <url>/2023/11/11/bioinfo_alignment_statistics_nucleotide.pair.frequencies.with.MEGA/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5056815&auto=1&height=32"></iframe></div>
-
-<h1 id="1-序列比对和MEGA的简单介绍"><a href="#1-序列比对和MEGA的简单介绍" class="headerlink" title="1. 序列比对和MEGA的简单介绍"></a>1. 序列比对和MEGA的简单介绍</h1><ul>
-<li>序列比对的知识可以参考另一篇博文的内容。Sequence Alignment：<a href="https://yanzhongsino.github.io/2021/09/06/bioinfo_alignment_align_intro/">https://yanzhongsino.github.io/2021/09/06/bioinfo_alignment_align_intro/</a></li>
-<li>MEGA软件的全称是Molecular Evolutionary Genetics Analysis，是用于对序列进行操作、比对、统计、系统发育和进化分析的一个软件包，拥有非常多的功能。</li>
-<li>MEGA官方网站是：<a href="https://www.megasoftware.net/%EF%BC%8C%E7%8E%B0%E5%9C%A8%E6%9C%80%E6%96%B0%E7%89%88%E6%9C%AC%E6%98%AFMEGA">https://www.megasoftware.net/，现在最新版本是MEGA</a> 11。</li>
-</ul>
-<h1 id="2-成对突变频谱"><a href="#2-成对突变频谱" class="headerlink" title="2. 成对突变频谱"></a>2. 成对突变频谱</h1><p>突变频谱是指序列间突变的类型和总数的统计。</p>
-<ol>
-<li>两条比对好的序列的碱基是一一对应的，序列的差异被认为是点突变的证据。如果考虑突变方向，点突变共有12种类型；如果不考虑突变方向，点突变共有6种类型。</li>
-<li>在两条序列间，对每种类型的点突变的总数进行统计，还可以根据突变类型计算Ti，Tv以及Ti&#x2F;Tv ratio (Ts&#x2F;Tv ratio)。</li>
-<li>在多序列比对中，也可以计算两两的成对序列的点突变的类型和数量，即成对突变频谱。</li>
-</ol>
-<h1 id="3-用MEGA统计成对突变频率"><a href="#3-用MEGA统计成对突变频率" class="headerlink" title="3. 用MEGA统计成对突变频率"></a>3. 用MEGA统计成对突变频率</h1><h2 id="3-1-准备文件"><a href="#3-1-准备文件" class="headerlink" title="3.1. 准备文件"></a>3.1. 准备文件</h2><ol>
-<li>需要的文件是已经比对好的多序列或两条序列的文件，fasta格式或meg格式都可用。<h2 id="3-2-统计的操作"><a href="#3-2-统计的操作" class="headerlink" title="3.2. 统计的操作"></a>3.2. 统计的操作</h2><h3 id="3-2-1-在MEGA导入多序列比对文件"><a href="#3-2-1-在MEGA导入多序列比对文件" class="headerlink" title="3.2.1. 在MEGA导入多序列比对文件"></a>3.2.1. 在MEGA导入多序列比对文件</h3></li>
-<li>在MEGA中打开fasta或meg格式的比对文件；</li>
-<li>弹出对话框【How would you like to open this fasta file?】，选【Analyze】；</li>
-<li>弹出对话框【Input Data Options】，选对应的数据类型，DNA序列选【Nucleotide Sequences】；</li>
-<li>弹出对话框【Confirmation-Protein-coding nucleotide sequence data】，如果是蛋白编码序列coding sequences就选【Yes】，否则选【No】；<h3 id="3-2-2-选择需要统计的序列"><a href="#3-2-2-选择需要统计的序列" class="headerlink" title="3.2.2. 选择需要统计的序列"></a>3.2.2. 选择需要统计的序列</h3></li>
-<li>此时打开了比对文件，点击MEGA界面操作栏下面的第一个小方块（显示TA，右边第二个小方块是Close Data），打开界面【Sequence Data Explorer】；此界面可以对导入的比对序列进行统计分析。</li>
-<li>界面【Sequence Data Explorer】中包含了导入的多序列比对文件的序列信息，默认选择所有序列（所有序列前的勾是勾上的），可以根据需要选择至少两条序列进行突变频谱的统计。如果选择了三条及以上序列，则突变频谱的统计结果是两两序列突变数量的平均值。</li>
-<li>在界面【Sequence Data Explorer】选择【Statistics】-【Nucleotide Pair Frequencies】-【Directional (16 Pairs)..】或【Undirectional (10 Pairs)..】。</li>
-</ol>
-<ul>
-<li>【Directional】代表进行有方向的突变频谱的统计，界面【Sequence Data Explorer】中勾选的序列的顺序决定了方向，代表从前面的序列到后面的序列的突变频谱。可以在选择序列时更改序列的顺序，来计算相应方向的突变频谱。</li>
-<li>【Undirectional (10 Pairs)..】则代表进行无方向的突变频谱统计，与有方向的类似，但把A-T和T-A的突变合并，依此类推。</li>
-<li>有方向的突变频谱包含12种突变类型，无方向的突变频谱包含6种突变类型，这里的统计还包含A-A,T-T,C-C,G-G四种无突变类型的数量，所以分别是16 pairs和10 pairs。</li>
-</ul>
-<ol start="4">
-<li>弹出对话框【Select Output Format】，根据需要选择输出文件格式（XLSX,XLS,TXT,CSV,ODS），以及直接展示结果（Display Results）还是保存结果到文件（Save to Disk）。</li>
-</ol>
-<h2 id="3-3-结果文件"><a href="#3-3-结果文件" class="headerlink" title="3.3. 结果文件"></a>3.3. 结果文件</h2><p>TXT格式的结果文件示例，这里选择了Liriodendron, Amborella, Nymphaea三条序列进行统计。</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Data Filename: samples.fa</span><br><span class="line">Data Title: fasta file</span><br><span class="line">Nucleotide Pair Frequencies</span><br><span class="line">Sites Used: All selected</span><br><span class="line">No. of sequences used: 3</span><br><span class="line">Sequences used</span><br><span class="line">  Liriodendron</span><br><span class="line">  Amborella</span><br><span class="line">  Nymphaea</span><br><span class="line">All frequencies are averages (rounded) over all taxa.</span><br><span class="line">ii = Identical Pairs</span><br><span class="line">si = Transitionsal Pairs</span><br><span class="line">sv = Transversional Pairs</span><br><span class="line">R  = si/sv</span><br><span class="line">Domain       ii    si    sv    R     TT    TC    TA    TG    CT    CC    CA    CG    AT    AC    AA    AG    GT    GC    GA    GG Total  Domain Info</span><br><span class="line">   1. Avg 23254   276   573   0.5  7099    62    67    82    86  4567   150    24    62    87  6299    65    80    21    63  5289 24102.3  Data</span><br></pre></td></tr></table></figure>
-
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>alignment</category>
-        <category>statistics</category>
-      </categories>
-      <tags>
-        <tag>Multiple Sequence Alignment</tag>
-        <tag>MSA</tag>
-        <tag>sequence alignment</tag>
-        <tag>Molecular Evolutionary Genetics Analysis</tag>
-        <tag>MEGA</tag>
-        <tag>nucleotide pair frequencies</tag>
-        <tag>Ti</tag>
-        <tag>Tv</tag>
-      </tags>
-  </entry>
   <entry>
     <title>富集分析：（四）clusterProfiler：不同物种的GO+KEGG富集分析</title>
     <url>/2022/04/26/bioinfo_enrichment_clusterProfiler.species/</url>
@@ -3011,67 +2966,6 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>clusterProfiler</tag>
       </tags>
   </entry>
-  <entry>
-    <title>从codon比对的cds序列中提取四倍简并位点</title>
-    <url>/2021/10/10/bioinfo_extract.4dtv.from.cds/</url>
-    <content><![CDATA[<h1 id="1-从condon比对的cds序列中提取四倍简并位点"><a href="#1-从condon比对的cds序列中提取四倍简并位点" class="headerlink" title="1. 从condon比对的cds序列中提取四倍简并位点"></a>1. 从condon比对的cds序列中提取四倍简并位点</h1><p>输入文件是根据密码子规则（即三个三个对齐）比对好的cds序列，输出文件是以其中一个物种的4dtv为标准提取所有物种的4dtv位点，提取的也是按照比对前的顺序排列，结果保存在4dtv.aln文件中。</p>
-<h2 id="1-1-输入数据和变量定义"><a href="#1-1-输入数据和变量定义" class="headerlink" title="1.1. 输入数据和变量定义"></a>1.1. 输入数据和变量定义</h2><ol>
-<li><code>cds=/path/to/cds.aln</code> # 定义变量cds为codon模式比对好的cds序列</li>
-<li><code>species=Athaliana</code> #以哪个物种的4dtv为标准提取，就定义变量species为那个物种的序列ID</li>
-</ol>
-<h2 id="1-2-三种方案【选择一种】"><a href="#1-2-三种方案【选择一种】" class="headerlink" title="1.2. 三种方案【选择一种】"></a>1.2. 三种方案【选择一种】</h2><h3 id="1-2-1-一步生成"><a href="#1-2-1-一步生成" class="headerlink" title="1.2.1. 一步生成"></a>1.2.1. 一步生成</h3><ol>
-<li>一步生成运行文件，生成从cds获取4dtv的命令，很长，所以储存在文件4dtv.sh中。运行4dtv.sh就可以获取<br><code>seqkit locate -V 0 -i -d -p GCN -p CGN -p GGN -p CTN -p CCN -p TCN -p ACN -p GTN $&#123;cds&#125; |awk -v awka=&quot;$&#123;species&#125;&quot; &#39;&#123;if ($1 == awka &amp;&amp; $6%3== 0) print $6&#125;&#39;|sort -k 1n |uniq |awk -v awkb=&quot;$&#123;cds&#125;&quot; &#39;&#123;print &quot;&lt;(seqkit subseq -r &quot;$1&quot;:&quot;$1&quot; &quot;awkb&quot;)&quot;&#125;&#39; |sed -e &#39;1i\seqkit concat -w 0&#39; -e &#39;$a\&gt; 4dtv.aln&#39;|xargs echo &gt;4dtv.sh</code></li>
-</ol>
-<p>代码含义：用seqkit locate根据4dtv的规则提取位置，然后筛选指定物种的位置，筛选第三位位置是3的整数倍的位置，把第三位位置(即4dtv位点)提取出来，排序去重；再按照seqkit concat要求的格式规则把seqkit concat命令写入提取脚本4dtv.sh中。</p>
-<p>除非位点在1000个以内，否则一步生成运行文件可能会运行失败，推荐用下一种方案。</p>
-<ol start="2">
-<li>运行生成的4dtv.sh，获得4dtv.aln结果<br><code>bash 4dtv.sh</code></li>
-</ol>
-<h3 id="1-2-2-切割分开运行【推荐】"><a href="#1-2-2-切割分开运行【推荐】" class="headerlink" title="1.2.2. 切割分开运行【推荐】"></a>1.2.2. 切割分开运行【推荐】</h3><p>如果需要合并的位点超过1000个，一步生成运行文件占用内存，运行较慢，可以分步骤进行。</p>
-<p>如果因为4dtv.sh太长无法运行，可以把4dtv.temp分成1-n几部分（比如1000行作为一个子文件），然后切割各部分分别生成4dtv_n.sh，之后分别运行再合并。</p>
-<ol>
-<li><p>生成临时文件4dtv.temp<br><code>seqkit locate -V 0 -i -d -p GCN -p CGN -p GGN -p CTN -p CCN -p TCN -p ACN -p GTN $&#123;cds&#125; |awk -v awka=&quot;$&#123;species&#125;&quot; &#39;&#123;if ($1 == awka &amp;&amp; $6%3== 0) print $6&#125;&#39; |sort -k 1n |uniq &gt;4dtv.temp</code></p>
-</li>
-<li><p>分割临时文件4dtv.temp<br><code>split -l 1000 4dtv.temp -d -a 4 4dtv.temp_</code><br>命令含义：分割文件4dtv.temp成多个文件，每个子文件1000行（-l 1000），以数字（-d）而非字母命名子文件，数字长度为4（-a 4，default是2）；会生成4dtv.temp_0000-nnnn的子文件。</p>
-</li>
-</ol>
-<p>可以先看看4dtv.temp的长度，我尝试了下按1000行切割比较合适，再多就达到能同时打开文件数量上限无法运行了。</p>
-<ol start="3">
-<li><p>生成4dtv.temp.n.sh<br><code>for i in $(ls 4dtv.temp_*);do cat $&#123;i&#125; |awk -v awkb=&quot;$&#123;cds&#125;&quot; &#39;&#123;print &quot;&lt;(seqkit subseq -r &quot;$1&quot;:&quot;$1&quot; &quot;awkb&quot;)&quot;&#125;&#39; |sed -e &#39;1i\seqkit concat -w 0&#39; -e &#39;$a\&gt;&#39;|sed &quot;s/^&gt;$/&gt; $&#123;i&#125;.aln/&quot; |sed &#39;s/$/ \\/g&#39; |sed &#39;$ s/.$//&#39; &gt;$&#123;i&#125;.sh; done</code></p>
-</li>
-<li><p>运行生成的4dtv.temp.n.sh，获得4dtv.aln_n结果【耗时步骤】<br><code>for i in $(ls 4dtv.temp_*.sh);do bash $&#123;i&#125;;done</code></p>
-</li>
-</ol>
-<p>建议用nohup放到后台运行<br><code>nohup sh -c &#39;for i in $(ls 4dtv.temp_*.sh);do bash $&#123;i&#125;;done&#39; &amp;</code></p>
-<ol start="5">
-<li>合并子文件，获得最终结果<br><code>seqkit concat ./4dtv.temp_*.aln &gt;4dtv.aln</code></li>
-</ol>
-<p>可以<code>seqkit stats *aln</code>检查是否都是1000长度的序列；以及<code>seqkit stats 4dtv.aln</code>检查是否序列长度与4dtv.temp的行数一致。</p>
-<ol start="6">
-<li>删除中间文件<br><code>rm 4dtv.temp*</code></li>
-</ol>
-<h3 id="1-2-3-分物种运行【不推荐】"><a href="#1-2-3-分物种运行【不推荐】" class="headerlink" title="1.2.3. 分物种运行【不推荐】"></a>1.2.3. 分物种运行【不推荐】</h3><p>一开始没发现seqkit concat这个工具，然后自己写的合并，有时会有aln的问题，不建议用。</p>
-<ol>
-<li>定义数组sps为物种列表<br><code>sps=($(seqkit seq -n $&#123;cds&#125;|xargs))</code></li>
-<li>以第六个物种${sps[5]}的4dtv为标准，为每个物种保存一份4dtv.bed文件<br><code>for i in $(echo $&#123;sps[*]&#125;); do cat $&#123;sps[5]&#125;.4dtv|sed &quot;s/$&#123;species[5]&#125;/$&#123;i&#125;/g&quot; &gt;$&#123;i&#125;.4dtv.bed; done</code></li>
-<li>为每个物种提取4dtv位点，保存到4dtv.aln.temp【耗时步骤】<br><code>for i in $(echo $&#123;sps[*]&#125;); do seqkit subseq --bed $&#123;i&#125;.4dtv.bed $&#123;cds&#125; | grep -v &quot;&gt;&quot;|xargs echo |sed &quot;1i\&gt;$&#123;i&#125;&quot; &gt;$&#123;i&#125;.4dtv.aln.temp; done</code></li>
-<li>合并物种的4dtv位点<br><code>cat *.4dtv.aln.temp &gt;4dtv.aln</code> #合并</li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>4dtv</category>
-      </categories>
-      <tags>
-        <tag>4dtv</tag>
-        <tag>codon</tag>
-      </tags>
-  </entry>
   <entry>
     <title>富集分析：（一）概述</title>
     <url>/2021/11/12/bioinfo_enrichment_intro/</url>
@@ -3546,187 +3440,27 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>GFF3文件处理软件 —— GFF3toolkit工具包的介绍和使用</title>
-    <url>/2022/05/24/bioinfo_fileformat_GFF3toolkit/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283095&auto=1&height=32"></iframe></div>
+    <title>samtools faidx创建fasta/fastq格式文件的.fai格式索引文件</title>
+    <url>/2023/11/24/bioinfo_fileformat_fa_fai/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1394643615&auto=1&height=32"></iframe></div>
 
-<h1 id="1-GFF3toolkit-简介"><a href="#1-GFF3toolkit-简介" class="headerlink" title="1. GFF3toolkit 简介"></a>1. GFF3toolkit 简介</h1><p><a href="https://github.com/NAL-i5K/GFF3toolkit">GFF3toolkit</a>是用于处理GFF3格式文件的一个基于python的工具包，功能包括检测GFF3格式错误，修正GFF3格式错误，合并GFF3格式文件，排序GFF3格式文件，用GFF3格式文件生成序列等。</p>
-<h1 id="2-GFF3toolkit-安装"><a href="#2-GFF3toolkit-安装" class="headerlink" title="2. GFF3toolkit 安装"></a>2. GFF3toolkit 安装</h1><p><code>pip install gff3tool</code></p>
-<h1 id="3-GFF3toolkit-模块"><a href="#3-GFF3toolkit-模块" class="headerlink" title="3. GFF3toolkit 模块"></a>3. GFF3toolkit 模块</h1><p>GFF3toolkit包含许多模块：</p>
-<ul>
-<li>gff3_QC：检测gff3格式错误</li>
-<li>gff3_fix：修正gff3格式错误</li>
-<li>gff3_merge：合并两个gff3文件</li>
-<li>gff3_sort：根据scaffold，coordinates坐标来排序gff3文件</li>
-<li>gff3_to_fasta：根据基因组fasta和注释gff生成gene&#x2F;cds&#x2F;protein&#x2F;exon等序列</li>
+<h1 id="1-为fasta-x2F-fastq格式文件创建索引"><a href="#1-为fasta-x2F-fastq格式文件创建索引" class="headerlink" title="1. 为fasta&#x2F;fastq格式文件创建索引"></a>1. 为fasta&#x2F;fastq格式文件创建索引</h1><ul>
+<li>fasta&#x2F;fastq文件是用于存储生物序列的常用格式，后缀为fasta&#x2F;fas&#x2F;fa&#x2F;fastq&#x2F;fq。</li>
+<li>为fasta&#x2F;fastq创建索引文件，可以快速查找和提取任意位置序列。如使用GATK,IGV等软件时用到。</li>
+<li><code>samtools faidx sample.fa</code>命令即可创建<code>sample.fa.fai</code>索引文件。</li>
+<li><code>samtools faidx</code>命令对fasta文件的格式有要求。即对每条序列内，除最后一行外，其他行长度必须相同，行末尾的换行符也必须一致（Unix-style或Windows-style）。同一个fasta文件内的不同序列间的长度和换行符可以不一致，但同一条序列内部必须一致。</li>
+<li><code>samtools faidx</code>命令对fastq文件的格式的要求是fastq的碱基质量行必须与序列行有着一样的长度。</li>
 </ul>
-<h1 id="4-GFF3toolkit-使用"><a href="#4-GFF3toolkit-使用" class="headerlink" title="4. GFF3toolkit 使用"></a>4. GFF3toolkit 使用</h1><h2 id="4-1-gff3-merge"><a href="#4-1-gff3-merge" class="headerlink" title="4.1. gff3_merge"></a>4.1. gff3_merge</h2><p>用于合并两个gff3注释文件。</p>
-<ol>
-<li><p>命令<br><code>gff3_merge -g1 sample1.gff3 -g2 sample2.gff3 -f genome.fa -og merged.gff3 -r merged.report</code></p>
-</li>
-<li><p>参数</p>
-</li>
-</ol>
-<ul>
-<li>-g1 sample1.gff3：指定待合并的gff3文件1</li>
-<li>-g2 sample2.gff3：指定待合并的gff3文件2</li>
-<li>-f genome.fa：指定基因组文件</li>
-<li>-og merged.gff3：指定合并后结果文件</li>
-<li>-r merged.report：指定合并报告结果文件</li>
-<li>-noAuto：这个参数关闭添加replace tags的自动任务。默认是开启的。</li>
-<li>-a：默认开启添加replace tags的自动任务只应用在没有replace tags的transcript转录本上，-a参数设置为应用在所有转录本上。</li>
-</ul>
-<ol start="3">
-<li>输出</li>
-</ol>
-<ul>
-<li>merged.gff3：合并后的gff文件</li>
-<li>merged.report：合并报告</li>
-</ul>
-<h2 id="4-2-gff3-sort"><a href="#4-2-gff3-sort" class="headerlink" title="4.2. gff3_sort"></a>4.2. gff3_sort</h2><p>用于排序gff3文件。根据scaffold(seqID)，coordinates坐标排序基因，根据特征关系(feature relationship)排序基因内注释。</p>
-<p>排序的前提假设：</p>
-<ul>
-<li>任何没有Parent属性的注释都是父注释。</li>
-<li>所有子注释都排在其父注释之后，并在新的父注释之前。</li>
-</ul>
-<ol>
-<li><p>命令<br><code>gff3_sort -g sample.gff3 -og sorted.gff3</code></p>
-</li>
-<li><p>参数</p>
-</li>
-</ol>
-<ul>
-<li>-g sample.gff3：指定输入gff3文件</li>
-<li>-og sorted.gff3：指定输出排序好的gff3文件</li>
-<li>-t sort_template.txt：指定排序模板，按照指定模板的features进行基因内注释的排序。</li>
-<li>-i：按特征类型(feature type)对多亚型基因(multi-isoform gene)进行排序，默认是关闭的。</li>
-<li>-r：此参数按gff3文件里scaffold(seqID)出现的顺序排序，默认是按照scaffold的数字排序。</li>
-</ul>
-<ol start="3">
-<li><p>排序模板sort_template.txt文件示例</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">gene pseudogene</span><br><span class="line">mRNA</span><br><span class="line">exon</span><br><span class="line">CDS</span><br></pre></td></tr></table></figure>
-</li>
-<li><p>输出</p>
-</li>
-</ol>
-<ul>
-<li>sorted.gff3：排序结果文件</li>
-</ul>
-<h2 id="4-3-gff3-QC"><a href="#4-3-gff3-QC" class="headerlink" title="4.3. gff3_QC"></a>4.3. gff3_QC</h2><p>用于检测gff3注释的格式错误</p>
-<ol>
-<li><p>命令<br><code>nohup gff3_QC -g sample.gff3 -f genome.fa -o sample.qc -s statistic.txt &gt;qc.log 2&gt;&amp;1 &amp;</code></p>
-</li>
-<li><p>参数</p>
-</li>
-</ol>
-<ul>
-<li>-g sample.gff3：指定需要质控的gff文件</li>
-<li>-f genome.fa：指定基因组文件</li>
-<li>-o sample.qc：指定质控结果输出文件</li>
-<li>-s statistic.txt：指定统计输出文件</li>
-</ul>
-<ol start="3">
-<li>输出</li>
-</ol>
-<ul>
-<li>sample.qc：质控结果</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Line_num	Error_code	Error_level	Error_tag</span><br><span class="line">[&#x27;Line 1&#x27;]	Esf0014	Error	[&quot;##gff-version&quot; missing from the first line]</span><br><span class="line">[&#x27;Line 1&#x27;]	Esf0041	Error	[Unknown reserved (uppercase) attribute: &quot;Augustus_transcriptSupport_percentage&quot;]</span><br><span class="line">[&#x27;Line 749964&#x27;]	Ema0002	Warning	[Protein sequence contains internal stop codons at bp 2397350]</span><br><span class="line">[&#x27;Line 750456&#x27;]	Ema0001	Warning	[Parent feature start and end coordinates exceed those of child features]</span><br><span class="line">[&#x27;Line 18852&#x27;, &#x27;Line 18838&#x27;]	Emr0002	Warning	[Incorrectly split gene parent?]</span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>statistic.txt：质控结果的统计</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Error_code	Number_of_problematic_models	Error_level	Error_tag</span><br><span class="line">Esf0014	1	Error	##gff-version&quot; missing from the first line</span><br><span class="line">Esf0041	378399	Error	Unknown reserved (uppercase) attribute</span><br><span class="line">Ema0006	9	Info	Wrong phase</span><br><span class="line">Esf0027	8	Error	Phase is required for all CDS features</span><br><span class="line">Ema0002	468	Warning	Protein sequence contains internal stop codons</span><br><span class="line">Ema0001	1024	Warning	Parent feature start and end coordinates exceed those of child features</span><br><span class="line">Emr0002	31	Warning	Incorrectly split gene parent?</span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>qc.log：运行log文件，也包含了质控相关信息。</li>
-</ul>
-<h2 id="4-4-gff3-fix"><a href="#4-4-gff3-fix" class="headerlink" title="4.4. gff3_fix"></a>4.4. gff3_fix</h2><p>用于修正gff3注释文件格式错误</p>
-<ol>
-<li><p>命令<br><code>nohup gff3_fix -qc_r sample.qc -g sample.gff3 -og corrected.gff3 &gt;fix.log 2&gt;&amp;1 &amp;</code></p>
-</li>
-<li><p>参数</p>
-</li>
-</ol>
-<ul>
-<li>-qc_r sample.qc：指定质控结果文件作为修正参考，这里用gff3_QC的输出文件</li>
-<li>-g sample.gff3：指定需要修正的gff文件</li>
-<li>-og corrected.gff3：指定输出修正后的gff文件</li>
-</ul>
-<ol start="3">
-<li>输出</li>
-</ol>
-<ul>
-<li>corrected.gff3：修正后的gff文件</li>
-<li>fix.log：运行log文件。</li>
-</ul>
-<h2 id="4-5-gff3-to-fasta"><a href="#4-5-gff3-to-fasta" class="headerlink" title="4.5. gff3_to_fasta"></a>4.5. gff3_to_fasta</h2><p>用于根据基因组fasta和注释gff生成gene&#x2F;cds&#x2F;protein&#x2F;exon等序列，速度很快。</p>
-<ol>
-<li><p>命令<br><code>gff3_to_fasta -g sample.gff3 -f genome.fa -st all -d simple -o sample</code></p>
-</li>
-<li><p>参数</p>
-</li>
-</ol>
-<ul>
-<li>-g sample.gff3：指定需要修正的gff文件</li>
-<li>-f genome.fa：指定基因组文件</li>
-<li>-st all：指定输出的序列类型，all&#x2F;gene&#x2F;exon&#x2F;cds&#x2F;pep&#x2F;trans&#x2F;pre_trans&#x2F;user_defined。</li>
-<li>-u mRNA CDS：如果用-st user_defined指定了输出自定义的序列，则需要-u参数指定自定义序列名称的parent和child类型。比如-u mRNA CDS会输出CDS序列。</li>
-<li>-d simple：指定输出fasta文件的序列ID的格式，simple是只输出gff文件的ID到fasta文件，complete输出完整的信息。</li>
-<li>-noQC：默认会执行质控程序，如果不想做质控，加上这个参数。</li>
-<li>-o sample：指定输出文件前缀</li>
-</ul>
-<ol start="3">
-<li>输出<br>输出指定序列，比如：</li>
-</ol>
-<ul>
-<li>sample_gene.fa</li>
-<li>sample_exon.fa：exon序列是根据注释情况每条注释生成一条exon序列。</li>
-<li>sample_cds.fa：cds序列是一个mRNA的一条CDS序列。</li>
-<li>sample_pep.fa</li>
-<li>sample_trans.fa</li>
-<li>sample_pre_trans.fa</li>
-</ul>
-<h1 id="5-reference"><a href="#5-reference" class="headerlink" title="5. reference"></a>5. reference</h1><ol>
-<li>gff3toolkit github：<a href="https://github.com/NAL-i5K/GFF3toolkit">https://github.com/NAL-i5K/GFF3toolkit</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>fileformat</category>
-      </categories>
-      <tags>
-        <tag>GFF3</tag>
-        <tag>GFF3toolkit</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>samtools faidx创建fasta/fastq格式文件的.fai格式索引文件</title>
-    <url>/2023/11/24/bioinfo_fileformat_fa_fai/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1394643615&auto=1&height=32"></iframe></div>
-
-<h1 id="1-为fasta-x2F-fastq格式文件创建索引"><a href="#1-为fasta-x2F-fastq格式文件创建索引" class="headerlink" title="1. 为fasta&#x2F;fastq格式文件创建索引"></a>1. 为fasta&#x2F;fastq格式文件创建索引</h1><ul>
-<li>fasta&#x2F;fastq文件是用于存储生物序列的常用格式，后缀为fasta&#x2F;fas&#x2F;fa&#x2F;fastq&#x2F;fq。</li>
-<li>为fasta&#x2F;fastq创建索引文件，可以快速查找和提取任意位置序列。如使用GATK,IGV等软件时用到。</li>
-<li><code>samtools faidx sample.fa</code>命令即可创建<code>sample.fa.fai</code>索引文件。</li>
-<li><code>samtools faidx</code>命令对fasta文件的格式有要求。即对每条序列内，除最后一行外，其他行长度必须相同，行末尾的换行符也必须一致（Unix-style或Windows-style）。同一个fasta文件内的不同序列间的长度和换行符可以不一致，但同一条序列内部必须一致。</li>
-<li><code>samtools faidx</code>命令对fastq文件的格式的要求是fastq的碱基质量行必须与序列行有着一样的长度。</li>
-</ul>
-<h1 id="2-索引文件含义"><a href="#2-索引文件含义" class="headerlink" title="2. 索引文件含义"></a>2. 索引文件含义</h1><p>.fai文件是文本文件，列间用TAB(\t)分隔，fa.fai共五列，fq.fai共六列（多一列QUALOFFSET）。</p>
-<h2 id="2-1-fai文件的六列"><a href="#2-1-fai文件的六列" class="headerlink" title="2.1. fai文件的六列"></a>2.1. fai文件的六列</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">NAME	Name of this reference sequence</span><br><span class="line">LENGTH	Total length of this reference sequence, in bases</span><br><span class="line">OFFSET	Offset in the FASTA/FASTQ file of this sequence&#x27;s first base</span><br><span class="line">LINEBASES	The number of bases on each line</span><br><span class="line">LINEWIDTH	The number of bytes in each line, including the newline</span><br><span class="line">QUALOFFSET	Offset of sequence&#x27;s first quality within the FASTQ file</span><br></pre></td></tr></table></figure>
-
-<h2 id="2-2-每列的含义"><a href="#2-2-每列的含义" class="headerlink" title="2.2. 每列的含义"></a>2.2. 每列的含义</h2><ol>
-<li>NAME：序列名称，保留&gt;符号后到第一个空格前的字符串。与SAM文件的@SO header的SN值一致。</li>
-<li>LENGTH：序列长度，即碱基的数量。与SAM文件的@SO header的SL值一致。</li>
-<li>OFFSET：OFFSET（偏移）列包含该序列第一个碱基在 FASTA&#x2F;FASTQ 文件中的偏移量（以字节为单位，从零开始计数，换行符也统计在内），即从标题行（FASTA 中为”&gt;”行，FASTQ 中为”@”行）的第一个字符到此条序列的第一个碱基（包括换行符）的字符数量。通常情况下，fai 索引文件的各行是按照序列在 FASTA&#x2F;FASTQ 文件中出现的顺序排列的，因此 .fai 文件通常是按照这一列排序的。</li>
-<li>LINEBASES：除了较短的最后一行外， 其他代表序列的行的碱基数， 单位为bp；</li>
-<li>LINEWIDTH：除了较短的最后一行外， 其他代表序列的行的长度。与LINEBASES不同的是，LINEWIDTH 包括换行符， unix系统中换行符为\n，在序列长度基础上加1；Windows系统中换行符为\r\n, 要在序列长度的基础上加2。</li>
-<li>QUALOFFSET：与 OFFSET 相同，但针对的是序列的第一个质量分数。这将是 “+”行末尾换行后的第一个字符。仅适用于 FASTQ 文件。</li>
+<h1 id="2-索引文件含义"><a href="#2-索引文件含义" class="headerlink" title="2. 索引文件含义"></a>2. 索引文件含义</h1><p>.fai文件是文本文件，列间用TAB(\t)分隔，fa.fai共五列，fq.fai共六列（多一列QUALOFFSET）。</p>
+<h2 id="2-1-fai文件的六列"><a href="#2-1-fai文件的六列" class="headerlink" title="2.1. fai文件的六列"></a>2.1. fai文件的六列</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">NAME	Name of this reference sequence</span><br><span class="line">LENGTH	Total length of this reference sequence, in bases</span><br><span class="line">OFFSET	Offset in the FASTA/FASTQ file of this sequence&#x27;s first base</span><br><span class="line">LINEBASES	The number of bases on each line</span><br><span class="line">LINEWIDTH	The number of bytes in each line, including the newline</span><br><span class="line">QUALOFFSET	Offset of sequence&#x27;s first quality within the FASTQ file</span><br></pre></td></tr></table></figure>
+
+<h2 id="2-2-每列的含义"><a href="#2-2-每列的含义" class="headerlink" title="2.2. 每列的含义"></a>2.2. 每列的含义</h2><ol>
+<li>NAME：序列名称，保留&gt;符号后到第一个空格前的字符串。与SAM文件的@SO header的SN值一致。</li>
+<li>LENGTH：序列长度，即碱基的数量。与SAM文件的@SO header的SL值一致。</li>
+<li>OFFSET：OFFSET（偏移）列包含该序列第一个碱基在 FASTA&#x2F;FASTQ 文件中的偏移量（以字节为单位，从零开始计数，换行符也统计在内），即从标题行（FASTA 中为”&gt;”行，FASTQ 中为”@”行）的第一个字符到此条序列的第一个碱基（包括换行符）的字符数量。通常情况下，fai 索引文件的各行是按照序列在 FASTA&#x2F;FASTQ 文件中出现的顺序排列的，因此 .fai 文件通常是按照这一列排序的。</li>
+<li>LINEBASES：除了较短的最后一行外， 其他代表序列的行的碱基数， 单位为bp；</li>
+<li>LINEWIDTH：除了较短的最后一行外， 其他代表序列的行的长度。与LINEBASES不同的是，LINEWIDTH 包括换行符， unix系统中换行符为\n，在序列长度基础上加1；Windows系统中换行符为\r\n, 要在序列长度的基础上加2。</li>
+<li>QUALOFFSET：与 OFFSET 相同，但针对的是序列的第一个质量分数。这将是 “+”行末尾换行后的第一个字符。仅适用于 FASTQ 文件。</li>
 </ol>
 <h2 id="2-3-例子"><a href="#2-3-例子" class="headerlink" title="2.3. 例子"></a>2.3. 例子</h2><p><a href="https://www.htslib.org/doc/faidx.html">https://www.htslib.org/doc/faidx.html</a> 中有一些fasta和fastq文件的索引文件的例子。</p>
 <h1 id="3-应用"><a href="#3-应用" class="headerlink" title="3. 应用"></a>3. 应用</h1><ol>
@@ -3761,6 +3495,67 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>fai</tag>
       </tags>
   </entry>
+  <entry>
+    <title>从codon比对的cds序列中提取四倍简并位点</title>
+    <url>/2021/10/10/bioinfo_extract.4dtv.from.cds/</url>
+    <content><![CDATA[<h1 id="1-从condon比对的cds序列中提取四倍简并位点"><a href="#1-从condon比对的cds序列中提取四倍简并位点" class="headerlink" title="1. 从condon比对的cds序列中提取四倍简并位点"></a>1. 从condon比对的cds序列中提取四倍简并位点</h1><p>输入文件是根据密码子规则（即三个三个对齐）比对好的cds序列，输出文件是以其中一个物种的4dtv为标准提取所有物种的4dtv位点，提取的也是按照比对前的顺序排列，结果保存在4dtv.aln文件中。</p>
+<h2 id="1-1-输入数据和变量定义"><a href="#1-1-输入数据和变量定义" class="headerlink" title="1.1. 输入数据和变量定义"></a>1.1. 输入数据和变量定义</h2><ol>
+<li><code>cds=/path/to/cds.aln</code> # 定义变量cds为codon模式比对好的cds序列</li>
+<li><code>species=Athaliana</code> #以哪个物种的4dtv为标准提取，就定义变量species为那个物种的序列ID</li>
+</ol>
+<h2 id="1-2-三种方案【选择一种】"><a href="#1-2-三种方案【选择一种】" class="headerlink" title="1.2. 三种方案【选择一种】"></a>1.2. 三种方案【选择一种】</h2><h3 id="1-2-1-一步生成"><a href="#1-2-1-一步生成" class="headerlink" title="1.2.1. 一步生成"></a>1.2.1. 一步生成</h3><ol>
+<li>一步生成运行文件，生成从cds获取4dtv的命令，很长，所以储存在文件4dtv.sh中。运行4dtv.sh就可以获取<br><code>seqkit locate -V 0 -i -d -p GCN -p CGN -p GGN -p CTN -p CCN -p TCN -p ACN -p GTN $&#123;cds&#125; |awk -v awka=&quot;$&#123;species&#125;&quot; &#39;&#123;if ($1 == awka &amp;&amp; $6%3== 0) print $6&#125;&#39;|sort -k 1n |uniq |awk -v awkb=&quot;$&#123;cds&#125;&quot; &#39;&#123;print &quot;&lt;(seqkit subseq -r &quot;$1&quot;:&quot;$1&quot; &quot;awkb&quot;)&quot;&#125;&#39; |sed -e &#39;1i\seqkit concat -w 0&#39; -e &#39;$a\&gt; 4dtv.aln&#39;|xargs echo &gt;4dtv.sh</code></li>
+</ol>
+<p>代码含义：用seqkit locate根据4dtv的规则提取位置，然后筛选指定物种的位置，筛选第三位位置是3的整数倍的位置，把第三位位置(即4dtv位点)提取出来，排序去重；再按照seqkit concat要求的格式规则把seqkit concat命令写入提取脚本4dtv.sh中。</p>
+<p>除非位点在1000个以内，否则一步生成运行文件可能会运行失败，推荐用下一种方案。</p>
+<ol start="2">
+<li>运行生成的4dtv.sh，获得4dtv.aln结果<br><code>bash 4dtv.sh</code></li>
+</ol>
+<h3 id="1-2-2-切割分开运行【推荐】"><a href="#1-2-2-切割分开运行【推荐】" class="headerlink" title="1.2.2. 切割分开运行【推荐】"></a>1.2.2. 切割分开运行【推荐】</h3><p>如果需要合并的位点超过1000个，一步生成运行文件占用内存，运行较慢，可以分步骤进行。</p>
+<p>如果因为4dtv.sh太长无法运行，可以把4dtv.temp分成1-n几部分（比如1000行作为一个子文件），然后切割各部分分别生成4dtv_n.sh，之后分别运行再合并。</p>
+<ol>
+<li><p>生成临时文件4dtv.temp<br><code>seqkit locate -V 0 -i -d -p GCN -p CGN -p GGN -p CTN -p CCN -p TCN -p ACN -p GTN $&#123;cds&#125; |awk -v awka=&quot;$&#123;species&#125;&quot; &#39;&#123;if ($1 == awka &amp;&amp; $6%3== 0) print $6&#125;&#39; |sort -k 1n |uniq &gt;4dtv.temp</code></p>
+</li>
+<li><p>分割临时文件4dtv.temp<br><code>split -l 1000 4dtv.temp -d -a 4 4dtv.temp_</code><br>命令含义：分割文件4dtv.temp成多个文件，每个子文件1000行（-l 1000），以数字（-d）而非字母命名子文件，数字长度为4（-a 4，default是2）；会生成4dtv.temp_0000-nnnn的子文件。</p>
+</li>
+</ol>
+<p>可以先看看4dtv.temp的长度，我尝试了下按1000行切割比较合适，再多就达到能同时打开文件数量上限无法运行了。</p>
+<ol start="3">
+<li><p>生成4dtv.temp.n.sh<br><code>for i in $(ls 4dtv.temp_*);do cat $&#123;i&#125; |awk -v awkb=&quot;$&#123;cds&#125;&quot; &#39;&#123;print &quot;&lt;(seqkit subseq -r &quot;$1&quot;:&quot;$1&quot; &quot;awkb&quot;)&quot;&#125;&#39; |sed -e &#39;1i\seqkit concat -w 0&#39; -e &#39;$a\&gt;&#39;|sed &quot;s/^&gt;$/&gt; $&#123;i&#125;.aln/&quot; |sed &#39;s/$/ \\/g&#39; |sed &#39;$ s/.$//&#39; &gt;$&#123;i&#125;.sh; done</code></p>
+</li>
+<li><p>运行生成的4dtv.temp.n.sh，获得4dtv.aln_n结果【耗时步骤】<br><code>for i in $(ls 4dtv.temp_*.sh);do bash $&#123;i&#125;;done</code></p>
+</li>
+</ol>
+<p>建议用nohup放到后台运行<br><code>nohup sh -c &#39;for i in $(ls 4dtv.temp_*.sh);do bash $&#123;i&#125;;done&#39; &amp;</code></p>
+<ol start="5">
+<li>合并子文件，获得最终结果<br><code>seqkit concat ./4dtv.temp_*.aln &gt;4dtv.aln</code></li>
+</ol>
+<p>可以<code>seqkit stats *aln</code>检查是否都是1000长度的序列；以及<code>seqkit stats 4dtv.aln</code>检查是否序列长度与4dtv.temp的行数一致。</p>
+<ol start="6">
+<li>删除中间文件<br><code>rm 4dtv.temp*</code></li>
+</ol>
+<h3 id="1-2-3-分物种运行【不推荐】"><a href="#1-2-3-分物种运行【不推荐】" class="headerlink" title="1.2.3. 分物种运行【不推荐】"></a>1.2.3. 分物种运行【不推荐】</h3><p>一开始没发现seqkit concat这个工具，然后自己写的合并，有时会有aln的问题，不建议用。</p>
+<ol>
+<li>定义数组sps为物种列表<br><code>sps=($(seqkit seq -n $&#123;cds&#125;|xargs))</code></li>
+<li>以第六个物种${sps[5]}的4dtv为标准，为每个物种保存一份4dtv.bed文件<br><code>for i in $(echo $&#123;sps[*]&#125;); do cat $&#123;sps[5]&#125;.4dtv|sed &quot;s/$&#123;species[5]&#125;/$&#123;i&#125;/g&quot; &gt;$&#123;i&#125;.4dtv.bed; done</code></li>
+<li>为每个物种提取4dtv位点，保存到4dtv.aln.temp【耗时步骤】<br><code>for i in $(echo $&#123;sps[*]&#125;); do seqkit subseq --bed $&#123;i&#125;.4dtv.bed $&#123;cds&#125; | grep -v &quot;&gt;&quot;|xargs echo |sed &quot;1i\&gt;$&#123;i&#125;&quot; &gt;$&#123;i&#125;.4dtv.aln.temp; done</code></li>
+<li>合并物种的4dtv位点<br><code>cat *.4dtv.aln.temp &gt;4dtv.aln</code> #合并</li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>4dtv</category>
+      </categories>
+      <tags>
+        <tag>4dtv</tag>
+        <tag>codon</tag>
+      </tags>
+  </entry>
   <entry>
     <title>转换GenBank文件为tbl格式，为提交注释做准备</title>
     <url>/2022/06/16/bioinfo_fileformat_gb2tbl/</url>
@@ -3910,29 +3705,189 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>软件mitogenomics用于线粒体基因组相关格式转换</title>
-    <url>/2022/06/30/bioinfo_fileformat_mitogenomics/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=27901965&auto=1&height=32"></iframe></div>
+    <title>GFF3文件处理软件 —— GFF3toolkit工具包的介绍和使用</title>
+    <url>/2022/05/24/bioinfo_fileformat_GFF3toolkit/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283095&auto=1&height=32"></iframe></div>
 
-<h1 id="mitogenomics简介"><a href="#mitogenomics简介" class="headerlink" title="mitogenomics简介"></a>mitogenomics简介</h1><ul>
-<li>无意中发现的一个软件。</li>
-<li>基于已有线粒体基因组序列和基因序列，通过比对获取注释tbl格式文件，所以只适用于线粒体的基因。</li>
-<li>包括两个转化格式的脚本mitos2fasta.py，aln2tbl.py（和python2版本）。</li>
+<h1 id="1-GFF3toolkit-简介"><a href="#1-GFF3toolkit-简介" class="headerlink" title="1. GFF3toolkit 简介"></a>1. GFF3toolkit 简介</h1><p><a href="https://github.com/NAL-i5K/GFF3toolkit">GFF3toolkit</a>是用于处理GFF3格式文件的一个基于python的工具包，功能包括检测GFF3格式错误，修正GFF3格式错误，合并GFF3格式文件，排序GFF3格式文件，用GFF3格式文件生成序列等。</p>
+<h1 id="2-GFF3toolkit-安装"><a href="#2-GFF3toolkit-安装" class="headerlink" title="2. GFF3toolkit 安装"></a>2. GFF3toolkit 安装</h1><p><code>pip install gff3tool</code></p>
+<h1 id="3-GFF3toolkit-模块"><a href="#3-GFF3toolkit-模块" class="headerlink" title="3. GFF3toolkit 模块"></a>3. GFF3toolkit 模块</h1><p>GFF3toolkit包含许多模块：</p>
+<ul>
+<li>gff3_QC：检测gff3格式错误</li>
+<li>gff3_fix：修正gff3格式错误</li>
+<li>gff3_merge：合并两个gff3文件</li>
+<li>gff3_sort：根据scaffold，coordinates坐标来排序gff3文件</li>
+<li>gff3_to_fasta：根据基因组fasta和注释gff生成gene&#x2F;cds&#x2F;protein&#x2F;exon等序列</li>
 </ul>
-<h1 id="软件安装"><a href="#软件安装" class="headerlink" title="软件安装"></a>软件安装</h1><ol>
-<li><p>git下载<br><code>git clone https://github.com/IMEDEA/mitogenomics</code></p>
+<h1 id="4-GFF3toolkit-使用"><a href="#4-GFF3toolkit-使用" class="headerlink" title="4. GFF3toolkit 使用"></a>4. GFF3toolkit 使用</h1><h2 id="4-1-gff3-merge"><a href="#4-1-gff3-merge" class="headerlink" title="4.1. gff3_merge"></a>4.1. gff3_merge</h2><p>用于合并两个gff3注释文件。</p>
+<ol>
+<li><p>命令<br><code>gff3_merge -g1 sample1.gff3 -g2 sample2.gff3 -f genome.fa -og merged.gff3 -r merged.report</code></p>
 </li>
-<li><p>脚本</p>
+<li><p>参数</p>
 </li>
 </ol>
 <ul>
-<li>因为软件是三个脚本组成，可以直接使用脚本。</li>
+<li>-g1 sample1.gff3：指定待合并的gff3文件1</li>
+<li>-g2 sample2.gff3：指定待合并的gff3文件2</li>
+<li>-f genome.fa：指定基因组文件</li>
+<li>-og merged.gff3：指定合并后结果文件</li>
+<li>-r merged.report：指定合并报告结果文件</li>
+<li>-noAuto：这个参数关闭添加replace tags的自动任务。默认是开启的。</li>
+<li>-a：默认开启添加replace tags的自动任务只应用在没有replace tags的transcript转录本上，-a参数设置为应用在所有转录本上。</li>
 </ul>
 <ol start="3">
-<li>依赖</li>
+<li>输出</li>
 </ol>
 <ul>
-<li>python3的Biopython和argparse模块。</li>
+<li>merged.gff3：合并后的gff文件</li>
+<li>merged.report：合并报告</li>
+</ul>
+<h2 id="4-2-gff3-sort"><a href="#4-2-gff3-sort" class="headerlink" title="4.2. gff3_sort"></a>4.2. gff3_sort</h2><p>用于排序gff3文件。根据scaffold(seqID)，coordinates坐标排序基因，根据特征关系(feature relationship)排序基因内注释。</p>
+<p>排序的前提假设：</p>
+<ul>
+<li>任何没有Parent属性的注释都是父注释。</li>
+<li>所有子注释都排在其父注释之后，并在新的父注释之前。</li>
+</ul>
+<ol>
+<li><p>命令<br><code>gff3_sort -g sample.gff3 -og sorted.gff3</code></p>
+</li>
+<li><p>参数</p>
+</li>
+</ol>
+<ul>
+<li>-g sample.gff3：指定输入gff3文件</li>
+<li>-og sorted.gff3：指定输出排序好的gff3文件</li>
+<li>-t sort_template.txt：指定排序模板，按照指定模板的features进行基因内注释的排序。</li>
+<li>-i：按特征类型(feature type)对多亚型基因(multi-isoform gene)进行排序，默认是关闭的。</li>
+<li>-r：此参数按gff3文件里scaffold(seqID)出现的顺序排序，默认是按照scaffold的数字排序。</li>
+</ul>
+<ol start="3">
+<li><p>排序模板sort_template.txt文件示例</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">gene pseudogene</span><br><span class="line">mRNA</span><br><span class="line">exon</span><br><span class="line">CDS</span><br></pre></td></tr></table></figure>
+</li>
+<li><p>输出</p>
+</li>
+</ol>
+<ul>
+<li>sorted.gff3：排序结果文件</li>
+</ul>
+<h2 id="4-3-gff3-QC"><a href="#4-3-gff3-QC" class="headerlink" title="4.3. gff3_QC"></a>4.3. gff3_QC</h2><p>用于检测gff3注释的格式错误</p>
+<ol>
+<li><p>命令<br><code>nohup gff3_QC -g sample.gff3 -f genome.fa -o sample.qc -s statistic.txt &gt;qc.log 2&gt;&amp;1 &amp;</code></p>
+</li>
+<li><p>参数</p>
+</li>
+</ol>
+<ul>
+<li>-g sample.gff3：指定需要质控的gff文件</li>
+<li>-f genome.fa：指定基因组文件</li>
+<li>-o sample.qc：指定质控结果输出文件</li>
+<li>-s statistic.txt：指定统计输出文件</li>
+</ul>
+<ol start="3">
+<li>输出</li>
+</ol>
+<ul>
+<li>sample.qc：质控结果</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Line_num	Error_code	Error_level	Error_tag</span><br><span class="line">[&#x27;Line 1&#x27;]	Esf0014	Error	[&quot;##gff-version&quot; missing from the first line]</span><br><span class="line">[&#x27;Line 1&#x27;]	Esf0041	Error	[Unknown reserved (uppercase) attribute: &quot;Augustus_transcriptSupport_percentage&quot;]</span><br><span class="line">[&#x27;Line 749964&#x27;]	Ema0002	Warning	[Protein sequence contains internal stop codons at bp 2397350]</span><br><span class="line">[&#x27;Line 750456&#x27;]	Ema0001	Warning	[Parent feature start and end coordinates exceed those of child features]</span><br><span class="line">[&#x27;Line 18852&#x27;, &#x27;Line 18838&#x27;]	Emr0002	Warning	[Incorrectly split gene parent?]</span><br></pre></td></tr></table></figure>
+
+<ul>
+<li>statistic.txt：质控结果的统计</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Error_code	Number_of_problematic_models	Error_level	Error_tag</span><br><span class="line">Esf0014	1	Error	##gff-version&quot; missing from the first line</span><br><span class="line">Esf0041	378399	Error	Unknown reserved (uppercase) attribute</span><br><span class="line">Ema0006	9	Info	Wrong phase</span><br><span class="line">Esf0027	8	Error	Phase is required for all CDS features</span><br><span class="line">Ema0002	468	Warning	Protein sequence contains internal stop codons</span><br><span class="line">Ema0001	1024	Warning	Parent feature start and end coordinates exceed those of child features</span><br><span class="line">Emr0002	31	Warning	Incorrectly split gene parent?</span><br></pre></td></tr></table></figure>
+
+<ul>
+<li>qc.log：运行log文件，也包含了质控相关信息。</li>
+</ul>
+<h2 id="4-4-gff3-fix"><a href="#4-4-gff3-fix" class="headerlink" title="4.4. gff3_fix"></a>4.4. gff3_fix</h2><p>用于修正gff3注释文件格式错误</p>
+<ol>
+<li><p>命令<br><code>nohup gff3_fix -qc_r sample.qc -g sample.gff3 -og corrected.gff3 &gt;fix.log 2&gt;&amp;1 &amp;</code></p>
+</li>
+<li><p>参数</p>
+</li>
+</ol>
+<ul>
+<li>-qc_r sample.qc：指定质控结果文件作为修正参考，这里用gff3_QC的输出文件</li>
+<li>-g sample.gff3：指定需要修正的gff文件</li>
+<li>-og corrected.gff3：指定输出修正后的gff文件</li>
+</ul>
+<ol start="3">
+<li>输出</li>
+</ol>
+<ul>
+<li>corrected.gff3：修正后的gff文件</li>
+<li>fix.log：运行log文件。</li>
+</ul>
+<h2 id="4-5-gff3-to-fasta"><a href="#4-5-gff3-to-fasta" class="headerlink" title="4.5. gff3_to_fasta"></a>4.5. gff3_to_fasta</h2><p>用于根据基因组fasta和注释gff生成gene&#x2F;cds&#x2F;protein&#x2F;exon等序列，速度很快。</p>
+<ol>
+<li><p>命令<br><code>gff3_to_fasta -g sample.gff3 -f genome.fa -st all -d simple -o sample</code></p>
+</li>
+<li><p>参数</p>
+</li>
+</ol>
+<ul>
+<li>-g sample.gff3：指定需要修正的gff文件</li>
+<li>-f genome.fa：指定基因组文件</li>
+<li>-st all：指定输出的序列类型，all&#x2F;gene&#x2F;exon&#x2F;cds&#x2F;pep&#x2F;trans&#x2F;pre_trans&#x2F;user_defined。</li>
+<li>-u mRNA CDS：如果用-st user_defined指定了输出自定义的序列，则需要-u参数指定自定义序列名称的parent和child类型。比如-u mRNA CDS会输出CDS序列。</li>
+<li>-d simple：指定输出fasta文件的序列ID的格式，simple是只输出gff文件的ID到fasta文件，complete输出完整的信息。</li>
+<li>-noQC：默认会执行质控程序，如果不想做质控，加上这个参数。</li>
+<li>-o sample：指定输出文件前缀</li>
+</ul>
+<ol start="3">
+<li>输出<br>输出指定序列，比如：</li>
+</ol>
+<ul>
+<li>sample_gene.fa</li>
+<li>sample_exon.fa：exon序列是根据注释情况每条注释生成一条exon序列。</li>
+<li>sample_cds.fa：cds序列是一个mRNA的一条CDS序列。</li>
+<li>sample_pep.fa</li>
+<li>sample_trans.fa</li>
+<li>sample_pre_trans.fa</li>
+</ul>
+<h1 id="5-reference"><a href="#5-reference" class="headerlink" title="5. reference"></a>5. reference</h1><ol>
+<li>gff3toolkit github：<a href="https://github.com/NAL-i5K/GFF3toolkit">https://github.com/NAL-i5K/GFF3toolkit</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>fileformat</category>
+      </categories>
+      <tags>
+        <tag>GFF3</tag>
+        <tag>GFF3toolkit</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>软件mitogenomics用于线粒体基因组相关格式转换</title>
+    <url>/2022/06/30/bioinfo_fileformat_mitogenomics/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=27901965&auto=1&height=32"></iframe></div>
+
+<h1 id="mitogenomics简介"><a href="#mitogenomics简介" class="headerlink" title="mitogenomics简介"></a>mitogenomics简介</h1><ul>
+<li>无意中发现的一个软件。</li>
+<li>基于已有线粒体基因组序列和基因序列，通过比对获取注释tbl格式文件，所以只适用于线粒体的基因。</li>
+<li>包括两个转化格式的脚本mitos2fasta.py，aln2tbl.py（和python2版本）。</li>
+</ul>
+<h1 id="软件安装"><a href="#软件安装" class="headerlink" title="软件安装"></a>软件安装</h1><ol>
+<li><p>git下载<br><code>git clone https://github.com/IMEDEA/mitogenomics</code></p>
+</li>
+<li><p>脚本</p>
+</li>
+</ol>
+<ul>
+<li>因为软件是三个脚本组成，可以直接使用脚本。</li>
+</ul>
+<ol start="3">
+<li>依赖</li>
+</ol>
+<ul>
+<li>python3的Biopython和argparse模块。</li>
 <li>可以用pip安装<code>pip install biopython argparse</code>。</li>
 </ul>
 <h1 id="软件mitogenomics"><a href="#软件mitogenomics" class="headerlink" title="软件mitogenomics"></a>软件mitogenomics</h1><h2 id="脚本mitos2fasta-py"><a href="#脚本mitos2fasta-py" class="headerlink" title="脚本mitos2fasta.py"></a>脚本mitos2fasta.py</h2><p>python 3 版本，用于转化基因序列为比对到线粒体基因组序列的格式。</p>
@@ -4286,65 +4241,234 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>系统发育网络推断 —— PhyloNetworks</title>
-    <url>/2022/04/14/bioinfo_geneflow_PhyloNetworks/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=108998&auto=1&height=32"></iframe></div>
+    <title>基因流推断 —— Dsuite</title>
+    <url>/2022/04/10/bioinfo_geneflow_Dsuite/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=171959&auto=1&height=32"></iframe></div>
 
-<h1 id="1-PhyloNetworks"><a href="#1-PhyloNetworks" class="headerlink" title="1. PhyloNetworks"></a>1. PhyloNetworks</h1><ol>
-<li>PhyloNetworks简介<ul>
-<li>PhyloNetworks是通过基因树或多位点序列(SNaQ)的最大伪似然进行推断系统发育网络的一个Julia包。</li>
+<h1 id="1-Dsuite"><a href="#1-Dsuite" class="headerlink" title="1. Dsuite"></a>1. Dsuite</h1><ol>
+<li>Dsuite简介<ul>
+<li>Dsuite是通过计算Patterson’s D统计量(即ABBA统计量)和f4等统计量来评估种群间或近缘种间基因流的基于C语言的软件。</li>
 </ul>
 </li>
-<li>PhyloNetworks原理<ul>
-<li>原理：通过SNaQ来实现网络推断，SNaQ通过估计4分类群子集的最大伪似然来加速运算，估计的网络不受根的影响。</li>
+<li>Dsuite 原理<ul>
+<li>D值（即ABBA统计量）和f4-ratio统计可以表示为适用于四个分类群的双等位基因SNP：P1,P2,P3,O，拓扑是 (((P1,P2),P3),O)。</li>
+<li>其中外类群O携带祖先等位基因A，衍生等位基因用B表示。BBAA,ABBA,BABA分别代表四个分类群携带等位的三种模式。</li>
+<li>在没有基因流的零假设下，由于具有相同频率的不完全谱系分类，预计P3与P1或P2共享衍生等位基因B的两种模式ABBA和BABA的频率相等，如果ABBA和BABA的频率有显著差异则代表在P3和P1或P2间存在基因渐渗。</li>
+<li>D&#x3D;(nABBA-nBABA)&#x2F;(nABBA+nBABA)；在外群对于祖先等位基因A是固定的（外群中B的频率为0）假设下，D统计量是等位基因模式计数的归一化差异。</li>
+<li>如果外群中衍生等位基因B不为0，则Dsuite的D值是Patterson’s D，适用于无根的四分类群树。</li>
 </ul>
 </li>
-<li>PhyloNetworks输入输出<ul>
-<li>输入：newick格式基因树(多个基因树组成的文件)</li>
-<li>输出：系统发育网络，基因流方向和杂交节点贡献比例</li>
+<li>Dsuite输入输出<ul>
+<li>输入：基因组snp的vcf格式文件，居群树文件(可选optional)</li>
+<li>输出：D值统计，f4-ratio统计，f-branch统计，f-branch树矩阵热图</li>
 </ul>
 </li>
-<li>PhyloNetworks优势和不足<ul>
-<li>推断系统发育网络，包括基因流的方向和强度。</li>
-<li>相较于其他推断系统发育网络的软件，PhyloNetworks集成了上游分析，网络估计，引导分析，下游特征进化分析，绘图等功能。</li>
-<li>不足是运行多样本(超过十个个体)和数据量大(超过1000个)会非常耗时(常常以星期&#x2F;月计时)。</li>
+<li>Dsuite优势和不足<ul>
+<li>Dsuite的优势是运行非常快(时间以小时计算)</li>
+<li>不足是Dsuite分析结果不包含基因流的方向</li>
 </ul>
 </li>
-<li>PhyloNetworks适用范围<ul>
-<li>PhyloNetworks适用于基因树数据</li>
+<li>Dsuite适用范围<ul>
+<li>Dsuite适用于基因组学大数据和多样本(超过十个)数据</li>
 <li>适用于居群间或物种间的基因流推测</li>
-<li>适用于推断基因流方向和强度</li>
+<li>即使每个群体只有一个个体也可以推测基因流</li>
+<li>还可以计算pool-seq数据的基因流</li>
+<li>相较其他计算D值软件，Dsuite还同时可以计算f4-ratio和f-branch，以及滑窗统计f相关值。</li>
 </ul>
 </li>
 </ol>
-<h1 id="2-PhyloNetworks安装"><a href="#2-PhyloNetworks安装" class="headerlink" title="2. PhyloNetworks安装"></a>2. PhyloNetworks安装</h1><p>PhyloNetworks是Julia包，Julia是一个类似R的计算机语言，可以交互式或脚本式执行命令。</p>
-<h2 id="2-1-Julia-安装"><a href="#2-1-Julia-安装" class="headerlink" title="2.1. Julia 安装"></a>2.1. Julia 安装</h2><p>如果想简要了解julia，可以看<a href="https://learnxinyminutes.com/docs/julia/">julia tutorials</a>。</p>
+<h1 id="2-Dsuite-install"><a href="#2-Dsuite-install" class="headerlink" title="2. Dsuite install"></a>2. Dsuite install</h1><ol>
+<li>安装Dsuite主程序<br>Dsuite是C编写的，需要编译；需要GCC(&gt;&#x3D;4.9.0)和zlib压缩库。<br>编译后可执行文件在Dsuite&#x2F;Build&#x2F;目录下，可以加到环境变量中或使用绝对路径。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/millanek/Dsuite.git</span><br><span class="line">cd Dsuite</span><br><span class="line">make</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>安装python3 Fbranch绘图脚本【可选但推荐】<br>如果要绘制f-branch计算结果，则需要安装这个绘图脚本。注意<code>--prefix=</code>后没有任何内容。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd utils</span><br><span class="line">python3 setup.py install --user --prefix=</span><br></pre></td></tr></table></figure>
+
+<h1 id="3-Dsuite分析"><a href="#3-Dsuite分析" class="headerlink" title="3. Dsuite分析"></a>3. Dsuite分析</h1><h2 id="3-1-输入文件"><a href="#3-1-输入文件" class="headerlink" title="3.1. 输入文件"></a>3.1. 输入文件</h2><ol>
+<li>sample.snp.vcf.gz<ul>
+<li>基因组尺度的snp文件，vcf格式，可用bgzip压缩</li>
+</ul>
+</li>
+<li>sets.txt<ul>
+<li>包括两列，tab分隔；第一列个体名，第二列物种名&#x2F;居群名。</li>
+<li>对于Dtrios模块，至少指定一个个体为外类群，外类群个体的第二列写Outgroup，可有多个。</li>
+<li>要注意第二列物种名不能包含短横杠-和句点.字符，否则Dsuite Dtrios虽然不会报错，但Dsuite Fbranch模块运行报错解析不了树文件(ERROR: The tree string could not be parsed correctly)。</li>
+</ul>
+</li>
+<li>species.newick【optional】<ul>
+<li>Newick格式的居群&#x2F;物种树文件，物种名称与sets.txt对应。</li>
+<li>外类群名称替换成Outgroup，与sets.txt对应。</li>
+<li>树文件必须定根在Outgroup，否则dtools.py报错树文件和franch.out的拓扑不一致。</li>
+<li>去掉支持率；否则Dsuite Fbranch模块运行报错解析不了树文件，ERROR: The tree string could not be parsed correctly。</li>
+<li>枝长最好也去掉，不去枝长也不会被Dsuite用到，还可能增加报错风险。</li>
+<li>这个文件对于Dtrios模块是可选项，但建议加上好画fbranch的图。</li>
+</ul>
+</li>
+</ol>
+<h2 id="3-2-Dtrios模块"><a href="#3-2-Dtrios模块" class="headerlink" title="3.2. Dtrios模块"></a>3.2. Dtrios模块</h2><p><code>Dsuite Dtrios</code>用于计算所有三物种组合的D和f4-ratio统计量</p>
 <ol>
-<li><p>预编译软件安装<br>在<a href="https://julialang.org/downloads/">julia software</a>找到对应版本的预编译的软件下载解压缩即可使用。</p>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://julialang-s3.julialang.org/bin/linux/x64/1.7/julia-1.7.2-linux-x86_64.tar.gz #下载linux的64位预编译的julia</span><br><span class="line">tar -xzf julia-1.7.2-linux-x86_64.tar.gz #解压缩</span><br><span class="line">julia-1.7.2/bin/julia -h #查看帮助文章</span><br></pre></td></tr></table></figure>
+<li><p>用法<br><code>Dsuite Dtrios sample.snp.vcf.gz sets.txt -t species.newick -o sample</code></p>
+</li>
+<li><p>输入和常用参数</p>
 </li>
-<li><p>源代码编译安装<br>通过<a href="https://github.com/JuliaLang/julia">julia github</a>获取源代码</p>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/JuliaLang/julia.git #克隆github上的最新版源代码</span><br><span class="line">cd julia</span><br><span class="line">git checkout v1.7.2 #运行checkout来获取julia的最新稳定版本1.7.2</span><br><span class="line">make #编译</span><br><span class="line">./julia -h #查看帮助文档</span><br></pre></td></tr></table></figure></li>
 </ol>
-<p>之后把julia命令的路径添加到环境变量，便可以直接使用julia了。<br>julia与R很像，进入交互界面，只需要在shell里键入<code>julia</code>回车即可，退出交互界面是ctrl+D。</p>
-<h2 id="2-2-安装PhyloNetworks包"><a href="#2-2-安装PhyloNetworks包" class="headerlink" title="2.2. 安装PhyloNetworks包"></a>2.2. 安装PhyloNetworks包</h2><ul>
-<li>进入julia运行<code>using Pkg</code>-<code>Pkg.add(&quot;PhyloNetworks&quot;)</code>即可安装PhyloNetworks包。</li>
-<li>在julia运行<code>Pkg.update()</code>会更新所有julia包。</li>
-<li>PhyloNetworks的配套包PhyloPlots是具有可视化网络和交互操作性的实用程序，例如将网络导出到 R（然后可以通过 R 绘制），建议安装：<code>using Pkg</code>-<code>Pkg.add(&quot;PhyloPlots&quot;)</code></li>
+<ul>
+<li>sample.snp.vcf.gz：变异文件</li>
+<li>sets.txt：分组文件</li>
+<li>-t species.newick：指定物种树文件</li>
+<li>-o sample：指定输出文件前缀，默认是sets</li>
+<li>-p 5：如果样品中包含pool-seq数据，-p用于设置最小深度，设置后从等位基因深度估计群体的等位基因频率。</li>
+<li>-c：不输出sample_combine_stderr.txt和sample_combine.txt；这两个文件用作DtriosCombine的输入，如果不需要可加上-c不输出这两个文件。</li>
 </ul>
-<h1 id="3-PhyloNetworks分析"><a href="#3-PhyloNetworks分析" class="headerlink" title="3. PhyloNetworks分析"></a>3. PhyloNetworks分析</h1><h2 id="3-1-输入文件"><a href="#3-1-输入文件" class="headerlink" title="3.1. 输入文件"></a>3.1. 输入文件</h2><ol>
-<li>CF表</li>
+<ol start="3">
+<li>运行耗时</li>
 </ol>
 <ul>
-<li>输入文件可以有多种形式，不管哪种形式，最终PhyloNetworks的SNaQ用到的都是CF表。</li>
-<li>每个4分类群子集的一致性因子(CF)表，即基因树频率表。CF表的格式是csv(逗号分隔的表格)，表头是t1,t2,t3,t4,CF12_34,CF13_24,CF14_23,ngenes。</li>
-<li>可以用建树分析得到的基因树来计算CF；也可以用经过对齐的基因序列，通过TICR管道通过BUCKy估计CF获取CF表（用BUCKy需要考虑基因树估计误差）。</li>
+<li>7Mb的29个物种的snp数据花费大概3-4小时。</li>
 </ul>
-<ol start="2">
-<li>起始树</li>
+<ol start="4">
+<li>结果</li>
 </ol>
 <ul>
-<li>除了CF表外，SNaQ还需要一棵起始树作为优化起点。</li>
+<li>sample_BBAA.txt：不参考-t的拓扑，尝试推断树拓扑的统计量。对每个三重奏进行排序，假设正确的树是 BBAA 模式比不一致的 ABBA和BABA 模式更常见；ABBA和BABA 模式被认为是由不完整的谱系分类或基因渗入引起的。</li>
+<li>sample_Dmin.txt：不管关于树拓扑的任何假设，不尝试推断树拓扑，而是输出每个三物种组合的最小D值统计量。三物种组合的排序是为了使nABBA和nBABA之间的差异最小。</li>
+<li>sample_tree.txt：如果-t指定物种树则输出此项；与指定树一致的排序的D和f4-ratio统计量。这个文件是后续f-branch分析的输入文件。</li>
+<li>sample.txt</li>
+<li>sample_combine_stderr.txt和sample_combine.txt：用作DtriosCombine的输入，如果不需要可删除</li>
+</ul>
+<ol start="5">
+<li>notes</li>
+</ol>
+<ul>
+<li>sample_BBAA.txt，sample_Dmin.txt，sample_tree.txt三个文件结构一致，是在不同树的假设前提下（意味着三物种组合的不同排序）的统计量输出。</li>
+<li>三个文件包含三物种组合的D统计量，Z-score，未调整的p-value，f4-ratio，三种模式（BBAA,ABBA,BABA）的计数。</li>
+<li>三个文件的D统计量总是正值，因为软件对P1和P2进行排序，使得nABBA&gt;&#x3D;nBABA。</li>
+<li>Dtrios模块使用标准block-jackknife程序获得一个近似正态分布的标准误差，在三个文件中，计算Z-scores的公式是Z&#x3D;D&#x2F;std_err(D)，并输出p-values。</li>
+<li>可以用p-values&lt;0.05&#x2F;0.01来筛选D显著大于0的三联体，当D显著大于0，代表P2与P3间有基因流。</li>
+</ul>
+<h2 id="3-3-计算和绘制f-branch"><a href="#3-3-计算和绘制f-branch" class="headerlink" title="3.3. 计算和绘制f-branch"></a>3.3. 计算和绘制f-branch</h2><h3 id="3-3-1-计算f-branch值"><a href="#3-3-1-计算f-branch值" class="headerlink" title="3.3.1. 计算f-branch值"></a>3.3.1. 计算f-branch值</h3><p><code>Dsuite Fbranch</code>是一种启发式方法，执行f-branch计算，用于解释f4-ratio相关结果。</p>
+<ol>
+<li><p>用法<br><code>Dsuite Fbranch species.newick sample_tree.txt &gt;fbranch.out</code></p>
+</li>
+<li><p>输入</p>
+</li>
+</ol>
+<ul>
+<li>species.newick：指定物种树文件</li>
+<li>sample_tree.txt：需要Dsuite Dtrios并-t指定树拓扑得到的结果文件sample_tree.txt作为输入</li>
+</ul>
+<ol start="3">
+<li>结果</li>
+</ol>
+<ul>
+<li>fbranch.out：f-branch统计量保存成矩阵格式</li>
+</ul>
+<h3 id="3-3-2-绘制f-branch图"><a href="#3-3-2-绘制f-branch图" class="headerlink" title="3.3.2. 绘制f-branch图"></a>3.3.2. 绘制f-branch图</h3><p>用dtools.py脚本绘制f-branch图</p>
+<ol>
+<li>用法<br><code>Dsuite/utils/dtools.py fbranch.out species.newick --outgroup Outgroup --use_distances --dpi 1200 --tree-label-size 30</code></li>
+</ol>
+<p>如果运行log出现Plotting fbranch… Saving plots可以看到图片文件生成，即使报错Segmentation fault (core dumped)也没关系。</p>
+<ol start="2">
+<li>输入和常用参数</li>
+</ol>
+<ul>
+<li>fbranch.out：指定fbranch文件</li>
+<li>species.newick：指定物种树文件</li>
+<li>–outgroup：指定外类群（与fbranch.out和species.newick一致，一般是Outgroup）</li>
+<li>–use_distances：画树时使用newick文件里节点距离</li>
+<li>–dpi：设置png分辨率，有些期刊投稿要求1200，800，600不等；最好高点。</li>
+<li>–tree-label-size：设置树节点标签大小</li>
+</ul>
+<ol start="3">
+<li>结果<br>画Fbranch的图，得到fbranch.svg和fbranch.png；</li>
+</ol>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/bioinfo_geneflow.Dsuite_fbranch.png?raw=true" width=90% title="f-branch示意图(A4,(A3,(A2,A1)B)C)D" align=center/>
+
+<p><strong><p align="center">Figure 1. f-branch示意图</p></strong></p>
+<h1 id="4-reference"><a href="#4-reference" class="headerlink" title="4. reference"></a>4. reference</h1><ol>
+<li>Dsuite github：<a href="https://github.com/millanek/Dsuite">https://github.com/millanek/Dsuite</a></li>
+<li>Dsuite paper：<a href="https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13265">https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13265</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>gene flow</category>
+      </categories>
+      <tags>
+        <tag>gene flow</tag>
+        <tag>hybridization</tag>
+        <tag>introgressive</tag>
+        <tag>Dsuite</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>系统发育网络推断 —— PhyloNetworks</title>
+    <url>/2022/04/14/bioinfo_geneflow_PhyloNetworks/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=108998&auto=1&height=32"></iframe></div>
+
+<h1 id="1-PhyloNetworks"><a href="#1-PhyloNetworks" class="headerlink" title="1. PhyloNetworks"></a>1. PhyloNetworks</h1><ol>
+<li>PhyloNetworks简介<ul>
+<li>PhyloNetworks是通过基因树或多位点序列(SNaQ)的最大伪似然进行推断系统发育网络的一个Julia包。</li>
+</ul>
+</li>
+<li>PhyloNetworks原理<ul>
+<li>原理：通过SNaQ来实现网络推断，SNaQ通过估计4分类群子集的最大伪似然来加速运算，估计的网络不受根的影响。</li>
+</ul>
+</li>
+<li>PhyloNetworks输入输出<ul>
+<li>输入：newick格式基因树(多个基因树组成的文件)</li>
+<li>输出：系统发育网络，基因流方向和杂交节点贡献比例</li>
+</ul>
+</li>
+<li>PhyloNetworks优势和不足<ul>
+<li>推断系统发育网络，包括基因流的方向和强度。</li>
+<li>相较于其他推断系统发育网络的软件，PhyloNetworks集成了上游分析，网络估计，引导分析，下游特征进化分析，绘图等功能。</li>
+<li>不足是运行多样本(超过十个个体)和数据量大(超过1000个)会非常耗时(常常以星期&#x2F;月计时)。</li>
+</ul>
+</li>
+<li>PhyloNetworks适用范围<ul>
+<li>PhyloNetworks适用于基因树数据</li>
+<li>适用于居群间或物种间的基因流推测</li>
+<li>适用于推断基因流方向和强度</li>
+</ul>
+</li>
+</ol>
+<h1 id="2-PhyloNetworks安装"><a href="#2-PhyloNetworks安装" class="headerlink" title="2. PhyloNetworks安装"></a>2. PhyloNetworks安装</h1><p>PhyloNetworks是Julia包，Julia是一个类似R的计算机语言，可以交互式或脚本式执行命令。</p>
+<h2 id="2-1-Julia-安装"><a href="#2-1-Julia-安装" class="headerlink" title="2.1. Julia 安装"></a>2.1. Julia 安装</h2><p>如果想简要了解julia，可以看<a href="https://learnxinyminutes.com/docs/julia/">julia tutorials</a>。</p>
+<ol>
+<li><p>预编译软件安装<br>在<a href="https://julialang.org/downloads/">julia software</a>找到对应版本的预编译的软件下载解压缩即可使用。</p>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://julialang-s3.julialang.org/bin/linux/x64/1.7/julia-1.7.2-linux-x86_64.tar.gz #下载linux的64位预编译的julia</span><br><span class="line">tar -xzf julia-1.7.2-linux-x86_64.tar.gz #解压缩</span><br><span class="line">julia-1.7.2/bin/julia -h #查看帮助文章</span><br></pre></td></tr></table></figure>
+</li>
+<li><p>源代码编译安装<br>通过<a href="https://github.com/JuliaLang/julia">julia github</a>获取源代码</p>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/JuliaLang/julia.git #克隆github上的最新版源代码</span><br><span class="line">cd julia</span><br><span class="line">git checkout v1.7.2 #运行checkout来获取julia的最新稳定版本1.7.2</span><br><span class="line">make #编译</span><br><span class="line">./julia -h #查看帮助文档</span><br></pre></td></tr></table></figure></li>
+</ol>
+<p>之后把julia命令的路径添加到环境变量，便可以直接使用julia了。<br>julia与R很像，进入交互界面，只需要在shell里键入<code>julia</code>回车即可，退出交互界面是ctrl+D。</p>
+<h2 id="2-2-安装PhyloNetworks包"><a href="#2-2-安装PhyloNetworks包" class="headerlink" title="2.2. 安装PhyloNetworks包"></a>2.2. 安装PhyloNetworks包</h2><ul>
+<li>进入julia运行<code>using Pkg</code>-<code>Pkg.add(&quot;PhyloNetworks&quot;)</code>即可安装PhyloNetworks包。</li>
+<li>在julia运行<code>Pkg.update()</code>会更新所有julia包。</li>
+<li>PhyloNetworks的配套包PhyloPlots是具有可视化网络和交互操作性的实用程序，例如将网络导出到 R（然后可以通过 R 绘制），建议安装：<code>using Pkg</code>-<code>Pkg.add(&quot;PhyloPlots&quot;)</code></li>
+</ul>
+<h1 id="3-PhyloNetworks分析"><a href="#3-PhyloNetworks分析" class="headerlink" title="3. PhyloNetworks分析"></a>3. PhyloNetworks分析</h1><h2 id="3-1-输入文件"><a href="#3-1-输入文件" class="headerlink" title="3.1. 输入文件"></a>3.1. 输入文件</h2><ol>
+<li>CF表</li>
+</ol>
+<ul>
+<li>输入文件可以有多种形式，不管哪种形式，最终PhyloNetworks的SNaQ用到的都是CF表。</li>
+<li>每个4分类群子集的一致性因子(CF)表，即基因树频率表。CF表的格式是csv(逗号分隔的表格)，表头是t1,t2,t3,t4,CF12_34,CF13_24,CF14_23,ngenes。</li>
+<li>可以用建树分析得到的基因树来计算CF；也可以用经过对齐的基因序列，通过TICR管道通过BUCKy估计CF获取CF表（用BUCKy需要考虑基因树估计误差）。</li>
+</ul>
+<ol start="2">
+<li>起始树</li>
+</ol>
+<ul>
+<li>除了CF表外，SNaQ还需要一棵起始树作为优化起点。</li>
 <li>可以用基因树分析得到的物种树(比如这里用的是ASTRAL分析raxmltrees.tre的结果astraltree.tre)。</li>
 </ul>
 <p>下面介绍两种获取CF表的方式：</p>
@@ -4689,11 +4813,41 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>基因流推断 —— Dsuite</title>
-    <url>/2022/04/10/bioinfo_geneflow_Dsuite/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=171959&auto=1&height=32"></iframe></div>
+    <title>基因流及其推断</title>
+    <url>/2022/04/10/bioinfo_geneflow_intro/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=210766&auto=1&height=32"></iframe></div>
 
-<h1 id="1-Dsuite"><a href="#1-Dsuite" class="headerlink" title="1. Dsuite"></a>1. Dsuite</h1><ol>
+<h1 id="1-基因流-gene-flow-x2F-杂交-hybridization-x2F-渐渗-introgressive"><a href="#1-基因流-gene-flow-x2F-杂交-hybridization-x2F-渐渗-introgressive" class="headerlink" title="1. 基因流(gene flow)&#x2F;杂交(hybridization)&#x2F;渐渗(introgressive)"></a>1. 基因流(gene flow)&#x2F;杂交(hybridization)&#x2F;渐渗(introgressive)</h1><p>基因流，杂交和渐渗通常一起讨论，有些情况下甚至三者在说同一件事。从定义判断，基因流通常发生在种内群体间，杂交则是发生在种间，渐渗是指杂交加回交产生的一种现象。<br>可以这样理解，杂交和渐渗都是基因流的具体结果。</p>
+<ol>
+<li>基因流(gene flow)</li>
+</ol>
+<ul>
+<li>基因流是指遗传物质在不同群体间的流动。</li>
+<li>造成基因流动的原因可能是个体或配子(例如花粉)在群体间的迁徙，或者不同群体间个体的交配等。</li>
+<li>基因流可能发生在同一物种的不同群体间，也可能发生在不同物种间。</li>
+</ul>
+<ol start="2">
+<li>杂交(hybridization)</li>
+</ol>
+<ul>
+<li>杂交指不同物种间通过有性生殖实现配子融合形成下一代的过程。</li>
+</ul>
+<ol start="3">
+<li>渐渗(introgressive)</li>
+</ol>
+<ul>
+<li>渐渗是指通过种间杂种与亲本物种之一的反复回交，将遗传物质从一个物种转移到另一个物种的基因库中，是一个长期的过程。</li>
+</ul>
+<h1 id="2-推断基因流-x2F-杂交"><a href="#2-推断基因流-x2F-杂交" class="headerlink" title="2. 推断基因流&#x2F;杂交"></a>2. 推断基因流&#x2F;杂交</h1><p>通常在物种内检测不同地区的群体间是否存在基因流，也可以在物种间检测基因流来判断杂交&#x2F;渐渗，物种间的基因流会导致系统发育树的不稳定或核质冲突等问题，所以可以推断系统发育网络来检测所有物种对的基因流。</p>
+<h2 id="2-1-推断基因流的软件"><a href="#2-1-推断基因流的软件" class="headerlink" title="2.1. 推断基因流的软件"></a>2.1. 推断基因流的软件</h2><ol>
+<li>通过计算Patterson’s D值(ABBA-BABA值)和相关统计量来判断基因流：Dsuite(2020),ADMIXTOOLS(2012),HyDe(2018),ANGSD(2011,2018),POPGENOME(2014,2019),COMP-D(2020)。</li>
+<li>推断系统发育网络：PhyloNetworks(2017),PhyloNet(2008,2018),TreeMix(2012),BEAST2(2017)。</li>
+<li>基于最大似然法：3s(2017)</li>
+<li>基于MCMC算法的：IM, IMA</li>
+</ol>
+<p>有几个软件单独写了博客：</p>
+<h3 id="2-1-1-Dsuite【推荐】"><a href="#2-1-1-Dsuite【推荐】" class="headerlink" title="2.1.1. Dsuite【推荐】"></a>2.1.1. Dsuite【推荐】</h3><p><a href="https://yanzhongsino.github.io/2022/04/10/bioinfo_geneflow_Dsuite/">Dsuite blog</a></p>
+<ol>
 <li>Dsuite简介<ul>
 <li>Dsuite是通过计算Patterson’s D统计量(即ABBA统计量)和f4等统计量来评估种群间或近缘种间基因流的基于C语言的软件。</li>
 </ul>
@@ -4725,260 +4879,61 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 </ul>
 </li>
 </ol>
-<h1 id="2-Dsuite-install"><a href="#2-Dsuite-install" class="headerlink" title="2. Dsuite install"></a>2. Dsuite install</h1><ol>
-<li>安装Dsuite主程序<br>Dsuite是C编写的，需要编译；需要GCC(&gt;&#x3D;4.9.0)和zlib压缩库。<br>编译后可执行文件在Dsuite&#x2F;Build&#x2F;目录下，可以加到环境变量中或使用绝对路径。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/millanek/Dsuite.git</span><br><span class="line">cd Dsuite</span><br><span class="line">make</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>安装python3 Fbranch绘图脚本【可选但推荐】<br>如果要绘制f-branch计算结果，则需要安装这个绘图脚本。注意<code>--prefix=</code>后没有任何内容。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd utils</span><br><span class="line">python3 setup.py install --user --prefix=</span><br></pre></td></tr></table></figure>
-
-<h1 id="3-Dsuite分析"><a href="#3-Dsuite分析" class="headerlink" title="3. Dsuite分析"></a>3. Dsuite分析</h1><h2 id="3-1-输入文件"><a href="#3-1-输入文件" class="headerlink" title="3.1. 输入文件"></a>3.1. 输入文件</h2><ol>
-<li>sample.snp.vcf.gz<ul>
-<li>基因组尺度的snp文件，vcf格式，可用bgzip压缩</li>
+<h3 id="2-1-2-PhyloNetworks"><a href="#2-1-2-PhyloNetworks" class="headerlink" title="2.1.2. PhyloNetworks"></a>2.1.2. PhyloNetworks</h3><p><a href="https://yanzhongsino.github.io/2022/04/14/bioinfo_geneflow_PhyloNetworks/">PhyloNetworks blog</a></p>
+<ol>
+<li>PhyloNetworks简介<ul>
+<li>PhyloNetworks是通过基因树或多位点序列(SNaQ)的最大伪似然进行推断系统发育网络的一个Julia包。</li>
 </ul>
 </li>
-<li>sets.txt<ul>
-<li>包括两列，tab分隔；第一列个体名，第二列物种名&#x2F;居群名。</li>
-<li>对于Dtrios模块，至少指定一个个体为外类群，外类群个体的第二列写Outgroup，可有多个。</li>
-<li>要注意第二列物种名不能包含短横杠-和句点.字符，否则Dsuite Dtrios虽然不会报错，但Dsuite Fbranch模块运行报错解析不了树文件(ERROR: The tree string could not be parsed correctly)。</li>
+<li>PhyloNetworks原理<ul>
+<li>原理：通过SNaQ来实现网络推断，SNaQ通过估计4分类群子集的最大伪似然来加速运算，估计的网络不受根的影响。</li>
 </ul>
 </li>
-<li>species.newick【optional】<ul>
-<li>Newick格式的居群&#x2F;物种树文件，物种名称与sets.txt对应。</li>
-<li>外类群名称替换成Outgroup，与sets.txt对应。</li>
-<li>树文件必须定根在Outgroup，否则dtools.py报错树文件和franch.out的拓扑不一致。</li>
-<li>去掉支持率；否则Dsuite Fbranch模块运行报错解析不了树文件，ERROR: The tree string could not be parsed correctly。</li>
-<li>枝长最好也去掉，不去枝长也不会被Dsuite用到，还可能增加报错风险。</li>
-<li>这个文件对于Dtrios模块是可选项，但建议加上好画fbranch的图。</li>
+<li>PhyloNetworks输入输出<ul>
+<li>输入：newick格式基因树(多个基因树组成的文件)</li>
+<li>输出：系统发育网络，基因流方向和杂交节点贡献比例</li>
+</ul>
+</li>
+<li>PhyloNetworks优势和不足<ul>
+<li>推断系统发育网络，包括基因流的方向和强度。</li>
+<li>相较于其他推断系统发育网络的软件，PhyloNetworks集成了上游分析，网络估计，引导分析，下游特征进化分析，绘图等功能。</li>
+<li>不足是运行多样本(超过十个个体)和数据量大(超过1000个)会非常耗时(常常以星期&#x2F;月计时)。</li>
+</ul>
+</li>
+<li>PhyloNetworks适用范围<ul>
+<li>PhyloNetworks适用于基因树数据</li>
+<li>适用于居群间或物种间的基因流推测</li>
+<li>适用于推断基因流方向和强度</li>
 </ul>
 </li>
 </ol>
-<h2 id="3-2-Dtrios模块"><a href="#3-2-Dtrios模块" class="headerlink" title="3.2. Dtrios模块"></a>3.2. Dtrios模块</h2><p><code>Dsuite Dtrios</code>用于计算所有三物种组合的D和f4-ratio统计量</p>
+<h3 id="2-1-3-TreeMix"><a href="#2-1-3-TreeMix" class="headerlink" title="2.1.3. TreeMix"></a>2.1.3. TreeMix</h3><p><a href="https://yanzhongsino.github.io/2022/03/20/bioinfo_geneflow_treemix/">TreeMix blog</a></p>
 <ol>
-<li><p>用法<br><code>Dsuite Dtrios sample.snp.vcf.gz sets.txt -t species.newick -o sample</code></p>
+<li>TreeMix简介<ul>
+<li>TreeMix利用等位基因频率来推断群体间分化和杂合（基因流动或基因渗入）</li>
+</ul>
 </li>
-<li><p>输入和常用参数</p>
+<li>TreeMix输入输出<ul>
+<li>输入：基因组snp的vcf文件，和居群系统树(可选optional)</li>
+<li>输出：最佳杂交次数和系统发育网络(包含杂交方向和强度)</li>
+</ul>
 </li>
-</ol>
-<ul>
-<li>sample.snp.vcf.gz：变异文件</li>
-<li>sets.txt：分组文件</li>
-<li>-t species.newick：指定物种树文件</li>
-<li>-o sample：指定输出文件前缀，默认是sets</li>
-<li>-p 5：如果样品中包含pool-seq数据，-p用于设置最小深度，设置后从等位基因深度估计群体的等位基因频率。</li>
-<li>-c：不输出sample_combine_stderr.txt和sample_combine.txt；这两个文件用作DtriosCombine的输入，如果不需要可加上-c不输出这两个文件。</li>
+<li>TreeMix优势和不足<ul>
+<li>TreeMix和PhyloNetworks一样，也是推断系统发育网络。</li>
+<li>我自己用时，有些PhyloNetworks报错无法定根和边缘错误的情况TreeMix可以找到最佳杂交次数。</li>
+<li>不足是比PhyloNetworks更耗时，超级耗时。</li>
 </ul>
-<ol start="3">
-<li>运行耗时</li>
+</li>
+</ol>
+<h3 id="2-1-4-3s"><a href="#2-1-4-3s" class="headerlink" title="2.1.4. 3s"></a>2.1.4. 3s</h3><p><a href="https://yanzhongsino.github.io/2022/09/22/bioinfo_geneflow_3s/">3s blog</a></p>
+<ol>
+<li>3s简介</li>
 </ol>
 <ul>
-<li>7Mb的29个物种的snp数据花费大概3-4小时。</li>
+<li>3s利用似然率来推断两个物种&#x2F;群体间的基因流方向和强度</li>
 </ul>
-<ol start="4">
-<li>结果</li>
-</ol>
-<ul>
-<li>sample_BBAA.txt：不参考-t的拓扑，尝试推断树拓扑的统计量。对每个三重奏进行排序，假设正确的树是 BBAA 模式比不一致的 ABBA和BABA 模式更常见；ABBA和BABA 模式被认为是由不完整的谱系分类或基因渗入引起的。</li>
-<li>sample_Dmin.txt：不管关于树拓扑的任何假设，不尝试推断树拓扑，而是输出每个三物种组合的最小D值统计量。三物种组合的排序是为了使nABBA和nBABA之间的差异最小。</li>
-<li>sample_tree.txt：如果-t指定物种树则输出此项；与指定树一致的排序的D和f4-ratio统计量。这个文件是后续f-branch分析的输入文件。</li>
-<li>sample.txt</li>
-<li>sample_combine_stderr.txt和sample_combine.txt：用作DtriosCombine的输入，如果不需要可删除</li>
-</ul>
-<ol start="5">
-<li>notes</li>
-</ol>
-<ul>
-<li>sample_BBAA.txt，sample_Dmin.txt，sample_tree.txt三个文件结构一致，是在不同树的假设前提下（意味着三物种组合的不同排序）的统计量输出。</li>
-<li>三个文件包含三物种组合的D统计量，Z-score，未调整的p-value，f4-ratio，三种模式（BBAA,ABBA,BABA）的计数。</li>
-<li>三个文件的D统计量总是正值，因为软件对P1和P2进行排序，使得nABBA&gt;&#x3D;nBABA。</li>
-<li>Dtrios模块使用标准block-jackknife程序获得一个近似正态分布的标准误差，在三个文件中，计算Z-scores的公式是Z&#x3D;D&#x2F;std_err(D)，并输出p-values。</li>
-<li>可以用p-values&lt;0.05&#x2F;0.01来筛选D显著大于0的三联体，当D显著大于0，代表P2与P3间有基因流。</li>
-</ul>
-<h2 id="3-3-计算和绘制f-branch"><a href="#3-3-计算和绘制f-branch" class="headerlink" title="3.3. 计算和绘制f-branch"></a>3.3. 计算和绘制f-branch</h2><h3 id="3-3-1-计算f-branch值"><a href="#3-3-1-计算f-branch值" class="headerlink" title="3.3.1. 计算f-branch值"></a>3.3.1. 计算f-branch值</h3><p><code>Dsuite Fbranch</code>是一种启发式方法，执行f-branch计算，用于解释f4-ratio相关结果。</p>
-<ol>
-<li><p>用法<br><code>Dsuite Fbranch species.newick sample_tree.txt &gt;fbranch.out</code></p>
-</li>
-<li><p>输入</p>
-</li>
-</ol>
-<ul>
-<li>species.newick：指定物种树文件</li>
-<li>sample_tree.txt：需要Dsuite Dtrios并-t指定树拓扑得到的结果文件sample_tree.txt作为输入</li>
-</ul>
-<ol start="3">
-<li>结果</li>
-</ol>
-<ul>
-<li>fbranch.out：f-branch统计量保存成矩阵格式</li>
-</ul>
-<h3 id="3-3-2-绘制f-branch图"><a href="#3-3-2-绘制f-branch图" class="headerlink" title="3.3.2. 绘制f-branch图"></a>3.3.2. 绘制f-branch图</h3><p>用dtools.py脚本绘制f-branch图</p>
-<ol>
-<li>用法<br><code>Dsuite/utils/dtools.py fbranch.out species.newick --outgroup Outgroup --use_distances --dpi 1200 --tree-label-size 30</code></li>
-</ol>
-<p>如果运行log出现Plotting fbranch… Saving plots可以看到图片文件生成，即使报错Segmentation fault (core dumped)也没关系。</p>
-<ol start="2">
-<li>输入和常用参数</li>
-</ol>
-<ul>
-<li>fbranch.out：指定fbranch文件</li>
-<li>species.newick：指定物种树文件</li>
-<li>–outgroup：指定外类群（与fbranch.out和species.newick一致，一般是Outgroup）</li>
-<li>–use_distances：画树时使用newick文件里节点距离</li>
-<li>–dpi：设置png分辨率，有些期刊投稿要求1200，800，600不等；最好高点。</li>
-<li>–tree-label-size：设置树节点标签大小</li>
-</ul>
-<ol start="3">
-<li>结果<br>画Fbranch的图，得到fbranch.svg和fbranch.png；</li>
-</ol>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/bioinfo_geneflow.Dsuite_fbranch.png?raw=true" width=90% title="f-branch示意图(A4,(A3,(A2,A1)B)C)D" align=center/>
-
-<p><strong><p align="center">Figure 1. f-branch示意图</p></strong></p>
-<h1 id="4-reference"><a href="#4-reference" class="headerlink" title="4. reference"></a>4. reference</h1><ol>
-<li>Dsuite github：<a href="https://github.com/millanek/Dsuite">https://github.com/millanek/Dsuite</a></li>
-<li>Dsuite paper：<a href="https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13265">https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13265</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>gene flow</category>
-      </categories>
-      <tags>
-        <tag>gene flow</tag>
-        <tag>hybridization</tag>
-        <tag>introgressive</tag>
-        <tag>Dsuite</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>基因流及其推断</title>
-    <url>/2022/04/10/bioinfo_geneflow_intro/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=210766&auto=1&height=32"></iframe></div>
-
-<h1 id="1-基因流-gene-flow-x2F-杂交-hybridization-x2F-渐渗-introgressive"><a href="#1-基因流-gene-flow-x2F-杂交-hybridization-x2F-渐渗-introgressive" class="headerlink" title="1. 基因流(gene flow)&#x2F;杂交(hybridization)&#x2F;渐渗(introgressive)"></a>1. 基因流(gene flow)&#x2F;杂交(hybridization)&#x2F;渐渗(introgressive)</h1><p>基因流，杂交和渐渗通常一起讨论，有些情况下甚至三者在说同一件事。从定义判断，基因流通常发生在种内群体间，杂交则是发生在种间，渐渗是指杂交加回交产生的一种现象。<br>可以这样理解，杂交和渐渗都是基因流的具体结果。</p>
-<ol>
-<li>基因流(gene flow)</li>
-</ol>
-<ul>
-<li>基因流是指遗传物质在不同群体间的流动。</li>
-<li>造成基因流动的原因可能是个体或配子(例如花粉)在群体间的迁徙，或者不同群体间个体的交配等。</li>
-<li>基因流可能发生在同一物种的不同群体间，也可能发生在不同物种间。</li>
-</ul>
-<ol start="2">
-<li>杂交(hybridization)</li>
-</ol>
-<ul>
-<li>杂交指不同物种间通过有性生殖实现配子融合形成下一代的过程。</li>
-</ul>
-<ol start="3">
-<li>渐渗(introgressive)</li>
-</ol>
-<ul>
-<li>渐渗是指通过种间杂种与亲本物种之一的反复回交，将遗传物质从一个物种转移到另一个物种的基因库中，是一个长期的过程。</li>
-</ul>
-<h1 id="2-推断基因流-x2F-杂交"><a href="#2-推断基因流-x2F-杂交" class="headerlink" title="2. 推断基因流&#x2F;杂交"></a>2. 推断基因流&#x2F;杂交</h1><p>通常在物种内检测不同地区的群体间是否存在基因流，也可以在物种间检测基因流来判断杂交&#x2F;渐渗，物种间的基因流会导致系统发育树的不稳定或核质冲突等问题，所以可以推断系统发育网络来检测所有物种对的基因流。</p>
-<h2 id="2-1-推断基因流的软件"><a href="#2-1-推断基因流的软件" class="headerlink" title="2.1. 推断基因流的软件"></a>2.1. 推断基因流的软件</h2><ol>
-<li>通过计算Patterson’s D值(ABBA-BABA值)和相关统计量来判断基因流：Dsuite(2020),ADMIXTOOLS(2012),HyDe(2018),ANGSD(2011,2018),POPGENOME(2014,2019),COMP-D(2020)。</li>
-<li>推断系统发育网络：PhyloNetworks(2017),PhyloNet(2008,2018),TreeMix(2012),BEAST2(2017)。</li>
-<li>基于最大似然法：3s(2017)</li>
-<li>基于MCMC算法的：IM, IMA</li>
-</ol>
-<p>有几个软件单独写了博客：</p>
-<h3 id="2-1-1-Dsuite【推荐】"><a href="#2-1-1-Dsuite【推荐】" class="headerlink" title="2.1.1. Dsuite【推荐】"></a>2.1.1. Dsuite【推荐】</h3><p><a href="https://yanzhongsino.github.io/2022/04/10/bioinfo_geneflow_Dsuite/">Dsuite blog</a></p>
-<ol>
-<li>Dsuite简介<ul>
-<li>Dsuite是通过计算Patterson’s D统计量(即ABBA统计量)和f4等统计量来评估种群间或近缘种间基因流的基于C语言的软件。</li>
-</ul>
-</li>
-<li>Dsuite 原理<ul>
-<li>D值（即ABBA统计量）和f4-ratio统计可以表示为适用于四个分类群的双等位基因SNP：P1,P2,P3,O，拓扑是 (((P1,P2),P3),O)。</li>
-<li>其中外类群O携带祖先等位基因A，衍生等位基因用B表示。BBAA,ABBA,BABA分别代表四个分类群携带等位的三种模式。</li>
-<li>在没有基因流的零假设下，由于具有相同频率的不完全谱系分类，预计P3与P1或P2共享衍生等位基因B的两种模式ABBA和BABA的频率相等，如果ABBA和BABA的频率有显著差异则代表在P3和P1或P2间存在基因渐渗。</li>
-<li>D&#x3D;(nABBA-nBABA)&#x2F;(nABBA+nBABA)；在外群对于祖先等位基因A是固定的（外群中B的频率为0）假设下，D统计量是等位基因模式计数的归一化差异。</li>
-<li>如果外群中衍生等位基因B不为0，则Dsuite的D值是Patterson’s D，适用于无根的四分类群树。</li>
-</ul>
-</li>
-<li>Dsuite输入输出<ul>
-<li>输入：基因组snp的vcf格式文件，居群树文件(可选optional)</li>
-<li>输出：D值统计，f4-ratio统计，f-branch统计，f-branch树矩阵热图</li>
-</ul>
-</li>
-<li>Dsuite优势和不足<ul>
-<li>Dsuite的优势是运行非常快(时间以小时计算)</li>
-<li>不足是Dsuite分析结果不包含基因流的方向</li>
-</ul>
-</li>
-<li>Dsuite适用范围<ul>
-<li>Dsuite适用于基因组学大数据和多样本(超过十个)数据</li>
-<li>适用于居群间或物种间的基因流推测</li>
-<li>即使每个群体只有一个个体也可以推测基因流</li>
-<li>还可以计算pool-seq数据的基因流</li>
-<li>相较其他计算D值软件，Dsuite还同时可以计算f4-ratio和f-branch，以及滑窗统计f相关值。</li>
-</ul>
-</li>
-</ol>
-<h3 id="2-1-2-PhyloNetworks"><a href="#2-1-2-PhyloNetworks" class="headerlink" title="2.1.2. PhyloNetworks"></a>2.1.2. PhyloNetworks</h3><p><a href="https://yanzhongsino.github.io/2022/04/14/bioinfo_geneflow_PhyloNetworks/">PhyloNetworks blog</a></p>
-<ol>
-<li>PhyloNetworks简介<ul>
-<li>PhyloNetworks是通过基因树或多位点序列(SNaQ)的最大伪似然进行推断系统发育网络的一个Julia包。</li>
-</ul>
-</li>
-<li>PhyloNetworks原理<ul>
-<li>原理：通过SNaQ来实现网络推断，SNaQ通过估计4分类群子集的最大伪似然来加速运算，估计的网络不受根的影响。</li>
-</ul>
-</li>
-<li>PhyloNetworks输入输出<ul>
-<li>输入：newick格式基因树(多个基因树组成的文件)</li>
-<li>输出：系统发育网络，基因流方向和杂交节点贡献比例</li>
-</ul>
-</li>
-<li>PhyloNetworks优势和不足<ul>
-<li>推断系统发育网络，包括基因流的方向和强度。</li>
-<li>相较于其他推断系统发育网络的软件，PhyloNetworks集成了上游分析，网络估计，引导分析，下游特征进化分析，绘图等功能。</li>
-<li>不足是运行多样本(超过十个个体)和数据量大(超过1000个)会非常耗时(常常以星期&#x2F;月计时)。</li>
-</ul>
-</li>
-<li>PhyloNetworks适用范围<ul>
-<li>PhyloNetworks适用于基因树数据</li>
-<li>适用于居群间或物种间的基因流推测</li>
-<li>适用于推断基因流方向和强度</li>
-</ul>
-</li>
-</ol>
-<h3 id="2-1-3-TreeMix"><a href="#2-1-3-TreeMix" class="headerlink" title="2.1.3. TreeMix"></a>2.1.3. TreeMix</h3><p><a href="https://yanzhongsino.github.io/2022/03/20/bioinfo_geneflow_treemix/">TreeMix blog</a></p>
-<ol>
-<li>TreeMix简介<ul>
-<li>TreeMix利用等位基因频率来推断群体间分化和杂合（基因流动或基因渗入）</li>
-</ul>
-</li>
-<li>TreeMix输入输出<ul>
-<li>输入：基因组snp的vcf文件，和居群系统树(可选optional)</li>
-<li>输出：最佳杂交次数和系统发育网络(包含杂交方向和强度)</li>
-</ul>
-</li>
-<li>TreeMix优势和不足<ul>
-<li>TreeMix和PhyloNetworks一样，也是推断系统发育网络。</li>
-<li>我自己用时，有些PhyloNetworks报错无法定根和边缘错误的情况TreeMix可以找到最佳杂交次数。</li>
-<li>不足是比PhyloNetworks更耗时，超级耗时。</li>
-</ul>
-</li>
-</ol>
-<h3 id="2-1-4-3s"><a href="#2-1-4-3s" class="headerlink" title="2.1.4. 3s"></a>2.1.4. 3s</h3><p><a href="https://yanzhongsino.github.io/2022/09/22/bioinfo_geneflow_3s/">3s blog</a></p>
-<ol>
-<li>3s简介</li>
-</ol>
-<ul>
-<li>3s利用似然率来推断两个物种&#x2F;群体间的基因流方向和强度</li>
-</ul>
-<ol start="2">
-<li>3s输入</li>
+<ol start="2">
+<li>3s输入</li>
 </ol>
 <ul>
 <li>输入：基因组或其他测序序列phylip文件</li>
@@ -5013,220 +4968,484 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>hybridization</tag>
         <tag>introgressive</tag>
         <tag>3s</tag>
+        <tag>Dsuite</tag>
         <tag>PhyloNetworks</tag>
         <tag>population networks</tag>
         <tag>TreeMix</tag>
-        <tag>Dsuite</tag>
       </tags>
   </entry>
   <entry>
-    <title>树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离</title>
-    <url>/2023/03/13/bioinfo_phylogeny_MDS/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105210&auto=1&height=32"></iframe></div>
+    <title>用OrthoFinder做直系同源推断</title>
+    <url>/2021/12/06/bioinfo_orthology.inference_OrthoFinder2/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=108151&auto=1&height=32"></iframe></div>
 
-<h1 id="1-树空间的多维缩放（multidimensional-scaling，MDS）可视化物种树和基因树的RF距离"><a href="#1-树空间的多维缩放（multidimensional-scaling，MDS）可视化物种树和基因树的RF距离" class="headerlink" title="1. 树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离"></a>1. 树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离</h1><ol>
-<li>简介</li>
-</ol>
+<h1 id="1-直系同源-orthology-推断"><a href="#1-直系同源-orthology-推断" class="headerlink" title="1. 直系同源(orthology)推断"></a>1. 直系同源(orthology)推断</h1><h2 id="1-1-直系同源推断"><a href="#1-1-直系同源推断" class="headerlink" title="1.1. 直系同源推断"></a>1.1. 直系同源推断</h2><p>直系同源(orthology)是指直系同源基因的直系同源这种属性，相关的概念可以参考<a href="https://yanzhongsino.github.io/2021/06/23/concept_homology/">博文-同源性相关概念</a>。</p>
+<p>直系同源推断(orthology inference)常常在多个物种间进行，通常是获取多个物种的直系同源群(orthogroups)和直系同源群间的关系。</p>
+<p>直系同源基因判定的方法有两种：</p>
 <ul>
-<li>当系统发育拓扑冲突时，需要评估基因树与物种树的不一致程度，可使用树空间的多维缩放（multidimensional scaling，MDS）来对树的RF距离进行降维，并在指定维度可视化基因树与物种树之间的RF距离。</li>
+<li>一种是将物种的直系同源基因两两比较，不同物种的每一对都是直系同源，但存在基因重复，使得用这种方法找到的同源关系是不具有传递性的（A和B是同源的，B和C是同源的，但是A和C不是同源的）。multiparanoid和OMA方法都是这个原理。所以需要每个同源基因都在多个集合中存在才行，进行两两比较。</li>
+<li>另一种是识别完整的正交群，一个同源组orthogroup包含所有物种共同祖先基因，同时包含直系／旁系同源基因，OrthoMCL是这个原理，它是通过正交组做推断，用BLAST计算多个物种序列之间的序列相似性得分，然后用MCL聚类算法，识别该数据集中高度相似的序列组。</li>
 </ul>
-<ol start="2">
-<li>分析步骤</li>
-</ol>
+<h2 id="1-2-直系同源基因推断常用软件：OrthoMCL和OrthoFinder"><a href="#1-2-直系同源基因推断常用软件：OrthoMCL和OrthoFinder" class="headerlink" title="1.2. 直系同源基因推断常用软件：OrthoMCL和OrthoFinder"></a>1.2. 直系同源基因推断常用软件：OrthoMCL和OrthoFinder</h2><h3 id="1-2-1-OrthoMCL"><a href="#1-2-1-OrthoMCL" class="headerlink" title="1.2.1. OrthoMCL"></a>1.2.1. OrthoMCL</h3><p>OrthoMCL是2003年发表的工具。发表以后得到广泛使用，但存在一些问题：</p>
 <ul>
-<li>先使用R包ape 5.0计算树之间的成对Robinson-Foulds（RF）距离，然后对RF距离进行多维缩放（multidimensional scaling，MDS），最后绘制指定维度下的RF距离示意图。</li>
+<li>2013年之后就没更新，安装需要使用MySQL。</li>
+<li>在BLAST序列相似度时发现，长序列比短序列更准确，有时会留下较长的质量差的序列，而丢弃较短的单质量好的序列。</li>
+<li>orthoBench是唯一可用的公开的正交群标准数据集，用orthoBench数据集评估orthoMCL性能，发现它对序列长度有依赖性，太长或者太短的序列都不准确。</li>
+<li>recall率有待提高。</li>
 </ul>
-<ol start="3">
-<li>结果</li>
-</ol>
+<h3 id="1-2-2-OrthoFinder"><a href="#1-2-2-OrthoFinder" class="headerlink" title="1.2.2. OrthoFinder"></a>1.2.2. OrthoFinder</h3><p>OrthoFinder是2015年才出现，2019年正式发表的工具。OrthoFinder解决了一些OrthoMCL中存在的问题，展现了优势：</p>
 <ul>
-<li>MDS的二维图如下所示，图中的物种树，包括不同数据和不同软件的物种树（包括ASTRAL_tree，SCGs_ML，SNPs_ML，Plastome_ML树）用其他颜色标示，每个蓝色的点代表一棵基因树，颜色深浅代表支持率平均值的大小。</li>
+<li>持续更新，安装友好，运行速度快。</li>
+<li>OrthoFinder解决了这个问题，它将BLAST评分做了转换，将原来的e值准换成bit值（比特值）来减少序列长度对准确度的影响，因为e值存在阈值，e 小于10的－180次方的都被算作0，而比特值没有阈值。</li>
+<li>OrthoFinder用RBNH代替了传统的RBH提高了recall。</li>
 </ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/MDS_support.png?raw=true" width=70% title="MDS图示例" align=center/>
-
-<p><strong><p align="center">图 1. MDS二维图示例</p></strong></p>
-<h1 id="2-分析过程"><a href="#2-分析过程" class="headerlink" title="2. 分析过程"></a>2. 分析过程</h1><ol>
-<li>输入文件</li>
+<p>这篇博文主要介绍OrthoFinder这个工具。</p>
+<h1 id="2-OrthoFinder"><a href="#2-OrthoFinder" class="headerlink" title="2. OrthoFinder"></a>2. OrthoFinder</h1><h2 id="2-1-OrthoFinder的功能"><a href="#2-1-OrthoFinder的功能" class="headerlink" title="2.1. OrthoFinder的功能"></a>2.1. OrthoFinder的功能</h2><p>OrthoFinder是用于推断系统发育直系同源关系的基于python的软件。OrthoFinder1模块推断直系同源群，OrthoFinder2模块在直系同源群的基础上构建物种系统发育树，步骤如下：</p>
+<ol>
+<li>推断所有物种的直系同源群和直系同源基因。</li>
 </ol>
 <ul>
-<li>species_genes.tre：物种树和基因树合并的树文件，可以合并多棵物种树（比如用不同算法和数据构建的树）。</li>
+<li>用all vs all比对所有输入的物种蛋白序列；</li>
+<li>RBNHs方法确定阈值；</li>
+<li>MCL聚类获得直系同源组。</li>
 </ul>
 <ol start="2">
-<li>用R包ape计算基因树成对间的Robinson-Foulds（RF）距离</li>
+<li>推断找到的所有直系同源基因的有根基因树。</li>
 </ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ape<span class="punctuation">)</span> <span class="comment"># 载入ape包</span></span><br><span class="line">trees<span class="operator">&lt;-</span>read.tree<span class="punctuation">(</span><span class="string">&quot;species_genes.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取树</span></span><br><span class="line">trees_dist<span class="operator">&lt;-</span>dist.topo<span class="punctuation">(</span>trees<span class="punctuation">,</span> method <span class="operator">=</span> <span class="string">&quot;PH85&quot;</span><span class="punctuation">)</span> <span class="comment"># 计算RF，trees_dist的数据类别是dist</span></span><br><span class="line">trees_matric<span class="operator">&lt;-</span>as.matrix<span class="punctuation">(</span>trees_dist<span class="punctuation">)</span> <span class="comment"># 转化dist到矩阵matrix</span></span><br><span class="line">write.table <span class="punctuation">(</span>trees_matric<span class="punctuation">,</span> file <span class="operator">=</span><span class="string">&quot;RF_pairs.tsv&quot;</span><span class="punctuation">,</span> sep <span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment"># 保存矩阵数据到RF_pairs.tsv文件</span></span><br></pre></td></tr></table></figure>
-
 <ul>
-<li>RF_pairs.tsv文件是行和列都是tree1,tree2,…,treen等依次命名的树之间的RF距离矩阵。</li>
+<li>为每个直系同源组构建基因系统发育树；</li>
+<li>使用STAG算法从无根基因树上构建无根物种树；</li>
+<li>使用STRIDE算法构建有根物种树；</li>
+<li>有根物种树进一步辅助构建有根基因树。</li>
 </ul>
 <ol start="3">
-<li>MDS多维缩放</li>
+<li>用基因树推断基因间的所有直系同源群间的关系。</li>
+<li>推断基因复制事件并交叉引用到对应的基因树和物种树上的对应节点。</li>
+<li>提供比较基因组学统计结果。</li>
 </ol>
-<ul>
-<li>经典MDS法</li>
-</ul>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">mds <span class="operator">&lt;-</span> cmdscale<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> eig<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br><span class="line">mds.var.per <span class="operator">&lt;-</span> <span class="built_in">round</span><span class="punctuation">(</span>mds<span class="operator">$</span>eig<span class="operator">/</span><span class="built_in">sum</span><span class="punctuation">(</span>mds<span class="operator">$</span>eig<span class="punctuation">)</span><span class="operator">*</span><span class="number">100</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">)</span></span><br><span class="line">mds.values <span class="operator">&lt;-</span> mds<span class="operator">$</span>points</span><br><span class="line">mds.data <span class="operator">&lt;-</span> data.frame<span class="punctuation">(</span>Sample<span class="operator">=</span>rownames<span class="punctuation">(</span>mds.values<span class="punctuation">)</span><span class="punctuation">,</span> X<span class="operator">=</span>mds.values<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">,</span> Y<span class="operator">=</span>mds.values<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">2</span><span class="punctuation">]</span><span class="punctuation">)</span></span><br><span class="line">write.table<span class="punctuation">(</span>mds.data<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;mds.tsv&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment"># 把MDS的二维值保存到mds.tsv</span></span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>mds.tsv文件示例。每棵树都有一行数据，这里是二维的，包含MDS1和MDS2两个值。用tree1,tree2,…,treen等依次命名，与species_genes.tre树文件中树的顺序一致。如果合并了物种树和基因树，可以依据顺序在mds.tsv中找到物种树对应的行。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&quot;Sample&quot;	&quot;MDS1&quot;	&quot;MDS2&quot;</span><br><span class="line">&quot;tree1&quot;	&quot;tree1&quot;	-15.2853071675022	-9.23665453167777</span><br><span class="line">&quot;tree2&quot;	&quot;tree2&quot;	-13.8416686613703	-7.78853307096205</span><br></pre></td></tr></table></figure>
+<img src="https://github.com/davidemms/OrthoFinder/raw/master/assets/Workflow.png" width=100% title="The OrthoFinder workflow" alt="The OrthoFinder workflow" align=center/>
 
-<ul>
-<li>nonmetric MDS法</li>
+<p><strong><p align="center">Figure 1. The OrthoFinder workflow</strong><br>from <a href="https://github.com/davidemms/OrthoFinder">github: orthofinder</a></p></p>
+<h2 id="2-2-OrthoFinder的分步原理"><a href="#2-2-OrthoFinder的分步原理" class="headerlink" title="2.2. OrthoFinder的分步原理"></a>2.2. OrthoFinder的分步原理</h2><h3 id="2-2-1-OrthoFinder1推断直系同源群的基本原理"><a href="#2-2-1-OrthoFinder1推断直系同源群的基本原理" class="headerlink" title="2.2.1. OrthoFinder1推断直系同源群的基本原理"></a>2.2.1. OrthoFinder1推断直系同源群的基本原理</h3><ul>
+<li>首先BLAST all vs all 做所有物种蛋白序列的比对。选best hits Top5%，用比特值代替e值，用最小二乘法将Top5%的分数值拟合成一个线性模型，将比特值用这个模型转换之后，较长的质量差的hit将不再优于短的质量好的序列；</li>
+<li>然后将基因长度和系统发育距离标准化。用的是RBNH方法，传统的是用RBH方法，它是一种高精度鉴定直系同源基因对的方法，用BLAST best hit的倒数做的，但是RBNH是用标准话的比特值来做的。</li>
 </ul>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>MASS<span class="punctuation">)</span></span><br><span class="line">mds <span class="operator">&lt;-</span> isoMDS<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br></pre></td></tr></table></figure>
-
-<ol start="4">
-<li>绘图</li>
+<h3 id="2-2-2-OrthoFinder1推断直系同源群的工作流程"><a href="#2-2-2-OrthoFinder1推断直系同源群的工作流程" class="headerlink" title="2.2.2. OrthoFinder1推断直系同源群的工作流程"></a>2.2.2. OrthoFinder1推断直系同源群的工作流程</h3><p>OrthoFinder1推断直系同源群的分析过程分为如下几步:</p>
+<ol>
+<li>BLAST all-vs-all搜索。使用BLASTP以evalue&#x3D;10e-3进行搜索，寻找潜在的同源基因。(除了BLAST, 还可以选择DIAMOND和MMSeq2等其他搜索工具)。</li>
+<li>基于基因长度和系统发育距离对BLAST bit得分进行标准化。</li>
+<li>使用RBNHs方法，确定一个同源组序列性相似度的阈值，筛选出质量好的直系同源组(orthogroup graph)。</li>
+<li>构建直系同源组图(orthogroup graph)，通过归一化的比特值画出正交组的边缘连接，用作MCL的输入。</li>
+<li>使用MCL对基因进行聚类，划分直系同源组。</li>
 </ol>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ggplot2<span class="punctuation">)</span></span><br><span class="line">ggplot<span class="punctuation">(</span>data<span class="operator">=</span>mds.data<span class="punctuation">,</span> aes<span class="punctuation">(</span>x<span class="operator">=</span>X<span class="punctuation">,</span> y<span class="operator">=</span>Y<span class="punctuation">,</span> label<span class="operator">=</span>Sample<span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  geom_text<span class="punctuation">(</span><span class="punctuation">)</span> <span class="operator">+</span></span><br><span class="line">  xlab<span class="punctuation">(</span>paste<span class="punctuation">(</span><span class="string">&quot;MDS1 - &quot;</span><span class="punctuation">,</span> mds.var.per<span class="punctuation">[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">,</span> <span class="string">&quot;%&quot;</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  ylab<span class="punctuation">(</span>paste<span class="punctuation">(</span><span class="string">&quot;MDS2 - &quot;</span><span class="punctuation">,</span> mds.var.per<span class="punctuation">[</span><span class="number">2</span><span class="punctuation">]</span><span class="punctuation">,</span> <span class="string">&quot;%&quot;</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  theme_bw<span class="punctuation">(</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  ggtitle<span class="punctuation">(</span><span class="string">&quot;MDS plot using Euclidean distance&quot;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<p>或者</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">mds <span class="operator">&lt;-</span> cmdscale<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> eig<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br><span class="line">x <span class="operator">&lt;-</span> mds<span class="operator">$</span>points<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">]</span></span><br><span class="line">y <span class="operator">&lt;-</span> mds<span class="operator">$</span>points<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">2</span><span class="punctuation">]</span></span><br><span class="line">plot<span class="punctuation">(</span>x<span class="punctuation">,</span> y<span class="punctuation">,</span> xlab<span class="operator">=</span><span class="string">&quot;Coordinate 1&quot;</span><span class="punctuation">,</span> ylab<span class="operator">=</span><span class="string">&quot;Coordinate 2&quot;</span><span class="punctuation">,</span> main<span class="operator">=</span><span class="string">&quot;Metric MDS&quot;</span><span class="punctuation">,</span> type<span class="operator">=</span><span class="string">&quot;n&quot;</span><span class="punctuation">)</span></span><br><span class="line">text<span class="punctuation">(</span>mds<span class="operator">$</span>points<span class="punctuation">,</span> labels <span class="operator">=</span> rownames<span class="punctuation">(</span>trees_matric<span class="punctuation">)</span><span class="punctuation">,</span> cex <span class="operator">=</span> <span class="number">0.6</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<p>结果图保存成pdf后可以在pdf编辑器里把物种树对应的点用更大尺寸和其他颜色标示出来。</p>
-<ol start="5">
-<li>建议</li>
+<h3 id="2-2-3-OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程"><a href="#2-2-3-OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程" class="headerlink" title="2.2.3. OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程"></a>2.2.3. OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程</h3><ol>
+<li>为每个直系同源组构建基因系统发育树；</li>
+<li>使用STAG算法从无根基因树上构建无根物种树；</li>
+<li>使用STRIDE算法构建有根物种树；</li>
+<li>有根物种树进一步辅助构建有根基因树；</li>
+<li>基于DLC(Duplication-Loss-Coalescent)分析模型，有根基因树可以用来推断物种形成和基因复制事件；</li>
+<li>最后把结果记录在统计信息中。</li>
+</ol>
+<h2 id="2-3-OrthoFinder使用"><a href="#2-3-OrthoFinder使用" class="headerlink" title="2.3. OrthoFinder使用"></a>2.3. OrthoFinder使用</h2><h3 id="2-3-1-OrthoFinder安装"><a href="#2-3-1-OrthoFinder安装" class="headerlink" title="2.3.1. OrthoFinder安装"></a>2.3.1. OrthoFinder安装</h3><ol>
+<li><p>conda安装【推荐】<br>conda安装一次解决依赖问题：<code>conda install orthofinder</code></p>
+</li>
+<li><p>下载解压缩<br><a href="https://github.com/davidemms/OrthoFinder/releases">OrthoFinder最新版下载地址</a></p>
+</li>
 </ol>
+<p>下载后解压缩：<code>tar xzf OrthoFinder_source.tar.gz</code> or <code>tar xzf OrthoFinder.tar.gz</code></p>
+<p>即可使用：<code>python OrthoFinder_source/orthofinder.py -h</code> or <code>./OrthoFinder/orthofinder -h</code></p>
+<h3 id="2-3-2-OrthoFinder运行"><a href="#2-3-2-OrthoFinder运行" class="headerlink" title="2.3.2. OrthoFinder运行"></a>2.3.2. OrthoFinder运行</h3><h4 id="2-3-2-1-输入文件"><a href="#2-3-2-1-输入文件" class="headerlink" title="2.3.2.1. 输入文件"></a>2.3.2.1. 输入文件</h4><p>把需要分析的物种的组学蛋白序列文件(.fa,.faa,.fasta,.fas,.pep后缀)放在同一个目录下(例如workdirectory目录下)，然后用OrthoFinder的-f参数指定目录做分析。</p>
+<p>如果是下载的蛋白数据，使用最长转录本的蛋白序列即可。</p>
+<h4 id="2-3-2-2-简单版"><a href="#2-3-2-2-简单版" class="headerlink" title="2.3.2.2. 简单版"></a>2.3.2.2. 简单版</h4><p><code>orthofinder -f ./workdirectory</code> # -f指定数据目录</p>
+<h4 id="2-3-2-3-参数版"><a href="#2-3-2-3-参数版" class="headerlink" title="2.3.2.3. 参数版"></a>2.3.2.3. 参数版</h4><p><code>orthofinder -f ./workdirectory -t 24 -a 8 -M msa -S blast -A mafft -T raxml-ng</code></p>
 <ul>
-<li>还可以把每个基因树和物种树的支持率的平均值计算出来之后，用支持率给每棵树的MSD点上色，从而查看是否是离物种树越近的基因树的平均支持率越高。</li>
-<li>也可以用基因树的平均支持率和基因树离物种树的RF距离画散点图，并做线性回归分析判断相关性。</li>
+<li>-t指定比对线程;</li>
+<li>-a指定分析线程;</li>
+<li>-M指定推断基因树的方法: dendroblast(default)&#x2F;msa;</li>
+<li>-S指定比对软件: diamond(default)&#x2F;blast&#x2F;blast_gz&#x2F;mmseqs&#x2F;blast_nucl</li>
+<li>-A指定多序列比对(MSA)使用软件：mafft(default)&#x2F;muscle；需要指定-M msa</li>
+<li>-T指定画树软件：fasttree(default)&#x2F;raxml&#x2F;raxml-ng&#x2F;iqtree</li>
 </ul>
-<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
-<li>MDS paper：<a href="https://academic.oup.com/sysbio/article/67/3/400/4175806">https://academic.oup.com/sysbio/article/67/3/400/4175806</a></li>
+<h4 id="2-3-2-4-修改参数文件config-json"><a href="#2-3-2-4-修改参数文件config-json" class="headerlink" title="2.3.2.4. 修改参数文件config.json"></a>2.3.2.4. 修改参数文件config.json</h4><ol>
+<li>如果需要做更细致的参数设定，比如对调用软件blast的参数evalue阈值的修改，可以通过修改参数文件config.json来实现。</li>
+<li>config.json文件位置：下载安装的在安装目录下，conda安装的在conda的bin目录下。</li>
+<li>推荐修改值：</li>
 </ol>
-<hr>
 <ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+<li>raxml-ng参数修改为–model LG+G8+F –threads 2 –bs-trees 1000（default是–model LG+G4 –threads 1)；</li>
+<li>blastp和blastn的参数修改为 -evalue 0.00001（default是0.001）</li>
 </ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>phylogeny</category>
-      </categories>
-      <tags>
-        <tag>phylogeny</tag>
-        <tag>ggplot2</tag>
-        <tag>R packages</tag>
-        <tag>multidimensional scaling</tag>
-        <tag>MDS</tag>
-        <tag>成对Robinson-Foulds距离</tag>
-        <tag>ape</tag>
-        <tag>gene tree</tag>
-        <tag>species tree</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用PlantTribes做直系同源推断</title>
-    <url>/2024/09/19/bioinfo_orthology.inference_PlantTribes/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1959437612&auto=1&height=32"></iframe></div>
-
-<h1 id="1-直系同源-orthology-推断"><a href="#1-直系同源-orthology-推断" class="headerlink" title="1. 直系同源(orthology)推断"></a>1. 直系同源(orthology)推断</h1><h2 id="1-1-直系同源推断"><a href="#1-1-直系同源推断" class="headerlink" title="1.1. 直系同源推断"></a>1.1. 直系同源推断</h2><p>直系同源(orthology)是指直系同源基因的直系同源这种属性，相关的概念可以参考<a href="https://yanzhongsino.github.io/2021/06/23/concept_homology/">博文-同源性相关概念</a>。</p>
-<p>直系同源推断(orthology inference)常常在多个物种间进行，通常是获取多个物种的直系同源群(orthogroups)和直系同源群间的关系。</p>
-<p>直系同源基因判定的方法有两种：</p>
+<h4 id="2-3-2-5-RESTART"><a href="#2-3-2-5-RESTART" class="headerlink" title="2.3.2.5. RESTART"></a>2.3.2.5. RESTART</h4><p>如果需要重新运行，可以使用以下参数设定从哪个步骤开始运行。</p>
 <ul>
-<li>一种是将物种的直系同源基因两两比较，不同物种的每一对都是直系同源，但存在基因重复，使得用这种方法找到的同源关系是不具有传递性的（A和B是同源的，B和C是同源的，但是A和C不是同源的）。multiparanoid和OMA方法都是这个原理。所以需要每个同源基因都在多个集合中存在才行，进行两两比较。</li>
-<li>另一种是识别完整的正交群，一个同源组orthogroup包含所有物种共同祖先基因，同时包含直系／旁系同源基因，OrthoMCL是这个原理，它是通过正交组做推断，用BLAST计算多个物种序列之间的序列相似性得分，然后用MCL聚类算法，识别该数据集中高度相似的序列组。</li>
+<li>-b  <dir> # Start OrthoFinder from pre-computed BLAST results in <dir></li>
+<li>-fg <dir> # Start OrthoFinder from pre-computed orthogroups in <dir></li>
+<li>-ft <dir> # Start OrthoFinder from pre-computed gene trees in <dir></li>
 </ul>
-<p>这篇博文主要介绍一个新工具PlantTribes2。</p>
-<h1 id="2-PlantTribes2介绍"><a href="#2-PlantTribes2介绍" class="headerlink" title="2. PlantTribes2介绍"></a>2. PlantTribes2介绍</h1><p>PlantTribes 是用于比较植物基因组学的自动化基因家族分析流程的集合。PlantTribes通过模块对植物基因组中完整蛋白质序列聚类来进行比较进化研究。 模块功能包括：将从头组装的转录本处理为推断的编码序列及其相应的氨基酸翻译序列，局部组装目标基因家族，推断直系同源基因，并进行基因家族多序列比对和构建相应的系统发育树，以及估算一组基因序列的旁系&#x2F;直系同源的成对同义&#x2F;非同义替换率。</p>
-<h1 id="3-PlantTribes-scaffold数据库"><a href="#3-PlantTribes-scaffold数据库" class="headerlink" title="3. PlantTribes_scaffold数据库"></a>3. PlantTribes_scaffold数据库</h1><p>PlantTribes2 (v1.0.4) 基于过去发表的系统基因组学研究提供了几个植物基因家族scaffolds数据库，包含了预先构建的多序列比对和orthogroups，下载网址是：<a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/</a></p>
 <ol>
-<li>单子叶植物数据库：12Gv1.0（代表12个物种的scaffolds）</li>
-<li>被子植物数据库的四个版本：22Gv1.1, 26Gv1.0, 26Gv2.0, and 37Gv1.0</li>
+<li>添加新物种<br>运行之后想要添加物种可以用<code>orthofinder -b previous_orthofinder_directory -f new_fasta_directory</code>而无需重新运行先前计算的 BLAST 搜索。</li>
 </ol>
-<h1 id="4-PlantTribes2的模块"><a href="#4-PlantTribes2的模块" class="headerlink" title="4. PlantTribes2的模块"></a>4. PlantTribes2的模块</h1><p>PlantTribes2一共有7个模块，每个模块是独立的功能，也可以联合使用。</p>
-<img src="https://www.frontiersin.org/files/Articles/1011199/fpls-13-1011199-HTML-r1/image_m/fpls-13-1011199-g001.jpg" width=80% title="PlantTribes2 analysis workflow" alt="PlantTribes2 analysis workflow" align=center/>
-
-<p><strong><p align="center">Figure 1. PlantTribes2 analysis workflow</strong><br>from <a href="https://www.frontiersin.org/files/Articles/1011199/fpls-13-1011199-HTML-r1/image_m/fpls-13-1011199-g001.jpg">PlantTribes2 analysis workflow</a></p></p>
-<h2 id="4-1-AssemblyPostProcessor模块"><a href="#4-1-AssemblyPostProcessor模块" class="headerlink" title="4.1. AssemblyPostProcessor模块"></a>4.1. AssemblyPostProcessor模块</h2><ol>
-<li>运行 ESTScan 编码区域预测</li>
+<p>这会将“new_fasta_directory”中的每个物种添加到现有物种集中，重用所有以前的 BLAST 结果，仅执行新物种所需的新 BLAST 搜索并重新计算正交群。’previous_orthofinder_directory’ 是包含文件 ‘SpeciesIDs.txt’ 的 OrthoFinder ‘WorkingDirectory&#x2F;‘。</p>
+<ol start="2">
+<li>移除物种<br>OrthoFinder 允许您从以前的分析中删除物种。<br>在先前分析的“WorkingDirectory&#x2F;”中有一个名为“SpeciesIDs.txt”的文件。使用“#”字符注释掉要从分析中删除的任何物种，然后使用以下命令运行 OrthoFinder：<code>orthofinder -b previous_orthofinder_directory</code></li>
+</ol>
+<p>其中 ‘previous_orthofinder_directory’ 是包含文件 ‘SpeciesIDs.txt’ 的 OrthoFinder ‘WorkingDirectory&#x2F;‘。</p>
+<ol start="3">
+<li>同时添加和删除物种<br>前面两个选项可以结合起来，如上所述注释掉要删除的物种，然后使用命令：<br><code>orthofinder -b previous_orthofinder_directory -f new_fasta_directory</code></li>
+</ol>
+<h4 id="2-3-2-6-结果文件"><a href="#2-3-2-6-结果文件" class="headerlink" title="2.3.2.6. 结果文件"></a>2.3.2.6. 结果文件</h4><p>OrthoFinder的标准输出包括：直系同源组，直系同源基因，有根基因树，解析基因树，无根物种树、有根物种树，基因重复事件以及相关的统计数据。</p>
+<ol>
+<li><p><strong>Phylogenetic_Hierarchical_Orthogroups 文件夹</strong></p>
+<p> 从 2.4.0 版本开始，主要文件储存在这个目录下面，orthogroups目录被弃用。</p>
+<p> OrthoFinder 通过分析有根基因树来推断每个层次级别（即物种树中的每个节点）的 HOG、正交群。这是一种比所有其他方法使用的和以前由 OrthoFinder 使用的基于基因相似性&#x2F;图形的方法（已弃用的 Orthogroups&#x2F;Orthogroups.tsv 文件）更准确的正交群推断方法。</p>
+<p> 根据 Orthobench 基准，这些新的正交群比 OrthoFinder 2 正交群 (Orthogroups&#x2F;Orthogroups.tsv) 准确 12%。通过包括外群物种，可以进一步提高准确度（在 Orthobench 上准确度提高 20%），这有助于解释有根基因树。</p>
+</li>
 </ol>
 <ul>
-<li><code>PlantTribes/pipelines/AssemblyPostProcessor  --transcripts transcripts.fasta --prediction_method estscan --score_matrices /path/to/score/matrices/Arabidopsis_thaliana.smat</code></li>
+<li>参阅“Species_Tree&#x2F;SpeciesTree_rooted_node_labels.txt”以确定哪个 N?.tsv 文件包含您需要的正交群。</li>
+<li>N0.tsv是制表符分隔的文本文件。每行包含属于单个正交群的基因。来自每个正交群的基因被组织成列，每个物种一个。附加列给出了 HOG（分层正交群）ID 和基因树中确定 HOG 的节点（注意，这可能位于包含基因的进化枝的根上方）。该文件有效地替换了使用 MCL 进行马尔可夫聚类的Orthogroups&#x2F;Orthogroups.tsv 中的正交群。</li>
+<li>N1.txt, N2.tsv, … : Orthogroups 从对应物种树 N1、N2 等物种进化枝的基因树推断出来。现在可以在分析中包含外群物种，然后使用 HOG 文件获取为物种树中所选进化枝定义的正交群。</li>
 </ul>
-<h2 id="4-2-GeneFamilyClassifier模块"><a href="#4-2-GeneFamilyClassifier模块" class="headerlink" title="4.2. GeneFamilyClassifier模块"></a>4.2. GeneFamilyClassifier模块</h2><ol>
-<li>用22Gv1.1 scaffolds，orthomcl 聚类法 和blastp 分类器运行</li>
+<ol start="2">
+<li><strong>Orthologues 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>PlantTribes/pipelines/GeneFamilyClassifier --proteins proteins.fasta --scaffold 22Gv1.1 --method orthomcl --classifier blastp</code></li>
+<li>Orthologues 目录包含每个物种的一个子目录，该子目录又包含每个成对物种比较的文件，列出该物种对之间的直向同源物。</li>
+<li>直向同源物可以是一对一、一对多或多对多，这取决于直向同源物分化后的基因复制事件。</li>
+<li>文件中的每一行都包含一个物种中的基因，这些基因是另一个物种中基因的直向同源物，并且每一行都与包含这些基因的直向群交叉引用。</li>
 </ul>
-<h2 id="4-3-PhylogenomicsAnalysis模块-legacy-pipeline"><a href="#4-3-PhylogenomicsAnalysis模块-legacy-pipeline" class="headerlink" title="4.3. PhylogenomicsAnalysis模块 (legacy pipeline)"></a>4.3. PhylogenomicsAnalysis模块 (legacy pipeline)</h2><ol>
-<li>用22Gv1.1 scaffolds，orthomcl 聚类法 和raxml建树法运行</li>
+<ol start="3">
+<li><strong>Orthogroups 文件夹【2.4.0版本后弃用】</strong><br>从 2.4.0 版本开始，主要文件储存在Phylogenetic_Hierarchical_Orthogroups目录下面，orthogroups目录被弃用。</li>
 </ol>
 <ul>
-<li><code>PlantTribes/pipelines/PhylogenomicsAnalysis --orthogroup_faa geneFamilyClassification_dir/orthogroups_fasta --scaffold 22Gv1.1  --method orthomcl  --add_alignments  --tree_inference raxml</code></li>
+<li>Orthogroups.tsv【2.4.0版本后弃用】：制表符分隔的文本文件。每行包含属于单个正交群的基因。来自每个正交群的基因被组织成列，每个物种一个。应改用 Phylogenetic_Hierarchical_Orthogroups&#x2F;N0.tsv 中的正交群。</li>
+<li>Orthogroups.txt【旧格式】：包含 Orthogroups.tsv 文件内容的 OrthoMCL 输出格式文件，列间用空格分隔。第一列是OG开头的orthogroups的ID号，冒号结束；后面不同数量的列是此orthogroup包含的各物种的基因ID。</li>
+<li>Orthogroups_UnassignedGenes.tsv：是制表符分隔的文本文件，其格式与 Orthogroups.csv 相同，但包含未分配给任何正交群的所有基因，即 MCL 中 未成功聚类（直系同源组中基因数 &gt;&#x3D; 1）的离散基因。</li>
+<li>Orthogroups.GeneCount.tsv：是一个制表符分隔的文本文件，其格式与 Orthogroups.csv 相同，但包含每个正交群中每个物种的基因数计数，可用于基因收缩扩张分析。</li>
+<li>Orthogroups_SingleCopyOrthologues.txt：是一个正交群列表，每个物种只包含一个基因，即它们包含一对一的直向同源物（单拷贝直系同源基因）。它们非常适合物种间比较和物种树推断。</li>
 </ul>
-<h2 id="4-4-GeneFamilyIntegrator模块"><a href="#4-4-GeneFamilyIntegrator模块" class="headerlink" title="4.4. GeneFamilyIntegrator模块"></a>4.4. GeneFamilyIntegrator模块</h2><ol>
-<li>用22Gv1.1 scaffolds，orthomcl 聚类法运行</li>
+<ol start="4">
+<li><strong>Gene_Trees 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>GeneFamilyIntegrator --orthogroup_faa geneFamilyClassification_dir/orthogroups_fasta --scaffold 22Gv1.1  --method orthomcl</code></li>
+<li>记录了每个 orthogroup（gene_num &gt;&#x3D; 4）的有根基因树结构。</li>
 </ul>
-<h2 id="4-5-GeneFamilyAligner模块"><a href="#4-5-GeneFamilyAligner模块" class="headerlink" title="4.5. GeneFamilyAligner模块"></a>4.5. GeneFamilyAligner模块</h2><ol>
-<li>用22Gv1.1 scaffolds，orthomcl 聚类法和mafft比对软件运行</li>
+<ol start="5">
+<li><strong>Resolved Gene Trees文件夹</strong></li>
 </ol>
 <ul>
-<li><code>GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft</code></li>
+<li>为具有 4 个或更多序列的每个正交群推断出有根的系统发育树，并使用OrthoFinder的杂种-重叠&#x2F;重复-丢失溯祖模型(hybrid species-overlap&#x2F;duplication-loss coalescent model)进行解析。</li>
 </ul>
-<h2 id="4-6-GeneFamilyPhylogenyBuilder模块"><a href="#4-6-GeneFamilyPhylogenyBuilder模块" class="headerlink" title="4.6. GeneFamilyPhylogenyBuilder模块"></a>4.6. GeneFamilyPhylogenyBuilder模块</h2><ol>
-<li>用22Gv1.1 scaffolds，orthomcl 聚类法和fastree建树软件运行</li>
+<ol start="6">
+<li><strong>Species_Tree 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --tree_inference fasttree</code></li>
+<li>SpeciesTree_rooted.txt：STAG物种树从所有orthogroups推断，含有内部节点STAG支持值，并使用STRIDE生根，计算出的有根物种树结构。</li>
+<li>SpeciesTree_rooted_node_labels.txt：与上述相同的树，但节点被赋予标签(N0,N1, . . . , N m N_0,N_1,…,N_mN0,N1,…,Nm)（而不是支持值）以允许其他结果文件交叉引用物种树中的分支&#x2F;节点（例如基因复制事件的位置）。</li>
+<li>Orthogroups_for_concatenated_alignment.txt：仅在 -M msa 模式下输出，列出了所有串联起来用于推断物种树的 orthogroup ID。</li>
 </ul>
-<h2 id="4-7-KaKsAnalysis模块"><a href="#4-7-KaKsAnalysis模块" class="headerlink" title="4.7. KaKsAnalysis模块"></a>4.7. KaKsAnalysis模块</h2><ol>
-<li>进行旁系同源分析</li>
+<ol start="7">
+<li><strong>Comparative_Genomics_Statistics 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>KaKsAnalysis --coding_sequences_species_1 species1.fna --proteins_species_1 species1.faa --comparison paralogs --num_threads 4</code></li>
+<li><p>Duplications_per_Orthogroup.tsv：是一个制表符分隔的文本文件，它给出了每个正交群中标识的重复数。此数据的主文件是 Gene_Duplication_Events&#x2F;Duplications.tsv。</p>
+</li>
+<li><p>Duplications_per_Species_Tree_Node.tsv：是一个制表符分隔的文本文件，它给出了识别为沿着物种树的每个分支发生的重复数。此数据的主文件是 Gene_Duplication_Events&#x2F;Duplications.tsv。</p>
+</li>
+<li><p>Orthogroups_SpeciesOverlaps.tsv：是一个制表符分隔的文本文件，其中包含作为方阵的每个物种对之间共享的正交群的数量。</p>
+</li>
+<li><p>OrthologuesStats_*.tsv：是制表符分隔的文本文件，包含矩阵给出每对物种之间一对一、一对多和多对多关系中的直向同源物数量。</p>
+<ul>
+<li>OrthologuesStats_one-to-one.tsv是每个物种对之间一对一直向同源物的数量。</li>
+<li>OrthologuesStats_many-to-many.tsv包含每个物种对的多对多关系中的直向同源物的数量（由于物种形成后两个谱系中的基因重复事件）。条目 (i,j) 是物种 i 中与物种 j 中的基因存在多对多直系关系的基因数。</li>
+<li>OrthologuesStats_one-to-many.tsv：条目 (i,j) 给出物种 i 中与物种 j 的基因处于一对多直系关系的基因数量。这里有一个示例结果文件的演练： https : &#x2F;&#x2F;github.com&#x2F;davidemms&#x2F;OrthoFinder&#x2F;issues&#x2F;259。</li>
+<li>OrthologuesStats_many-to-one.tsv：条目 (i,j) 给出物种 i 中与物种 j 的基因处于多对一直系关系的基因数量。这里有一个示例结果文件的演练： https : &#x2F;&#x2F;github.com&#x2F;davidemms&#x2F;OrthoFinder&#x2F;issues&#x2F;259。</li>
+<li>OrthologuesStats_Total.tsv包含任何多样性的每个物种的直向同源物对的总数。条目 (i,j) 是物种 i 中在物种 j 中具有直向同源物的基因总数。</li>
 </ul>
-<h1 id="5-PlantTribes2安装"><a href="#5-PlantTribes2安装" class="headerlink" title="5. PlantTribes2安装"></a>5. PlantTribes2安装</h1><ol>
-<li>conda安装：PhylogenomicsAnalysis模块无法通过conda安装</li>
-</ol>
+</li>
+<li><p>Statistics_Overall.tsv：是一个制表符分隔的文本文件，其中包含有关正交群大小和分配给正交群的基因比例的一般统计信息。</p>
+</li>
+<li><p>Statistics_PerSpecies.tsv：是一个制表符分隔的文本文件，包含与 Statistics_Overall.csv 文件相同的信息，但针对每个单独的物种。</p>
+</li>
+</ul>
+<p>文件“Statistics_Overall.csv”和“Statistics_PerSpecies.csv”中的大部分术语都是不言自明的，其余的定义如下。</p>
 <ul>
-<li>AssemblyPostProcessor模块：<code>conda install bioconda::plant_tribes_assembly_post_processor</code></li>
-<li>GeneFamilyAligner模块：<code>conda install bioconda::plant_tribes_gene_family_aligner</code></li>
-<li>GeneFamilyClassifier模块：<code>conda install bioconda::plant_tribes_gene_family_classifier</code></li>
-<li>GeneFamilyIntegrator模块：<code>conda install bioconda::plant_tribes_gene_family_integrator</code></li>
-<li>GeneFamilyPhylogenyBuilder模块：<code>conda install bioconda::plant_tribes_gene_family_phylogeny_builder</code></li>
-<li>KaKsAnalysis模块：<code>conda install bioconda::plant_tribes_kaks_analysis</code></li>
+<li>Species-specific orthogroup：完全由来自一个物种的基因组成的正交群。</li>
+<li>G50：正交群中的基因数量，使得 50% 的基因处于该大小或更大的正交群中。</li>
+<li>O50：正交群的最小数量，使得 50% 的基因处于该大小或更大的正交群中。</li>
+<li>单拷贝正群：每个物种只有一个基因（并且没有更多）的正群。这些正交群是推断物种树和许多其他分析的理想选择。</li>
+<li>未分配基因：未与任何其他基因放在同一个群中的基因。</li>
 </ul>
-<ol start="2">
-<li>下载安装</li>
+<ol start="8">
+<li><strong>Gene_Duplication_Events 文件夹</strong></li>
 </ol>
 <ul>
-<li>确保依赖软件都已安装</li>
-<li>下载PlantTribes的github库：<code>git clone https://github.com/dePamphilis/PlantTribes.git</code></li>
-<li>PlantTribes的可执行文件在pipelines子目录下</li>
+<li>Duplications.tsv是一个制表符分隔的文本文件，它列出了通过检查每个正群基因树的每个节点识别出的所有基因复制事件，每行代表至少一次基因复制事件。</li>
+<li>Duplications.tsv的列分别是1.“Orthogroup”，2.“Species Tree node”（发生复制的物种树的分支，参见Species_Tree&#x2F;SpeciesTree_rooted_node_labels.txt），3.“Gene tree node”（与基因复制事件对应的节点，参见相应的orthogroup Resolved_Gene_Trees&#x2F;) 中的树；4.”Support”（存在复制基因的两个副本的预期物种的比例）；5.“Type”（”Terminal”：物种树终端分支上的重复，”Non-Terminal”：物种树内部分支上的重复，因此被多个物种共享，”Non-Terminal”：STRIDE检查基因树的拓扑结构在复制后应该是什么）；6.“Genes 1”（基因列表来自复制基因的一个副本，逗号分隔）；7.“Genes 2”（基因列表来自复制基因的另一个副本，逗号分隔）。</li>
 </ul>
-<h1 id="6-PlantTribes2快速使用"><a href="#6-PlantTribes2快速使用" class="headerlink" title="6. PlantTribes2快速使用"></a>6. PlantTribes2快速使用</h1><h2 id="6-1-标准运行"><a href="#6-1-标准运行" class="headerlink" title="6.1. 标准运行"></a>6.1. 标准运行</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">nohup AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --dereplicate --min_length 100 --num_threads 12 &amp; # 预测转录本的CDS区域</span><br><span class="line">nohup GeneFamilyClassifier --proteins assemblyPostProcessing_dir/transcripts.cleaned.nr.pep --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --classifier both --num_threads 24 --orthogroup_fasta --coding_sequences assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &amp; # 转录本分类到orthologs</span><br><span class="line">nohup GeneFamilyIntegrator --orthogroup_fasta geneFamilyClassification_dir/orthogroups_fasta/ --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder &amp; # 匹配scaffolds中的orthologs</span><br><span class="line">nohup GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft --codon_alignments --num_threads 24 &amp; # orthogroups序列比对</span><br><span class="line">nohup GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --tree_inference raxml --bootstrap_replicates 100 --num_threads 24 &amp; # 建ML树</span><br></pre></td></tr></table></figure>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Orthogroup      Species Tree Node       Gene Tree Node  Support Type    Genes 1 Genes 2</span><br><span class="line">OG0000000       N5      n62     0.5     Non-Terminal    Mcandidum_pep_mc33950   Mdodecandrum_pep_DR005872, Mcandidum_pep_mc01326, Mdodecandrum_pep_DR006000, Mcandidum_pep_mc01492</span><br><span class="line">OG0000000       N5      n63     1.0     Non-Terminal    Mdodecandrum_pep_DR005872, Mcandidum_pep_mc01326        Mdodecandrum_pep_DR006000, Mcandidum_pep_mc01492</span><br><span class="line">OG0000000       N5      n177    0.5     Non-Terminal    Mdodecandrum_pep_DR002276, Mdodecandrum_pep_DR013872, Mcandidum_pep_mc36653, Mdodecandrum_pep_DR026891, Mcandidum_pep_mc22876, Mcandidum_pep_mc37513, Mdodecandrum_pep_DR026886 Mdodecandrum_pep_DR004265</span><br></pre></td></tr></table></figure>
 
-<h2 id="6-2-focus-on目标基因的运行"><a href="#6-2-focus-on目标基因的运行" class="headerlink" title="6.2. focus on目标基因的运行"></a>6.2. focus on目标基因的运行</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">预测转录本的CDS区域，并组装RRRgenes_orthogroups.ids文件中指定的orthogroups家族的基因。</span></span><br><span class="line">nohup AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --gene_family_search RRRgenes_orthogroups.ids --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --dereplicate --min_length 100 --num_threads 12 &amp; </span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">从组装好的转录组中提取目标转录本</span></span><br><span class="line">mkdir RRRgenes &amp;&amp; cd RRRgenes &amp;&amp; mkdir genefamily</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do cat ../assemblyPostProcessing_dir/targeted_gene_family_assemblies/$&#123;i&#125;.faa |seqkit seq -w 0|grep -v &quot;&gt;&quot; &gt;$&#123;i&#125;.pep &amp;&amp; seqkit grep -s -f $&#123;i&#125;.pep ../assemblyPostProcessing_dir/transcripts.cleaned.nr.pep &gt; ./genefamily/$&#123;i&#125;.faa &amp;&amp; rm *.pep;done # 更换蛋白序列的ID</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do cat ../assemblyPostProcessing_dir/targeted_gene_family_assemblies/$&#123;i&#125;.fna |seqkit seq -w 0|grep -v &quot;&gt;&quot; &gt;$&#123;i&#125;.nucl &amp;&amp; seqkit grep -s -f $&#123;i&#125;.nucl ../assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &gt; ./genefamily/$&#123;i&#125;.fna &amp;&amp; rm *.nucl;done # 更换DNA的CDS序列的ID</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do seqkit seq -n $i.fna &gt;$i.id &amp;&amp; seqkit seq -n $i.faa &gt;&gt;$i.id &amp;&amp; seqkit grep -r -f $i.id ../assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &gt;$i.fna &amp;&amp; seqkit grep -r -f $i.id ../assemblyPostProcessing_dir/transcripts.cleaned.nr.pep &gt;$i.faa &amp;&amp; rm $i.id;done #统一CDS序列和蛋白序列</span><br><span class="line"><span class="meta prompt_">#</span><span class="language-bash"><span class="comment"># notes：有时做完上面几步后仍然有序列没有被提取（可能是因为组装的指定id的基因与预测的转录本序列并非所有碱基都完全对应），生成了空文件，这时需要检查和手动提取。</span></span></span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">整合目标基因的orthogroups成员</span></span><br><span class="line">nohup GeneFamilyIntegrator --orthogroup_fasta ./genefamily/ --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder &amp; # 匹配scaffolds中的orthologs</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">目标基因的orthogroups序列比对</span></span><br><span class="line">nohup GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft --codon_alignments --num_threads 24 &amp;</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">建目标基因的ML树</span></span><br><span class="line">nohup GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --tree_inference raxml --bootstrap_replicates 1000 --num_threads 24 &amp;</span><br></pre></td></tr></table></figure>
+<ul>
+<li>SpeciesTree_Gene_Duplications_0.5_Support.txt：提供了物种树分支上的上述重复的总和。它是一个 newick 格式的文本文件。每个节点或物种名称后面的数字是在导致节点&#x2F;物种的分支上发生的具有至少 50% 支持度的基因复制事件的数量。分支长度是标准分支长度，如 Species_Tree&#x2F;SpeciesTree_rooted.txt 中给出的。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">(((((Mcandidum.pep_7199:1,Mdodecandrum.pep_3425:1)N5_9299:1,Egrandis.pep_12485:1)N3_296:1,((Ptrichocarpa.pep_13300:1,(Csinensis.pep_5628:1,(Graimondii.pep_15253:1,Athaliana.pep_8750:1)N10_30:1)N8_5:1)N6_61:1,(Ppersica.pep_5798:1,(Mtruncatula.pep_23336:1,Csativus.pep_3576:1)N9_48:1)N7_12:1)N4_19:1)N2_96:1,Vvinifera.pep_7596:1)N1_535:1,Mguttatus.pep_8911:1)N0_1806;</span><br></pre></td></tr></table></figure>
 
-<p><strong>个人经验</strong>：这种运行方式focus on目标基因，去掉了GeneFamilyClassifier模块的使用，计算量小很多，能很快的得到目标基因的结果。虽然能够尽可能地拼接出目标基因的转录本，但是可能包含更多假阳性结果。</p>
-<p><strong>推荐</strong>：在使用AssemblyPostProcessor模块并且–gene_family_search参数指定目标基因id的list，获得转录本CDS区域后，就按照标准运行流程依次使用GeneFamilyClassifier模块、GeneFamilyIntegrator模块、GeneFamilyAligner模块、GeneFamilyPhylogenyBuilder模块来进行直系同源转录本的推断，结果更可靠。<br>同时建议对GeneFamilyAligner模块获得的align比对好的序列进行手动和肉眼检查，看是否有假阳性的结果（明显比对不上的转录本序列被放进了结果）。</p>
-<h1 id="7-PlantTribes2运行的详细参数"><a href="#7-PlantTribes2运行的详细参数" class="headerlink" title="7. PlantTribes2运行的详细参数"></a>7. PlantTribes2运行的详细参数</h1><p>用de novo转录组组装得到的Trinity.fasta文件来演示PlantTribes2运行流程。</p>
-<h2 id="7-1-准备数据"><a href="#7-1-准备数据" class="headerlink" title="7.1. 准备数据"></a>7.1. 准备数据</h2><ul>
-<li>下载你想用的PlantTribes_scaffold数据库，从数据库网址（<a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/%EF%BC%89%E4%B8%AD%E9%80%89%E6%8B%A9%E4%B8%80%E4%B8%AA%E6%95%B0%E6%8D%AE%E5%BA%93%E4%B8%8B%E8%BD%BD%EF%BC%8C%E6%A3%80%E6%9F%A5%E5%B9%B6%E8%A7%A3%E5%8E%8B%E7%BC%A9%60wget">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/）中选择一个数据库下载，检查并解压缩`wget</a> <a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/22Gv1.1.tar.bz2%60,%60md5sum">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/22Gv1.1.tar.bz2`,`md5sum</a> 22Gv1.1.tar.bz<code>,</code>tar -xjvf 22Gv1.1.tar.bz2&#96;。</li>
-<li>下载Arabidopsis_thaliana.smat矩阵文件，<code>wget https://github.com/dePamphilis/PlantTribes/raw/master/test/Arabidopsis_thaliana.smat.gz &amp;&amp; gzip -d Arabidopsis_thaliana.smat.gz</code></li>
+<ol start="9">
+<li><strong>Orthogroup_Sequences 文件夹</strong></li>
+</ol>
+<ul>
+<li>FASTA格式文件，每个正交群的 FASTA 文件给出了正交群中每个基因的氨基酸序列。</li>
 </ul>
-<h2 id="7-2-组装后处理模块：AssemblyPostProcessor-Pipeline"><a href="#7-2-组装后处理模块：AssemblyPostProcessor-Pipeline" class="headerlink" title="7.2. 组装后处理模块：AssemblyPostProcessor Pipeline"></a>7.2. 组装后处理模块：AssemblyPostProcessor Pipeline</h2><p>AssemblyPostProcessor Pipeline 对de novo组装的转录本进行蛋白编码区域预测，获得CDS区域和去除冗余后的转录组序列，用于后续分析。</p>
-<h3 id="7-2-1-运行"><a href="#7-2-1-运行" class="headerlink" title="7.2.1. 运行"></a>7.2.1. 运行</h3><ol>
-<li>用ESTScan预测coding region</li>
+<ol start="10">
+<li><strong>Single_Copy_Orthologue_Sequences 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>PlantTribes/pipelines/AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method estscan --score_matrices /path/to/score/matrices/Arabidopsis_thaliana.smat --strand_specific --dereplicate --min_length 200 --num_threads 12</code></li>
+<li>FASTA格式文件，包含每个单拷贝 Orthogroup 所包含的氨基酸的序列信息。</li>
 </ul>
-<ol start="2">
-<li>用TransDecoder预测coding region</li>
+<ol start="11">
+<li><strong>WorkingDirectory 文件夹</strong></li>
 </ol>
 <ul>
-<li><code>PlantTribes/pipelines/AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --strand_specific --dereplicate --min_length 200 --num_threads 12</code></li>
-<li>输出文件包括：</li>
-<li>assemblyPostProcessing_dir&#x2F;transcripts.cds</li>
+<li>OrthoFinder 运行所需的必需中间文件，如 DIAMOND 比对结果，STAG 输出的无根物种树等。</li>
+</ul>
+<ol start="12">
+<li><strong>MultipleSequenceAlignments 文件夹</strong></li>
+</ol>
+<ul>
+<li>此文件夹仅在 -M msa 模式下输出，均为 FASTA 格式文件。</li>
+<li>记录了每个 orthogroup 中序列间的多序列比对结果。</li>
+<li>记录了程序通过 CMSA 算法过滤后的 orthogroup 中各序列串联后的多序列比对结果，同时比对结果中空位数 &gt; 50% 的列已被删除。</li>
+</ul>
+<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
+<li>tutorial：<a href="https://davidemms.github.io/">https://davidemms.github.io/</a></li>
+<li>github：<a href="https://github.com/davidemms/OrthoFinder">https://github.com/davidemms/OrthoFinder</a></li>
+<li>paper：<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y</a></li>
+<li>xuzhougeng’s blog：<a href="https://xuzhougeng.top/archives/OrthoFinder2-fast-and-accurate-phylogenomic-orthology-analysis-from-gene-sequences">https://xuzhougeng.top/archives/OrthoFinder2-fast-and-accurate-phylogenomic-orthology-analysis-from-gene-sequences</a></li>
+<li><a href="https://blog.csdn.net/weixin_44614936/article/details/101640473">https://blog.csdn.net/weixin_44614936/article/details/101640473</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>orthology inference</category>
+      </categories>
+      <tags>
+        <tag>OrthoFinder2</tag>
+        <tag>orthology</tag>
+        <tag>OrthoMCL</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离</title>
+    <url>/2023/03/13/bioinfo_phylogeny_MDS/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105210&auto=1&height=32"></iframe></div>
+
+<h1 id="1-树空间的多维缩放（multidimensional-scaling，MDS）可视化物种树和基因树的RF距离"><a href="#1-树空间的多维缩放（multidimensional-scaling，MDS）可视化物种树和基因树的RF距离" class="headerlink" title="1. 树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离"></a>1. 树空间的多维缩放（multidimensional scaling，MDS）可视化物种树和基因树的RF距离</h1><ol>
+<li>简介</li>
+</ol>
+<ul>
+<li>当系统发育拓扑冲突时，需要评估基因树与物种树的不一致程度，可使用树空间的多维缩放（multidimensional scaling，MDS）来对树的RF距离进行降维，并在指定维度可视化基因树与物种树之间的RF距离。</li>
+</ul>
+<ol start="2">
+<li>分析步骤</li>
+</ol>
+<ul>
+<li>先使用R包ape 5.0计算树之间的成对Robinson-Foulds（RF）距离，然后对RF距离进行多维缩放（multidimensional scaling，MDS），最后绘制指定维度下的RF距离示意图。</li>
+</ul>
+<ol start="3">
+<li>结果</li>
+</ol>
+<ul>
+<li>MDS的二维图如下所示，图中的物种树，包括不同数据和不同软件的物种树（包括ASTRAL_tree，SCGs_ML，SNPs_ML，Plastome_ML树）用其他颜色标示，每个蓝色的点代表一棵基因树，颜色深浅代表支持率平均值的大小。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/MDS_support.png?raw=true" width=70% title="MDS图示例" align=center/>
+
+<p><strong><p align="center">图 1. MDS二维图示例</p></strong></p>
+<h1 id="2-分析过程"><a href="#2-分析过程" class="headerlink" title="2. 分析过程"></a>2. 分析过程</h1><ol>
+<li>输入文件</li>
+</ol>
+<ul>
+<li>species_genes.tre：物种树和基因树合并的树文件，可以合并多棵物种树（比如用不同算法和数据构建的树）。</li>
+</ul>
+<ol start="2">
+<li>用R包ape计算基因树成对间的Robinson-Foulds（RF）距离</li>
+</ol>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ape<span class="punctuation">)</span> <span class="comment"># 载入ape包</span></span><br><span class="line">trees<span class="operator">&lt;-</span>read.tree<span class="punctuation">(</span><span class="string">&quot;species_genes.tre&quot;</span><span class="punctuation">)</span> <span class="comment"># 读取树</span></span><br><span class="line">trees_dist<span class="operator">&lt;-</span>dist.topo<span class="punctuation">(</span>trees<span class="punctuation">,</span> method <span class="operator">=</span> <span class="string">&quot;PH85&quot;</span><span class="punctuation">)</span> <span class="comment"># 计算RF，trees_dist的数据类别是dist</span></span><br><span class="line">trees_matric<span class="operator">&lt;-</span>as.matrix<span class="punctuation">(</span>trees_dist<span class="punctuation">)</span> <span class="comment"># 转化dist到矩阵matrix</span></span><br><span class="line">write.table <span class="punctuation">(</span>trees_matric<span class="punctuation">,</span> file <span class="operator">=</span><span class="string">&quot;RF_pairs.tsv&quot;</span><span class="punctuation">,</span> sep <span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment"># 保存矩阵数据到RF_pairs.tsv文件</span></span><br></pre></td></tr></table></figure>
+
+<ul>
+<li>RF_pairs.tsv文件是行和列都是tree1,tree2,…,treen等依次命名的树之间的RF距离矩阵。</li>
+</ul>
+<ol start="3">
+<li>MDS多维缩放</li>
+</ol>
+<ul>
+<li>经典MDS法</li>
+</ul>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">mds <span class="operator">&lt;-</span> cmdscale<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> eig<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br><span class="line">mds.var.per <span class="operator">&lt;-</span> <span class="built_in">round</span><span class="punctuation">(</span>mds<span class="operator">$</span>eig<span class="operator">/</span><span class="built_in">sum</span><span class="punctuation">(</span>mds<span class="operator">$</span>eig<span class="punctuation">)</span><span class="operator">*</span><span class="number">100</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">)</span></span><br><span class="line">mds.values <span class="operator">&lt;-</span> mds<span class="operator">$</span>points</span><br><span class="line">mds.data <span class="operator">&lt;-</span> data.frame<span class="punctuation">(</span>Sample<span class="operator">=</span>rownames<span class="punctuation">(</span>mds.values<span class="punctuation">)</span><span class="punctuation">,</span> X<span class="operator">=</span>mds.values<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">,</span> Y<span class="operator">=</span>mds.values<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">2</span><span class="punctuation">]</span><span class="punctuation">)</span></span><br><span class="line">write.table<span class="punctuation">(</span>mds.data<span class="punctuation">,</span>file<span class="operator">=</span><span class="string">&quot;mds.tsv&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">)</span> <span class="comment"># 把MDS的二维值保存到mds.tsv</span></span><br></pre></td></tr></table></figure>
+
+<ul>
+<li>mds.tsv文件示例。每棵树都有一行数据，这里是二维的，包含MDS1和MDS2两个值。用tree1,tree2,…,treen等依次命名，与species_genes.tre树文件中树的顺序一致。如果合并了物种树和基因树，可以依据顺序在mds.tsv中找到物种树对应的行。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&quot;Sample&quot;	&quot;MDS1&quot;	&quot;MDS2&quot;</span><br><span class="line">&quot;tree1&quot;	&quot;tree1&quot;	-15.2853071675022	-9.23665453167777</span><br><span class="line">&quot;tree2&quot;	&quot;tree2&quot;	-13.8416686613703	-7.78853307096205</span><br></pre></td></tr></table></figure>
+
+<ul>
+<li>nonmetric MDS法</li>
+</ul>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>MASS<span class="punctuation">)</span></span><br><span class="line">mds <span class="operator">&lt;-</span> isoMDS<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br></pre></td></tr></table></figure>
+
+<ol start="4">
+<li>绘图</li>
+</ol>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ggplot2<span class="punctuation">)</span></span><br><span class="line">ggplot<span class="punctuation">(</span>data<span class="operator">=</span>mds.data<span class="punctuation">,</span> aes<span class="punctuation">(</span>x<span class="operator">=</span>X<span class="punctuation">,</span> y<span class="operator">=</span>Y<span class="punctuation">,</span> label<span class="operator">=</span>Sample<span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  geom_text<span class="punctuation">(</span><span class="punctuation">)</span> <span class="operator">+</span></span><br><span class="line">  xlab<span class="punctuation">(</span>paste<span class="punctuation">(</span><span class="string">&quot;MDS1 - &quot;</span><span class="punctuation">,</span> mds.var.per<span class="punctuation">[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">,</span> <span class="string">&quot;%&quot;</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  ylab<span class="punctuation">(</span>paste<span class="punctuation">(</span><span class="string">&quot;MDS2 - &quot;</span><span class="punctuation">,</span> mds.var.per<span class="punctuation">[</span><span class="number">2</span><span class="punctuation">]</span><span class="punctuation">,</span> <span class="string">&quot;%&quot;</span><span class="punctuation">,</span> sep<span class="operator">=</span><span class="string">&quot;&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  theme_bw<span class="punctuation">(</span><span class="punctuation">)</span><span class="operator">+</span></span><br><span class="line">  ggtitle<span class="punctuation">(</span><span class="string">&quot;MDS plot using Euclidean distance&quot;</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<p>或者</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">mds <span class="operator">&lt;-</span> cmdscale<span class="punctuation">(</span>trees_dist<span class="punctuation">,</span> eig<span class="operator">=</span><span class="literal">TRUE</span><span class="punctuation">,</span> k<span class="operator">=</span><span class="number">2</span><span class="punctuation">)</span> <span class="comment"># k代表维度</span></span><br><span class="line">x <span class="operator">&lt;-</span> mds<span class="operator">$</span>points<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">1</span><span class="punctuation">]</span></span><br><span class="line">y <span class="operator">&lt;-</span> mds<span class="operator">$</span>points<span class="punctuation">[</span><span class="punctuation">,</span><span class="number">2</span><span class="punctuation">]</span></span><br><span class="line">plot<span class="punctuation">(</span>x<span class="punctuation">,</span> y<span class="punctuation">,</span> xlab<span class="operator">=</span><span class="string">&quot;Coordinate 1&quot;</span><span class="punctuation">,</span> ylab<span class="operator">=</span><span class="string">&quot;Coordinate 2&quot;</span><span class="punctuation">,</span> main<span class="operator">=</span><span class="string">&quot;Metric MDS&quot;</span><span class="punctuation">,</span> type<span class="operator">=</span><span class="string">&quot;n&quot;</span><span class="punctuation">)</span></span><br><span class="line">text<span class="punctuation">(</span>mds<span class="operator">$</span>points<span class="punctuation">,</span> labels <span class="operator">=</span> rownames<span class="punctuation">(</span>trees_matric<span class="punctuation">)</span><span class="punctuation">,</span> cex <span class="operator">=</span> <span class="number">0.6</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<p>结果图保存成pdf后可以在pdf编辑器里把物种树对应的点用更大尺寸和其他颜色标示出来。</p>
+<ol start="5">
+<li>建议</li>
+</ol>
+<ul>
+<li>还可以把每个基因树和物种树的支持率的平均值计算出来之后，用支持率给每棵树的MSD点上色，从而查看是否是离物种树越近的基因树的平均支持率越高。</li>
+<li>也可以用基因树的平均支持率和基因树离物种树的RF距离画散点图，并做线性回归分析判断相关性。</li>
+</ul>
+<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
+<li>MDS paper：<a href="https://academic.oup.com/sysbio/article/67/3/400/4175806">https://academic.oup.com/sysbio/article/67/3/400/4175806</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>phylogeny</category>
+      </categories>
+      <tags>
+        <tag>phylogeny</tag>
+        <tag>ggplot2</tag>
+        <tag>R packages</tag>
+        <tag>multidimensional scaling</tag>
+        <tag>MDS</tag>
+        <tag>成对Robinson-Foulds距离</tag>
+        <tag>ape</tag>
+        <tag>gene tree</tag>
+        <tag>species tree</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>用PlantTribes做直系同源推断</title>
+    <url>/2024/09/19/bioinfo_orthology.inference_PlantTribes/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1959437612&auto=1&height=32"></iframe></div>
+
+<h1 id="1-直系同源-orthology-推断"><a href="#1-直系同源-orthology-推断" class="headerlink" title="1. 直系同源(orthology)推断"></a>1. 直系同源(orthology)推断</h1><h2 id="1-1-直系同源推断"><a href="#1-1-直系同源推断" class="headerlink" title="1.1. 直系同源推断"></a>1.1. 直系同源推断</h2><p>直系同源(orthology)是指直系同源基因的直系同源这种属性，相关的概念可以参考<a href="https://yanzhongsino.github.io/2021/06/23/concept_homology/">博文-同源性相关概念</a>。</p>
+<p>直系同源推断(orthology inference)常常在多个物种间进行，通常是获取多个物种的直系同源群(orthogroups)和直系同源群间的关系。</p>
+<p>直系同源基因判定的方法有两种：</p>
+<ul>
+<li>一种是将物种的直系同源基因两两比较，不同物种的每一对都是直系同源，但存在基因重复，使得用这种方法找到的同源关系是不具有传递性的（A和B是同源的，B和C是同源的，但是A和C不是同源的）。multiparanoid和OMA方法都是这个原理。所以需要每个同源基因都在多个集合中存在才行，进行两两比较。</li>
+<li>另一种是识别完整的正交群，一个同源组orthogroup包含所有物种共同祖先基因，同时包含直系／旁系同源基因，OrthoMCL是这个原理，它是通过正交组做推断，用BLAST计算多个物种序列之间的序列相似性得分，然后用MCL聚类算法，识别该数据集中高度相似的序列组。</li>
+</ul>
+<p>这篇博文主要介绍一个新工具PlantTribes2。</p>
+<h1 id="2-PlantTribes2介绍"><a href="#2-PlantTribes2介绍" class="headerlink" title="2. PlantTribes2介绍"></a>2. PlantTribes2介绍</h1><p>PlantTribes 是用于比较植物基因组学的自动化基因家族分析流程的集合。PlantTribes通过模块对植物基因组中完整蛋白质序列聚类来进行比较进化研究。 模块功能包括：将从头组装的转录本处理为推断的编码序列及其相应的氨基酸翻译序列，局部组装目标基因家族，推断直系同源基因，并进行基因家族多序列比对和构建相应的系统发育树，以及估算一组基因序列的旁系&#x2F;直系同源的成对同义&#x2F;非同义替换率。</p>
+<h1 id="3-PlantTribes-scaffold数据库"><a href="#3-PlantTribes-scaffold数据库" class="headerlink" title="3. PlantTribes_scaffold数据库"></a>3. PlantTribes_scaffold数据库</h1><p>PlantTribes2 (v1.0.4) 基于过去发表的系统基因组学研究提供了几个植物基因家族scaffolds数据库，包含了预先构建的多序列比对和orthogroups，下载网址是：<a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/</a></p>
+<ol>
+<li>单子叶植物数据库：12Gv1.0（代表12个物种的scaffolds）</li>
+<li>被子植物数据库的四个版本：22Gv1.1, 26Gv1.0, 26Gv2.0, and 37Gv1.0</li>
+</ol>
+<h1 id="4-PlantTribes2的模块"><a href="#4-PlantTribes2的模块" class="headerlink" title="4. PlantTribes2的模块"></a>4. PlantTribes2的模块</h1><p>PlantTribes2一共有7个模块，每个模块是独立的功能，也可以联合使用。</p>
+<img src="https://www.frontiersin.org/files/Articles/1011199/fpls-13-1011199-HTML-r1/image_m/fpls-13-1011199-g001.jpg" width=80% title="PlantTribes2 analysis workflow" alt="PlantTribes2 analysis workflow" align=center/>
+
+<p><strong><p align="center">Figure 1. PlantTribes2 analysis workflow</strong><br>from <a href="https://www.frontiersin.org/files/Articles/1011199/fpls-13-1011199-HTML-r1/image_m/fpls-13-1011199-g001.jpg">PlantTribes2 analysis workflow</a></p></p>
+<h2 id="4-1-AssemblyPostProcessor模块"><a href="#4-1-AssemblyPostProcessor模块" class="headerlink" title="4.1. AssemblyPostProcessor模块"></a>4.1. AssemblyPostProcessor模块</h2><ol>
+<li>运行 ESTScan 编码区域预测</li>
+</ol>
+<ul>
+<li><code>PlantTribes/pipelines/AssemblyPostProcessor  --transcripts transcripts.fasta --prediction_method estscan --score_matrices /path/to/score/matrices/Arabidopsis_thaliana.smat</code></li>
+</ul>
+<h2 id="4-2-GeneFamilyClassifier模块"><a href="#4-2-GeneFamilyClassifier模块" class="headerlink" title="4.2. GeneFamilyClassifier模块"></a>4.2. GeneFamilyClassifier模块</h2><ol>
+<li>用22Gv1.1 scaffolds，orthomcl 聚类法 和blastp 分类器运行</li>
+</ol>
+<ul>
+<li><code>PlantTribes/pipelines/GeneFamilyClassifier --proteins proteins.fasta --scaffold 22Gv1.1 --method orthomcl --classifier blastp</code></li>
+</ul>
+<h2 id="4-3-PhylogenomicsAnalysis模块-legacy-pipeline"><a href="#4-3-PhylogenomicsAnalysis模块-legacy-pipeline" class="headerlink" title="4.3. PhylogenomicsAnalysis模块 (legacy pipeline)"></a>4.3. PhylogenomicsAnalysis模块 (legacy pipeline)</h2><ol>
+<li>用22Gv1.1 scaffolds，orthomcl 聚类法 和raxml建树法运行</li>
+</ol>
+<ul>
+<li><code>PlantTribes/pipelines/PhylogenomicsAnalysis --orthogroup_faa geneFamilyClassification_dir/orthogroups_fasta --scaffold 22Gv1.1  --method orthomcl  --add_alignments  --tree_inference raxml</code></li>
+</ul>
+<h2 id="4-4-GeneFamilyIntegrator模块"><a href="#4-4-GeneFamilyIntegrator模块" class="headerlink" title="4.4. GeneFamilyIntegrator模块"></a>4.4. GeneFamilyIntegrator模块</h2><ol>
+<li>用22Gv1.1 scaffolds，orthomcl 聚类法运行</li>
+</ol>
+<ul>
+<li><code>GeneFamilyIntegrator --orthogroup_faa geneFamilyClassification_dir/orthogroups_fasta --scaffold 22Gv1.1  --method orthomcl</code></li>
+</ul>
+<h2 id="4-5-GeneFamilyAligner模块"><a href="#4-5-GeneFamilyAligner模块" class="headerlink" title="4.5. GeneFamilyAligner模块"></a>4.5. GeneFamilyAligner模块</h2><ol>
+<li>用22Gv1.1 scaffolds，orthomcl 聚类法和mafft比对软件运行</li>
+</ol>
+<ul>
+<li><code>GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft</code></li>
+</ul>
+<h2 id="4-6-GeneFamilyPhylogenyBuilder模块"><a href="#4-6-GeneFamilyPhylogenyBuilder模块" class="headerlink" title="4.6. GeneFamilyPhylogenyBuilder模块"></a>4.6. GeneFamilyPhylogenyBuilder模块</h2><ol>
+<li>用22Gv1.1 scaffolds，orthomcl 聚类法和fastree建树软件运行</li>
+</ol>
+<ul>
+<li><code>GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --tree_inference fasttree</code></li>
+</ul>
+<h2 id="4-7-KaKsAnalysis模块"><a href="#4-7-KaKsAnalysis模块" class="headerlink" title="4.7. KaKsAnalysis模块"></a>4.7. KaKsAnalysis模块</h2><ol>
+<li>进行旁系同源分析</li>
+</ol>
+<ul>
+<li><code>KaKsAnalysis --coding_sequences_species_1 species1.fna --proteins_species_1 species1.faa --comparison paralogs --num_threads 4</code></li>
+</ul>
+<h1 id="5-PlantTribes2安装"><a href="#5-PlantTribes2安装" class="headerlink" title="5. PlantTribes2安装"></a>5. PlantTribes2安装</h1><ol>
+<li>conda安装：PhylogenomicsAnalysis模块无法通过conda安装</li>
+</ol>
+<ul>
+<li>AssemblyPostProcessor模块：<code>conda install bioconda::plant_tribes_assembly_post_processor</code></li>
+<li>GeneFamilyAligner模块：<code>conda install bioconda::plant_tribes_gene_family_aligner</code></li>
+<li>GeneFamilyClassifier模块：<code>conda install bioconda::plant_tribes_gene_family_classifier</code></li>
+<li>GeneFamilyIntegrator模块：<code>conda install bioconda::plant_tribes_gene_family_integrator</code></li>
+<li>GeneFamilyPhylogenyBuilder模块：<code>conda install bioconda::plant_tribes_gene_family_phylogeny_builder</code></li>
+<li>KaKsAnalysis模块：<code>conda install bioconda::plant_tribes_kaks_analysis</code></li>
+</ul>
+<ol start="2">
+<li>下载安装</li>
+</ol>
+<ul>
+<li>确保依赖软件都已安装</li>
+<li>下载PlantTribes的github库：<code>git clone https://github.com/dePamphilis/PlantTribes.git</code></li>
+<li>PlantTribes的可执行文件在pipelines子目录下</li>
+</ul>
+<h1 id="6-PlantTribes2快速使用"><a href="#6-PlantTribes2快速使用" class="headerlink" title="6. PlantTribes2快速使用"></a>6. PlantTribes2快速使用</h1><h2 id="6-1-标准运行"><a href="#6-1-标准运行" class="headerlink" title="6.1. 标准运行"></a>6.1. 标准运行</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">nohup AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --dereplicate --min_length 100 --num_threads 12 &amp; # 预测转录本的CDS区域</span><br><span class="line">nohup GeneFamilyClassifier --proteins assemblyPostProcessing_dir/transcripts.cleaned.nr.pep --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --classifier both --num_threads 24 --orthogroup_fasta --coding_sequences assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &amp; # 转录本分类到orthologs</span><br><span class="line">nohup GeneFamilyIntegrator --orthogroup_fasta geneFamilyClassification_dir/orthogroups_fasta/ --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder &amp; # 匹配scaffolds中的orthologs</span><br><span class="line">nohup GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft --codon_alignments --num_threads 24 &amp; # orthogroups序列比对</span><br><span class="line">nohup GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --tree_inference raxml --bootstrap_replicates 100 --num_threads 24 &amp; # 建ML树</span><br></pre></td></tr></table></figure>
+
+<h2 id="6-2-focus-on目标基因的运行"><a href="#6-2-focus-on目标基因的运行" class="headerlink" title="6.2. focus on目标基因的运行"></a>6.2. focus on目标基因的运行</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">预测转录本的CDS区域，并组装RRRgenes_orthogroups.ids文件中指定的orthogroups家族的基因。</span></span><br><span class="line">nohup AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --gene_family_search RRRgenes_orthogroups.ids --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --dereplicate --min_length 100 --num_threads 12 &amp; </span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">从组装好的转录组中提取目标转录本</span></span><br><span class="line">mkdir RRRgenes &amp;&amp; cd RRRgenes &amp;&amp; mkdir genefamily</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do cat ../assemblyPostProcessing_dir/targeted_gene_family_assemblies/$&#123;i&#125;.faa |seqkit seq -w 0|grep -v &quot;&gt;&quot; &gt;$&#123;i&#125;.pep &amp;&amp; seqkit grep -s -f $&#123;i&#125;.pep ../assemblyPostProcessing_dir/transcripts.cleaned.nr.pep &gt; ./genefamily/$&#123;i&#125;.faa &amp;&amp; rm *.pep;done # 更换蛋白序列的ID</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do cat ../assemblyPostProcessing_dir/targeted_gene_family_assemblies/$&#123;i&#125;.fna |seqkit seq -w 0|grep -v &quot;&gt;&quot; &gt;$&#123;i&#125;.nucl &amp;&amp; seqkit grep -s -f $&#123;i&#125;.nucl ../assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &gt; ./genefamily/$&#123;i&#125;.fna &amp;&amp; rm *.nucl;done # 更换DNA的CDS序列的ID</span><br><span class="line">for i in $(cat ../../../RRRgenes_orthogroups.ids);do seqkit seq -n $i.fna &gt;$i.id &amp;&amp; seqkit seq -n $i.faa &gt;&gt;$i.id &amp;&amp; seqkit grep -r -f $i.id ../assemblyPostProcessing_dir/transcripts.cleaned.nr.cds &gt;$i.fna &amp;&amp; seqkit grep -r -f $i.id ../assemblyPostProcessing_dir/transcripts.cleaned.nr.pep &gt;$i.faa &amp;&amp; rm $i.id;done #统一CDS序列和蛋白序列</span><br><span class="line"><span class="meta prompt_">#</span><span class="language-bash"><span class="comment"># notes：有时做完上面几步后仍然有序列没有被提取（可能是因为组装的指定id的基因与预测的转录本序列并非所有碱基都完全对应），生成了空文件，这时需要检查和手动提取。</span></span></span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">整合目标基因的orthogroups成员</span></span><br><span class="line">nohup GeneFamilyIntegrator --orthogroup_fasta ./genefamily/ --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder &amp; # 匹配scaffolds中的orthologs</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">目标基因的orthogroups序列比对</span></span><br><span class="line">nohup GeneFamilyAligner --orthogroup_faa integratedGeneFamilies_dir --alignment_method mafft --codon_alignments --num_threads 24 &amp;</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">建目标基因的ML树</span></span><br><span class="line">nohup GeneFamilyPhylogenyBuilder --orthogroup_aln geneFamilyAlignments_dir/orthogroups_aln --scaffold /path/to/PlantTribes_scaffolds/22Gv1.1 --method orthofinder --tree_inference raxml --bootstrap_replicates 1000 --num_threads 24 &amp;</span><br></pre></td></tr></table></figure>
+
+<p><strong>个人经验</strong>：这种运行方式focus on目标基因，去掉了GeneFamilyClassifier模块的使用，计算量小很多，能很快的得到目标基因的结果。虽然能够尽可能地拼接出目标基因的转录本，但是可能包含更多假阳性结果。</p>
+<p><strong>推荐</strong>：在使用AssemblyPostProcessor模块并且–gene_family_search参数指定目标基因id的list，获得转录本CDS区域后，就按照标准运行流程依次使用GeneFamilyClassifier模块、GeneFamilyIntegrator模块、GeneFamilyAligner模块、GeneFamilyPhylogenyBuilder模块来进行直系同源转录本的推断，结果更可靠。<br>同时建议对GeneFamilyAligner模块获得的align比对好的序列进行手动和肉眼检查，看是否有假阳性的结果（明显比对不上的转录本序列被放进了结果）。</p>
+<h1 id="7-PlantTribes2运行的详细参数"><a href="#7-PlantTribes2运行的详细参数" class="headerlink" title="7. PlantTribes2运行的详细参数"></a>7. PlantTribes2运行的详细参数</h1><p>用de novo转录组组装得到的Trinity.fasta文件来演示PlantTribes2运行流程。</p>
+<h2 id="7-1-准备数据"><a href="#7-1-准备数据" class="headerlink" title="7.1. 准备数据"></a>7.1. 准备数据</h2><ul>
+<li>下载你想用的PlantTribes_scaffold数据库，从数据库网址（<a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/%EF%BC%89%E4%B8%AD%E9%80%89%E6%8B%A9%E4%B8%80%E4%B8%AA%E6%95%B0%E6%8D%AE%E5%BA%93%E4%B8%8B%E8%BD%BD%EF%BC%8C%E6%A3%80%E6%9F%A5%E5%B9%B6%E8%A7%A3%E5%8E%8B%E7%BC%A9%60wget">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/）中选择一个数据库下载，检查并解压缩`wget</a> <a href="http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/22Gv1.1.tar.bz2%60,%60md5sum">http://bigdata.bx.psu.edu/PlantTribes_scaffolds/data/22Gv1.1.tar.bz2`,`md5sum</a> 22Gv1.1.tar.bz<code>,</code>tar -xjvf 22Gv1.1.tar.bz2&#96;。</li>
+<li>下载Arabidopsis_thaliana.smat矩阵文件，<code>wget https://github.com/dePamphilis/PlantTribes/raw/master/test/Arabidopsis_thaliana.smat.gz &amp;&amp; gzip -d Arabidopsis_thaliana.smat.gz</code></li>
+</ul>
+<h2 id="7-2-组装后处理模块：AssemblyPostProcessor-Pipeline"><a href="#7-2-组装后处理模块：AssemblyPostProcessor-Pipeline" class="headerlink" title="7.2. 组装后处理模块：AssemblyPostProcessor Pipeline"></a>7.2. 组装后处理模块：AssemblyPostProcessor Pipeline</h2><p>AssemblyPostProcessor Pipeline 对de novo组装的转录本进行蛋白编码区域预测，获得CDS区域和去除冗余后的转录组序列，用于后续分析。</p>
+<h3 id="7-2-1-运行"><a href="#7-2-1-运行" class="headerlink" title="7.2.1. 运行"></a>7.2.1. 运行</h3><ol>
+<li>用ESTScan预测coding region</li>
+</ol>
+<ul>
+<li><code>PlantTribes/pipelines/AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method estscan --score_matrices /path/to/score/matrices/Arabidopsis_thaliana.smat --strand_specific --dereplicate --min_length 200 --num_threads 12</code></li>
+</ul>
+<ol start="2">
+<li>用TransDecoder预测coding region</li>
+</ol>
+<ul>
+<li><code>PlantTribes/pipelines/AssemblyPostProcessor --transcripts Trinity.fasta --prediction_method transdecoder --strand_specific --dereplicate --min_length 200 --num_threads 12</code></li>
+<li>输出文件包括：</li>
+<li>assemblyPostProcessing_dir&#x2F;transcripts.cds</li>
 <li>assemblyPostProcessing_dir&#x2F;transcripts.pep</li>
 <li>assemblyPostProcessing_dir&#x2F;transcripts.cleaned.cds</li>
 <li>assemblyPostProcessing_dir&#x2F;transcripts.cleaned.pep</li>
@@ -5255,7 +5474,7 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <li>目标基因家族组装的参数：</li>
 </ol>
 <ul>
-<li><code>--gene_family_search</code>：目标orthogroup identifiers的list文件。需要”–scaffold” and “–method”两个参数。如果有目标基因想要分析（比如有个课题分析RRR基因），那么一定要指定以得到更有针对性的转录本。scaffold数据库文件里的22Gv1.1&#x2F;annot&#x2F;orthofinder.list保存所有orthogroup IDs（如果使用orthofinder的话）。目标list文件可以通过目标基因的拟南芥或者其他21个物种的ID来搜索orthofinder.list文件，搜索到的第一列即为目标orthogroup IDs。</li>
+<li><code>--gene_family_search</code>：目标orthogroup identifiers的list文件。需要”–scaffold” and “–method”两个参数。如果有目标基因想要分析（比如有个课题分析RRR基因），那么一定要指定以得到更有针对性的转录本。scaffold数据库文件里的22Gv1.1&#x2F;annot&#x2F;orthofinder.list保存所有orthogroup IDs（如果使用orthofinder的话）。目标list文件可以通过目标基因的拟南芥或者其他21个物种的ID来搜索orthofinder.list文件，搜索到的第一列（数字）即为目标orthogroup IDs。</li>
 <li><code>--scaffold</code>：Orthogroups或者gene families proteins scaffold文件。用绝对路径指定。eg. &#x2F;home&#x2F;scaffolds&#x2F;22Gv1.1</li>
 <li><code>--method</code>：蛋白聚类方法。GFam：gfam；OrthoFinder：orthofinder；OrthoMCL：orthomcl；其他非PlantTribes方法：methodname。</li>
 <li><code>--gap_trimming</code>：在alignments里移除gappy sites，默认0.1，代表移除含有90% gaps的sites。</li>
@@ -5572,8 +5791,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <category>orthology inference</category>
       </categories>
       <tags>
-        <tag>PlantTribes</tag>
         <tag>orthology</tag>
+        <tag>PlantTribes</tag>
         <tag>Ka/Ks</tag>
         <tag>orthofinder</tag>
       </tags>
@@ -5772,449 +5991,185 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用OrthoFinder做直系同源推断</title>
-    <url>/2021/12/06/bioinfo_orthology.inference_OrthoFinder2/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=108151&auto=1&height=32"></iframe></div>
+    <title>用软件Quartet Sampling来计算分支支持率，评估系统发育树不一致程度，区分强冲突和弱支持</title>
+    <url>/2022/11/08/bioinfo_phylogeny_QuartetSampling/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105143&auto=1&height=32"></iframe></div>
 
-<h1 id="1-直系同源-orthology-推断"><a href="#1-直系同源-orthology-推断" class="headerlink" title="1. 直系同源(orthology)推断"></a>1. 直系同源(orthology)推断</h1><h2 id="1-1-直系同源推断"><a href="#1-1-直系同源推断" class="headerlink" title="1.1. 直系同源推断"></a>1.1. 直系同源推断</h2><p>直系同源(orthology)是指直系同源基因的直系同源这种属性，相关的概念可以参考<a href="https://yanzhongsino.github.io/2021/06/23/concept_homology/">博文-同源性相关概念</a>。</p>
-<p>直系同源推断(orthology inference)常常在多个物种间进行，通常是获取多个物种的直系同源群(orthogroups)和直系同源群间的关系。</p>
-<p>直系同源基因判定的方法有两种：</p>
-<ul>
-<li>一种是将物种的直系同源基因两两比较，不同物种的每一对都是直系同源，但存在基因重复，使得用这种方法找到的同源关系是不具有传递性的（A和B是同源的，B和C是同源的，但是A和C不是同源的）。multiparanoid和OMA方法都是这个原理。所以需要每个同源基因都在多个集合中存在才行，进行两两比较。</li>
-<li>另一种是识别完整的正交群，一个同源组orthogroup包含所有物种共同祖先基因，同时包含直系／旁系同源基因，OrthoMCL是这个原理，它是通过正交组做推断，用BLAST计算多个物种序列之间的序列相似性得分，然后用MCL聚类算法，识别该数据集中高度相似的序列组。</li>
-</ul>
-<h2 id="1-2-直系同源基因推断常用软件：OrthoMCL和OrthoFinder"><a href="#1-2-直系同源基因推断常用软件：OrthoMCL和OrthoFinder" class="headerlink" title="1.2. 直系同源基因推断常用软件：OrthoMCL和OrthoFinder"></a>1.2. 直系同源基因推断常用软件：OrthoMCL和OrthoFinder</h2><h3 id="1-2-1-OrthoMCL"><a href="#1-2-1-OrthoMCL" class="headerlink" title="1.2.1. OrthoMCL"></a>1.2.1. OrthoMCL</h3><p>OrthoMCL是2003年发表的工具。发表以后得到广泛使用，但存在一些问题：</p>
-<ul>
-<li>2013年之后就没更新，安装需要使用MySQL。</li>
-<li>在BLAST序列相似度时发现，长序列比短序列更准确，有时会留下较长的质量差的序列，而丢弃较短的单质量好的序列。</li>
-<li>orthoBench是唯一可用的公开的正交群标准数据集，用orthoBench数据集评估orthoMCL性能，发现它对序列长度有依赖性，太长或者太短的序列都不准确。</li>
-<li>recall率有待提高。</li>
-</ul>
-<h3 id="1-2-2-OrthoFinder"><a href="#1-2-2-OrthoFinder" class="headerlink" title="1.2.2. OrthoFinder"></a>1.2.2. OrthoFinder</h3><p>OrthoFinder是2015年才出现，2019年正式发表的工具。OrthoFinder解决了一些OrthoMCL中存在的问题，展现了优势：</p>
-<ul>
-<li>持续更新，安装友好，运行速度快。</li>
-<li>OrthoFinder解决了这个问题，它将BLAST评分做了转换，将原来的e值准换成bit值（比特值）来减少序列长度对准确度的影响，因为e值存在阈值，e 小于10的－180次方的都被算作0，而比特值没有阈值。</li>
-<li>OrthoFinder用RBNH代替了传统的RBH提高了recall。</li>
-</ul>
-<p>这篇博文主要介绍OrthoFinder这个工具。</p>
-<h1 id="2-OrthoFinder"><a href="#2-OrthoFinder" class="headerlink" title="2. OrthoFinder"></a>2. OrthoFinder</h1><h2 id="2-1-OrthoFinder的功能"><a href="#2-1-OrthoFinder的功能" class="headerlink" title="2.1. OrthoFinder的功能"></a>2.1. OrthoFinder的功能</h2><p>OrthoFinder是用于推断系统发育直系同源关系的基于python的软件。OrthoFinder1模块推断直系同源群，OrthoFinder2模块在直系同源群的基础上构建物种系统发育树，步骤如下：</p>
+
+<h1 id="1-Quartet-Sampling（QS）method"><a href="#1-Quartet-Sampling（QS）method" class="headerlink" title="1. Quartet Sampling（QS）method"></a>1. Quartet Sampling（QS）method</h1><p>Quartet Sampling（QS）是为系统发育树量化分支的支持率的一种方法，用于区分植物系统发育树上的强烈拓扑冲突和信息不足（缺乏支持）导致的支持率低，即区分强冲突（不一致）和弱支持（不确定性）。</p>
+<h2 id="1-1-原理"><a href="#1-1-原理" class="headerlink" title="1.1. 原理"></a>1.1. 原理</h2><h3 id="1-1-1-四重采样"><a href="#1-1-1-四重采样" class="headerlink" title="1.1.1. 四重采样"></a>1.1.1. 四重采样</h3><ol>
+<li>对于给定的系统发育树，每个内部枝将树划分为四个不重叠的分类群子集（图A）。</li>
+<li>这四组分类群（也被称为元-四重奏，meta-quartet）可以以三种拓扑关系存在：与给定拓扑一致的关系，以及两种替代的不一致关系（Replicate 1和Replicate 2）。</li>
+<li>QS方法从四个子集中的每一个子集中重复随机采样一个分类单元，然后基于给定序列数据（完全比对或随机采样的基因分区），评估跨越该特定分支的随机选择的四条序列（即四重奏，quartet）的所有三个可能系统发育拓扑的似然值。</li>
+<li>对于同一分支采样的所有四重奏，评估这四个采样分类群可以采用的所有三种可能拓扑的可能性。</li>
+<li>然后记录所有重复中具有最佳可能性的四重拓扑并制成表格。</li>
+<li>此过程生成一组计数（跨每个分支的所有复制），其中一致或两个不一致关系中的每一个都具有最佳可能性。</li>
+<li>该过程可以通过评估每个四重奏的完全比对的可能性（即在单矩阵框架中）或通过从多基因或全基因组比对（即在多基因树中）的单个基因&#x2F;分区比对中随机抽样来执行聚结框架）。</li>
+</ol>
+<img src="https://bsapubs.onlinelibrary.wiley.com/cms/asset/e55c64b7-cc9d-49bb-92c9-6e59e0661dde/ajb21016-fig-0001-m.jpg" width=90% title="四重采样方法的描述" align=center/>
+
+<p><strong><p align="center">图1. 四重采样方法的描述</strong><br>图源：<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/ajb2.1016">https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/ajb2.1016</a></p></p>
+<h3 id="1-1-2-评估分数"><a href="#1-1-2-评估分数" class="headerlink" title="1.1.2. 评估分数"></a>1.1.2. 评估分数</h3><p>QS 方法使用这些重新采样的四重树计数来计算焦点树的每个内部分支的三个分数(QC,QD,QI）和评估分类单元的QF分数。</p>
+<p>总的来说，这四个分数提供了区分信息一致性（Quartet Concordance，QC），次生进化历史的存在（Quartet Differential，QD），信息量（Quartet Informativeness，QI）和树中单个分类群的可靠性（Quartet Fidelity，QF）的方法。 </p>
+<p>QS方法把这些影响分开，而不是像系统发育支持的标准测量那样将它们混为一个单一的汇总支持率。</p>
 <ol>
-<li>推断所有物种的直系同源群和直系同源基因。</li>
+<li>Quartet Concordance (QC) score - 四重奏一致性分数</li>
 </ol>
 <ul>
-<li>用all vs all比对所有输入的物种蛋白序列；</li>
-<li>RBNHs方法确定阈值；</li>
-<li>MCL聚类获得直系同源组。</li>
+<li>QC分数是一种类似熵的度量，量化了四种分类群的三种拓扑之间的相对支持度。</li>
+<li>即支持率在三种拓扑中分配越平均，QC值越接近0；越不均，约远离0。</li>
+<li>当频率最高的拓扑与输入树一致时，QC 值在 (0,1] 范围内为正。</li>
+<li>当所有四重树与输入树的焦点分支一致时，QC 等于 1。</li>
+<li>当其中一个不一致的拓扑是最常见的采样四重奏，QC 值在 [-1,0) 范围内为负。</li>
+<li>当所有四重奏树都是两个不一致的系统发育拓扑之一时接近 -1。</li>
+<li>当三个拓扑中平均分配时，QC 等于 0（或者如果三个可能中只有两个被注册为在所有复制中具有最佳可能性，则在两个之间平均分配）。</li>
 </ul>
 <ol start="2">
-<li>推断找到的所有直系同源基因的有根基因树。</li>
+<li>Quartet Differential (QD) score - 四重奏差异分数</li>
 </ol>
 <ul>
-<li>为每个直系同源组构建基因系统发育树；</li>
-<li>使用STAG算法从无根基因树上构建无根物种树；</li>
-<li>使用STRIDE算法构建有根物种树；</li>
-<li>有根物种树进一步辅助构建有根基因树。</li>
+<li>QD分数使用基因渐渗的f和D统计量的逻辑（尽管使用四重拓扑比例，而不是位点频率），并测量两个不一致拓扑的采样比例之间的差异。</li>
+<li>QD 分数没有具体量化基因渗入，也没有识别基因渗入的分类群，但确实表明一种拓扑的频率高于另一种拓扑。</li>
+<li>QD 的低值表明在两个不一致的拓扑结构之间存在一个倾向性的拓扑结构，这也可能表明在给定分支上有背景谱系分选之外的有偏（biased）生物过程的，包括混杂变量，例如基因渗入、强烈的异质性、异质碱基组成等。</li>
+<li>QD 表示支持率在两种替代拓扑上的差异水平，在 [0,1] 范围内变化，值为 1 表示两棵替代拓扑树的比例没有偏斜（skew），值为0 表示采样的所有不一致树仅来自两种替代关系之一。</li>
 </ul>
 <ol start="3">
-<li>用基因树推断基因间的所有直系同源群间的关系。</li>
-<li>推断基因复制事件并交叉引用到对应的基因树和物种树上的对应节点。</li>
-<li>提供比较基因组学统计结果。</li>
+<li>Quartet Informativeness (QI) score - 四重奏信息分数</li>
 </ol>
-<img src="https://github.com/davidemms/OrthoFinder/raw/master/assets/Workflow.png" width=100% title="The OrthoFinder workflow" alt="The OrthoFinder workflow" align=center/>
-
-<p><strong><p align="center">Figure 1. The OrthoFinder workflow</strong><br>from <a href="https://github.com/davidemms/OrthoFinder">github: orthofinder</a></p></p>
-<h2 id="2-2-OrthoFinder的分步原理"><a href="#2-2-OrthoFinder的分步原理" class="headerlink" title="2.2. OrthoFinder的分步原理"></a>2.2. OrthoFinder的分步原理</h2><h3 id="2-2-1-OrthoFinder1推断直系同源群的基本原理"><a href="#2-2-1-OrthoFinder1推断直系同源群的基本原理" class="headerlink" title="2.2.1. OrthoFinder1推断直系同源群的基本原理"></a>2.2.1. OrthoFinder1推断直系同源群的基本原理</h3><ul>
-<li>首先BLAST all vs all 做所有物种蛋白序列的比对。选best hits Top5%，用比特值代替e值，用最小二乘法将Top5%的分数值拟合成一个线性模型，将比特值用这个模型转换之后，较长的质量差的hit将不再优于短的质量好的序列；</li>
-<li>然后将基因长度和系统发育距离标准化。用的是RBNH方法，传统的是用RBH方法，它是一种高精度鉴定直系同源基因对的方法，用BLAST best hit的倒数做的，但是RBNH是用标准话的比特值来做的。</li>
+<ul>
+<li>在给定最佳和次佳的差异阈值的基础上，最佳似然四重树的似然值超过了次优似然值的四重树。这种情况下，QI分数量化了给定分支的复制比例。</li>
+<li>该分数表明，当分子数据在拓扑上有效地模棱两可时（即，当三个可能的四重拓扑中的两个或所有三个具有几乎无法区分的似然分数时），重复不被视为一致或不一致。</li>
+<li>QI在 [0,1] 范围内变化，表示超过阈值的采样四重奏的比例。</li>
+<li>QI 值为 1 表示所有四重奏都提供信息，而值为 0 表示所有四重奏都不确定（即，对于给定分支没有重要信息）。</li>
+<li>QI 代表了分支的信息量，与 QC 和 QD 结合使用，以区分具有低信息的分支与具有冲突信息（即高度不一致）的分支。</li>
 </ul>
-<h3 id="2-2-2-OrthoFinder1推断直系同源群的工作流程"><a href="#2-2-2-OrthoFinder1推断直系同源群的工作流程" class="headerlink" title="2.2.2. OrthoFinder1推断直系同源群的工作流程"></a>2.2.2. OrthoFinder1推断直系同源群的工作流程</h3><p>OrthoFinder1推断直系同源群的分析过程分为如下几步:</p>
-<ol>
-<li>BLAST all-vs-all搜索。使用BLASTP以evalue&#x3D;10e-3进行搜索，寻找潜在的同源基因。(除了BLAST, 还可以选择DIAMOND和MMSeq2等其他搜索工具)。</li>
-<li>基于基因长度和系统发育距离对BLAST bit得分进行标准化。</li>
-<li>使用RBNHs方法，确定一个同源组序列性相似度的阈值，筛选出质量好的直系同源组(orthogroup graph)。</li>
-<li>构建直系同源组图(orthogroup graph)，通过归一化的比特值画出正交组的边缘连接，用作MCL的输入。</li>
-<li>使用MCL对基因进行聚类，划分直系同源组。</li>
+<ol start="4">
+<li>Quartet Fidelity (QF) score - 四重奏保真度分数</li>
 </ol>
-<h3 id="2-2-3-OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程"><a href="#2-2-3-OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程" class="headerlink" title="2.2.3. OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程"></a>2.2.3. OrthoFinder2在OrthoFinder1的基础上构建物种系统发育树的流程</h3><ol>
-<li>为每个直系同源组构建基因系统发育树；</li>
-<li>使用STAG算法从无根基因树上构建无根物种树；</li>
-<li>使用STRIDE算法构建有根物种树；</li>
-<li>有根物种树进一步辅助构建有根基因树；</li>
-<li>基于DLC(Duplication-Loss-Coalescent)分析模型，有根基因树可以用来推断物种形成和基因复制事件；</li>
-<li>最后把结果记录在统计信息中。</li>
+<ul>
+<li>对于每个末端分类单元，计算 QF 分数以报告总复制的比例（跨所有分支），包括导致一致四重奏拓扑的给定分类单元。</li>
+<li>因此，QF 的使用与“rogue taxon”测试的方法相似。</li>
+<li>然而，一个重要的区别是使用分类群完整的引导复制来计算这些分数而不是重新采样的子树，因此在系统发育分析中会遇到与引导得分本身相同的问题（即，当所有引导时，RogueNaRok 算法不会报告rogue分类分数为 100）。</li>
+<li>对于给定的分类单元，QF 分数在 [0,1] 范围，作为涉及与焦点树分支一致的分类单元的四重拓扑的比例。</li>
+<li>因此，QF 值为 1 表示当一个给定的分类单元在四重奏中采样时，总是在所有内部分支中产生一致的拓扑结构。</li>
+<li>QF 值接近 0 表明涉及该分类单元的拓扑结构大多不一致，也可能表明序列质量或比对性（identity）较差，或者表明存在使系统无序的特定的谱系，或者分类单元在给定树中明显错位。</li>
+<li>请注意，QF 与 QC、QD 和 QI 的具体不同之处在于它是跨内部分支评估的<strong>特定分类单元</strong>的评估，而不是内部分支的评估。</li>
+</ul>
+<h2 id="1-2-程序"><a href="#1-2-程序" class="headerlink" title="1.2. 程序"></a>1.2. 程序</h2><p>主要包含以下几个脚本：</p>
+<ol>
+<li>pysrc&#x2F;quartet_sampling.py：主程序，用于实现Quartet Sampling（QS）方法。</li>
+<li>pysrc&#x2F;merge_output.py：合并quartet_sampling.py程序的输出文件RESULT.node.scores.csv，当使用同一拓扑树运行多次时，用这个脚本合并多次运行的结果到一套结果。</li>
+<li>pysrc&#x2F;query_tree.py：树查询脚本，从运行后注释好的大型树中找到特定节点。</li>
+<li>pysrc&#x2F;utils&#x2F;calc_qstats.py：用于对quartet_sampling.py程序的输出文件RESULT.node.scores.csv进行基础统计。</li>
+<li>pysrc&#x2F;utils&#x2F;fasta2phy.py：用于把fasta格式序列转换成relaxed phylip格式。</li>
 </ol>
-<h2 id="2-3-OrthoFinder使用"><a href="#2-3-OrthoFinder使用" class="headerlink" title="2.3. OrthoFinder使用"></a>2.3. OrthoFinder使用</h2><h3 id="2-3-1-OrthoFinder安装"><a href="#2-3-1-OrthoFinder安装" class="headerlink" title="2.3.1. OrthoFinder安装"></a>2.3.1. OrthoFinder安装</h3><ol>
-<li><p>conda安装【推荐】<br>conda安装一次解决依赖问题：<code>conda install orthofinder</code></p>
-</li>
-<li><p>下载解压缩<br><a href="https://github.com/davidemms/OrthoFinder/releases">OrthoFinder最新版下载地址</a></p>
-</li>
+<h2 id="1-3-安装"><a href="#1-3-安装" class="headerlink" title="1.3. 安装"></a>1.3. 安装</h2><p>无需安装，在已安装python3和RAxML的基础上，脚本可直接运行。</p>
+<h2 id="1-4-quartet-sampling-py程序"><a href="#1-4-quartet-sampling-py程序" class="headerlink" title="1.4. quartet_sampling.py程序"></a>1.4. quartet_sampling.py程序</h2><p>quartet_sampling.py脚本用于进行Quartet Sampling（QS）计算。</p>
+<h3 id="1-4-1-输入"><a href="#1-4-1-输入" class="headerlink" title="1.4.1. 输入"></a>1.4.1. 输入</h3><p>拓扑树和比对序列中的样品名称必须一致和对应。可以有多余的序列（拓扑树上没有的），程序会忽略；但不能有拓扑树上出现但缺失序列的情况。</p>
+<ol>
+<li>拓扑树(sample.tre)</li>
 </ol>
-<p>下载后解压缩：<code>tar xzf OrthoFinder_source.tar.gz</code> or <code>tar xzf OrthoFinder.tar.gz</code></p>
-<p>即可使用：<code>python OrthoFinder_source/orthofinder.py -h</code> or <code>./OrthoFinder/orthofinder -h</code></p>
-<h3 id="2-3-2-OrthoFinder运行"><a href="#2-3-2-OrthoFinder运行" class="headerlink" title="2.3.2. OrthoFinder运行"></a>2.3.2. OrthoFinder运行</h3><h4 id="2-3-2-1-输入文件"><a href="#2-3-2-1-输入文件" class="headerlink" title="2.3.2.1. 输入文件"></a>2.3.2.1. 输入文件</h4><p>把需要分析的物种的组学蛋白序列文件(.fa,.faa,.fasta,.fas,.pep后缀)放在同一个目录下(例如workdirectory目录下)，然后用OrthoFinder的-f参数指定目录做分析。</p>
-<p>如果是下载的蛋白数据，使用最长转录本的蛋白序列即可。</p>
-<h4 id="2-3-2-2-简单版"><a href="#2-3-2-2-简单版" class="headerlink" title="2.3.2.2. 简单版"></a>2.3.2.2. 简单版</h4><p><code>orthofinder -f ./workdirectory</code> # -f指定数据目录</p>
-<h4 id="2-3-2-3-参数版"><a href="#2-3-2-3-参数版" class="headerlink" title="2.3.2.3. 参数版"></a>2.3.2.3. 参数版</h4><p><code>orthofinder -f ./workdirectory -t 24 -a 8 -M msa -S blast -A mafft -T raxml-ng</code></p>
 <ul>
-<li>-t指定比对线程;</li>
-<li>-a指定分析线程;</li>
-<li>-M指定推断基因树的方法: dendroblast(default)&#x2F;msa;</li>
-<li>-S指定比对软件: diamond(default)&#x2F;blast&#x2F;blast_gz&#x2F;mmseqs&#x2F;blast_nucl</li>
-<li>-A指定多序列比对(MSA)使用软件：mafft(default)&#x2F;muscle；需要指定-M msa</li>
-<li>-T指定画树软件：fasttree(default)&#x2F;raxml&#x2F;raxml-ng&#x2F;iqtree</li>
+<li>newick格式</li>
+<li>不需要枝长数据，如果有会被忽略</li>
+<li>建议去除方括号和方括号内的支持率</li>
 </ul>
-<h4 id="2-3-2-4-修改参数文件config-json"><a href="#2-3-2-4-修改参数文件config-json" class="headerlink" title="2.3.2.4. 修改参数文件config.json"></a>2.3.2.4. 修改参数文件config.json</h4><ol>
-<li>如果需要做更细致的参数设定，比如对调用软件blast的参数evalue阈值的修改，可以通过修改参数文件config.json来实现。</li>
-<li>config.json文件位置：下载安装的在安装目录下，conda安装的在conda的bin目录下。</li>
-<li>推荐修改值：</li>
+<ol start="2">
+<li>比对好的序列(sample_aligned.phy)</li>
 </ol>
 <ul>
-<li>raxml-ng参数修改为–model LG+G8+F –threads 2 –bs-trees 1000（default是–model LG+G4 –threads 1)；</li>
-<li>blastp和blastn的参数修改为 -evalue 0.00001（default是0.001）</li>
+<li>relaxed phylip格式</li>
+<li>DNA核酸序列或者氨基酸序列，如果是氨基酸序列在参数中加上<code>--amino-acid</code></li>
+<li><code>utils/fasta2phy.py</code>脚本可以把fasta格式转为relaxed phylip格式</li>
 </ul>
-<h4 id="2-3-2-5-RESTART"><a href="#2-3-2-5-RESTART" class="headerlink" title="2.3.2.5. RESTART"></a>2.3.2.5. RESTART</h4><p>如果需要重新运行，可以使用以下参数设定从哪个步骤开始运行。</p>
+<ol start="3">
+<li>partition分区文件【可选】</li>
+</ol>
 <ul>
-<li>-b  <dir> # Start OrthoFinder from pre-computed BLAST results in <dir></li>
-<li>-fg <dir> # Start OrthoFinder from pre-computed orthogroups in <dir></li>
-<li>-ft <dir> # Start OrthoFinder from pre-computed gene trees in <dir></li>
+<li>是可选的输入文件</li>
+<li>RAxML格式</li>
+<li>用作串联比对序列的分区，或者用作单基因树的基因分区。</li>
+<li>如果序列稀疏到某些分区包含空序列，建议用<code>--ignore-error</code>参数忽略空分区造成的RAxML错误。</li>
 </ul>
-<ol>
-<li>添加新物种<br>运行之后想要添加物种可以用<code>orthofinder -b previous_orthofinder_directory -f new_fasta_directory</code>而无需重新运行先前计算的 BLAST 搜索。</li>
+<h3 id="1-4-2-运行"><a href="#1-4-2-运行" class="headerlink" title="1.4.2. 运行"></a>1.4.2. 运行</h3><ol>
+<li>运行命令</li>
 </ol>
-<p>这会将“new_fasta_directory”中的每个物种添加到现有物种集中，重用所有以前的 BLAST 结果，仅执行新物种所需的新 BLAST 搜索并重新计算正交群。’previous_orthofinder_directory’ 是包含文件 ‘SpeciesIDs.txt’ 的 OrthoFinder ‘WorkingDirectory&#x2F;‘。</p>
+<ul>
+<li><code>python3 quartet_sampling.py --tree sample.tre --align sample_aligned.phy --reps 1000 --threads 4 --lnlike 2  --verbout</code></li>
+</ul>
 <ol start="2">
-<li>移除物种<br>OrthoFinder 允许您从以前的分析中删除物种。<br>在先前分析的“WorkingDirectory&#x2F;”中有一个名为“SpeciesIDs.txt”的文件。使用“#”字符注释掉要从分析中删除的任何物种，然后使用以下命令运行 OrthoFinder：<code>orthofinder -b previous_orthofinder_directory</code></li>
+<li>参数</li>
 </ol>
-<p>其中 ‘previous_orthofinder_directory’ 是包含文件 ‘SpeciesIDs.txt’ 的 OrthoFinder ‘WorkingDirectory&#x2F;‘。</p>
+<ul>
+<li>–tree：必需，指定拓扑树</li>
+<li>–align：必需，指定aligned序列</li>
+<li>–reps：必需，每个枝计算的重复次数，默认100，推荐1000</li>
+<li>–threads：必需，每个枝的重复的平行运算的线程，默认1</li>
+<li>–lnlike：log-likelihood阈值，默认2。用来指定最好似然树超过第二好似然树的最小差异（对每个枝来说）。推荐指定，如果不指定，则调用简单模式，用RAxML从比对序列推断树，不评估似然值，所有枝上的QI分数为0。</li>
+<li>–data-type：指定数据类型，默认nuc，可选amino，cat</li>
+<li>–engine：指定推断树和评估树模型似然值的软件，默认raxml-ng，可选raxml，paup，iqtree</li>
+<li>–partitions：指定分区文件（RAxML格式）。</li>
+<li>–genetrees：指定分区文件，用分区文件划分比对好的序列成分离的基因树区域。</li>
+<li>–ignore-errors：忽略RAxML和PAUP运行错误。</li>
+<li>–clade：执行CSV taxon list指定的特定枝的分析。</li>
+<li>–calc-qdstats：实验性参数，为QD树频率做卡方检验。只有脚本可用的时候才能用。</li>
+<li>–result-prefix：指定结果文件前缀</li>
+<li>–verbose：指定后提供更详尽的输出，生成RESULT.verbose文件。</li>
+<li>–verbout：指定后提供每个topology和QC的频率的输出，生成RESULT.verbout文件。</li>
+<li>–min-overlap 20000：在四分体中所有类群被采样需要的最少位点数，默认不设置，推荐20000。</li>
+</ul>
 <ol start="3">
-<li>同时添加和删除物种<br>前面两个选项可以结合起来，如上所述注释掉要删除的物种，然后使用命令：<br><code>orthofinder -b previous_orthofinder_directory -f new_fasta_directory</code></li>
+<li>速度</li>
 </ol>
-<h4 id="2-3-2-6-结果文件"><a href="#2-3-2-6-结果文件" class="headerlink" title="2.3.2.6. 结果文件"></a>2.3.2.6. 结果文件</h4><p>OrthoFinder的标准输出包括：直系同源组，直系同源基因，有根基因树，解析基因树，无根物种树、有根物种树，基因重复事件以及相关的统计数据。</p>
-<ol>
-<li><p><strong>Phylogenetic_Hierarchical_Orthogroups 文件夹</strong></p>
-<p> 从 2.4.0 版本开始，主要文件储存在这个目录下面，orthogroups目录被弃用。</p>
-<p> OrthoFinder 通过分析有根基因树来推断每个层次级别（即物种树中的每个节点）的 HOG、正交群。这是一种比所有其他方法使用的和以前由 OrthoFinder 使用的基于基因相似性&#x2F;图形的方法（已弃用的 Orthogroups&#x2F;Orthogroups.tsv 文件）更准确的正交群推断方法。</p>
-<p> 根据 Orthobench 基准，这些新的正交群比 OrthoFinder 2 正交群 (Orthogroups&#x2F;Orthogroups.tsv) 准确 12%。通过包括外群物种，可以进一步提高准确度（在 Orthobench 上准确度提高 20%），这有助于解释有根基因树。</p>
-</li>
+<ul>
+<li>速度很快，6Mb多的snp序列数据40分钟跑完。</li>
+</ul>
+<h3 id="1-4-3-结果文件"><a href="#1-4-3-结果文件" class="headerlink" title="1.4.3. 结果文件"></a>1.4.3. 结果文件</h3><ol>
+<li>RESULT.node.scores.csv</li>
 </ol>
 <ul>
-<li>参阅“Species_Tree&#x2F;SpeciesTree_rooted_node_labels.txt”以确定哪个 N?.tsv 文件包含您需要的正交群。</li>
-<li>N0.tsv是制表符分隔的文本文件。每行包含属于单个正交群的基因。来自每个正交群的基因被组织成列，每个物种一个。附加列给出了 HOG（分层正交群）ID 和基因树中确定 HOG 的节点（注意，这可能位于包含基因的进化枝的根上方）。该文件有效地替换了使用 MCL 进行马尔可夫聚类的Orthogroups&#x2F;Orthogroups.tsv 中的正交群。</li>
-<li>N1.txt, N2.tsv, … : Orthogroups 从对应物种树 N1、N2 等物种进化枝的基因树推断出来。现在可以在分析中包含外群物种，然后使用 HOG 文件获取为物种树中所选进化枝定义的正交群。</li>
+<li>csv格式文件，包含以下信息：</li>
+<li>node_label</li>
 </ul>
 <ol start="2">
-<li><strong>Orthologues 文件夹</strong></li>
+<li>RESULT.node.counts.csv</li>
 </ol>
 <ul>
-<li>Orthologues 目录包含每个物种的一个子目录，该子目录又包含每个成对物种比较的文件，列出该物种对之间的直向同源物。</li>
-<li>直向同源物可以是一对一、一对多或多对多，这取决于直向同源物分化后的基因复制事件。</li>
-<li>文件中的每一行都包含一个物种中的基因，这些基因是另一个物种中基因的直向同源物，并且每一行都与包含这些基因的直向群交叉引用。</li>
+<li>tsv格式文件，包含以下信息：</li>
 </ul>
 <ol start="3">
-<li><strong>Orthogroups 文件夹【2.4.0版本后弃用】</strong><br>从 2.4.0 版本开始，主要文件储存在Phylogenetic_Hierarchical_Orthogroups目录下面，orthogroups目录被弃用。</li>
+<li>RESULT.labeled.tre</li>
 </ol>
 <ul>
-<li>Orthogroups.tsv【2.4.0版本后弃用】：制表符分隔的文本文件。每行包含属于单个正交群的基因。来自每个正交群的基因被组织成列，每个物种一个。应改用 Phylogenetic_Hierarchical_Orthogroups&#x2F;N0.tsv 中的正交群。</li>
-<li>Orthogroups.txt【旧格式】：包含 Orthogroups.tsv 文件内容的 OrthoMCL 输出格式文件，列间用空格分隔。第一列是OG开头的orthogroups的ID号，冒号结束；后面不同数量的列是此orthogroup包含的各物种的基因ID。</li>
-<li>Orthogroups_UnassignedGenes.tsv：是制表符分隔的文本文件，其格式与 Orthogroups.csv 相同，但包含未分配给任何正交群的所有基因，即 MCL 中 未成功聚类（直系同源组中基因数 &gt;&#x3D; 1）的离散基因。</li>
-<li>Orthogroups.GeneCount.tsv：是一个制表符分隔的文本文件，其格式与 Orthogroups.csv 相同，但包含每个正交群中每个物种的基因数计数，可用于基因收缩扩张分析。</li>
-<li>Orthogroups_SingleCopyOrthologues.txt：是一个正交群列表，每个物种只包含一个基因，即它们包含一对一的直向同源物（单拷贝直系同源基因）。它们非常适合物种间比较和物种树推断。</li>
+<li>一棵newick树，每个中间枝都含有QS##标识符</li>
 </ul>
 <ol start="4">
-<li><strong>Gene_Trees 文件夹</strong></li>
+<li>RESULT.labeled.tre.freq&#x2F;qc&#x2F;qd&#x2F;qi</li>
 </ol>
 <ul>
-<li>记录了每个 orthogroup（gene_num &gt;&#x3D; 4）的有根基因树结构。</li>
+<li>一棵newick树，每个中间枝都含有一致性重复频率(frequency of concordant replicates)&#x2F;QC&#x2F;QD&#x2F;QI分数标识符</li>
 </ul>
 <ol start="5">
-<li><strong>Resolved Gene Trees文件夹</strong></li>
+<li>RESULT.labeled.tre.figtree</li>
 </ol>
 <ul>
-<li>为具有 4 个或更多序列的每个正交群推断出有根的系统发育树，并使用OrthoFinder的杂种-重叠&#x2F;重复-丢失溯祖模型(hybrid species-overlap&#x2F;duplication-loss coalescent model)进行解析。</li>
+<li>FigTree格式的树，包含所有QS分数和中间枝的QC&#x2F;QD&#x2F;QI分数。</li>
 </ul>
 <ol start="6">
-<li><strong>Species_Tree 文件夹</strong></li>
+<li>RESULT.run.stats</li>
 </ol>
 <ul>
-<li>SpeciesTree_rooted.txt：STAG物种树从所有orthogroups推断，含有内部节点STAG支持值，并使用STRIDE生根，计算出的有根物种树结构。</li>
-<li>SpeciesTree_rooted_node_labels.txt：与上述相同的树，但节点被赋予标签(N0,N1, . . . , N m N_0,N_1,…,N_mN0,N1,…,Nm)（而不是支持值）以允许其他结果文件交叉引用物种树中的分支&#x2F;节点（例如基因复制事件的位置）。</li>
-<li>Orthogroups_for_concatenated_alignment.txt：仅在 -M msa 模式下输出，列出了所有串联起来用于推断物种树的 orthogroup ID。</li>
+<li>记录了nonoverlapping_count</li>
 </ul>
 <ol start="7">
-<li><strong>Comparative_Genomics_Statistics 文件夹</strong></li>
+<li>RESULT.verbout</li>
 </ol>
 <ul>
-<li><p>Duplications_per_Orthogroup.tsv：是一个制表符分隔的文本文件，它给出了每个正交群中标识的重复数。此数据的主文件是 Gene_Duplication_Events&#x2F;Duplications.tsv。</p>
-</li>
-<li><p>Duplications_per_Species_Tree_Node.tsv：是一个制表符分隔的文本文件，它给出了识别为沿着物种树的每个分支发生的重复数。此数据的主文件是 Gene_Duplication_Events&#x2F;Duplications.tsv。</p>
-</li>
-<li><p>Orthogroups_SpeciesOverlaps.tsv：是一个制表符分隔的文本文件，其中包含作为方阵的每个物种对之间共享的正交群的数量。</p>
-</li>
-<li><p>OrthologuesStats_*.tsv：是制表符分隔的文本文件，包含矩阵给出每对物种之间一对一、一对多和多对多关系中的直向同源物数量。</p>
-<ul>
-<li>OrthologuesStats_one-to-one.tsv是每个物种对之间一对一直向同源物的数量。</li>
-<li>OrthologuesStats_many-to-many.tsv包含每个物种对的多对多关系中的直向同源物的数量（由于物种形成后两个谱系中的基因重复事件）。条目 (i,j) 是物种 i 中与物种 j 中的基因存在多对多直系关系的基因数。</li>
-<li>OrthologuesStats_one-to-many.tsv：条目 (i,j) 给出物种 i 中与物种 j 的基因处于一对多直系关系的基因数量。这里有一个示例结果文件的演练： https : &#x2F;&#x2F;github.com&#x2F;davidemms&#x2F;OrthoFinder&#x2F;issues&#x2F;259。</li>
-<li>OrthologuesStats_many-to-one.tsv：条目 (i,j) 给出物种 i 中与物种 j 的基因处于多对一直系关系的基因数量。这里有一个示例结果文件的演练： https : &#x2F;&#x2F;github.com&#x2F;davidemms&#x2F;OrthoFinder&#x2F;issues&#x2F;259。</li>
-<li>OrthologuesStats_Total.tsv包含任何多样性的每个物种的直向同源物对的总数。条目 (i,j) 是物种 i 中在物种 j 中具有直向同源物的基因总数。</li>
-</ul>
-</li>
-<li><p>Statistics_Overall.tsv：是一个制表符分隔的文本文件，其中包含有关正交群大小和分配给正交群的基因比例的一般统计信息。</p>
-</li>
-<li><p>Statistics_PerSpecies.tsv：是一个制表符分隔的文本文件，包含与 Statistics_Overall.csv 文件相同的信息，但针对每个单独的物种。</p>
-</li>
+<li>csv格式文件，每个topology和QC的频率，标题行是topo1,topo2,topo3,topou,qc,qd,qi。</li>
 </ul>
-<p>文件“Statistics_Overall.csv”和“Statistics_PerSpecies.csv”中的大部分术语都是不言自明的，其余的定义如下。</p>
-<ul>
-<li>Species-specific orthogroup：完全由来自一个物种的基因组成的正交群。</li>
-<li>G50：正交群中的基因数量，使得 50% 的基因处于该大小或更大的正交群中。</li>
-<li>O50：正交群的最小数量，使得 50% 的基因处于该大小或更大的正交群中。</li>
-<li>单拷贝正群：每个物种只有一个基因（并且没有更多）的正群。这些正交群是推断物种树和许多其他分析的理想选择。</li>
-<li>未分配基因：未与任何其他基因放在同一个群中的基因。</li>
-</ul>
-<ol start="8">
-<li><strong>Gene_Duplication_Events 文件夹</strong></li>
-</ol>
-<ul>
-<li>Duplications.tsv是一个制表符分隔的文本文件，它列出了通过检查每个正群基因树的每个节点识别出的所有基因复制事件，每行代表至少一次基因复制事件。</li>
-<li>Duplications.tsv的列分别是1.“Orthogroup”，2.“Species Tree node”（发生复制的物种树的分支，参见Species_Tree&#x2F;SpeciesTree_rooted_node_labels.txt），3.“Gene tree node”（与基因复制事件对应的节点，参见相应的orthogroup Resolved_Gene_Trees&#x2F;) 中的树；4.”Support”（存在复制基因的两个副本的预期物种的比例）；5.“Type”（”Terminal”：物种树终端分支上的重复，”Non-Terminal”：物种树内部分支上的重复，因此被多个物种共享，”Non-Terminal”：STRIDE检查基因树的拓扑结构在复制后应该是什么）；6.“Genes 1”（基因列表来自复制基因的一个副本，逗号分隔）；7.“Genes 2”（基因列表来自复制基因的另一个副本，逗号分隔）。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Orthogroup      Species Tree Node       Gene Tree Node  Support Type    Genes 1 Genes 2</span><br><span class="line">OG0000000       N5      n62     0.5     Non-Terminal    Mcandidum_pep_mc33950   Mdodecandrum_pep_DR005872, Mcandidum_pep_mc01326, Mdodecandrum_pep_DR006000, Mcandidum_pep_mc01492</span><br><span class="line">OG0000000       N5      n63     1.0     Non-Terminal    Mdodecandrum_pep_DR005872, Mcandidum_pep_mc01326        Mdodecandrum_pep_DR006000, Mcandidum_pep_mc01492</span><br><span class="line">OG0000000       N5      n177    0.5     Non-Terminal    Mdodecandrum_pep_DR002276, Mdodecandrum_pep_DR013872, Mcandidum_pep_mc36653, Mdodecandrum_pep_DR026891, Mcandidum_pep_mc22876, Mcandidum_pep_mc37513, Mdodecandrum_pep_DR026886 Mdodecandrum_pep_DR004265</span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>SpeciesTree_Gene_Duplications_0.5_Support.txt：提供了物种树分支上的上述重复的总和。它是一个 newick 格式的文本文件。每个节点或物种名称后面的数字是在导致节点&#x2F;物种的分支上发生的具有至少 50% 支持度的基因复制事件的数量。分支长度是标准分支长度，如 Species_Tree&#x2F;SpeciesTree_rooted.txt 中给出的。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">(((((Mcandidum.pep_7199:1,Mdodecandrum.pep_3425:1)N5_9299:1,Egrandis.pep_12485:1)N3_296:1,((Ptrichocarpa.pep_13300:1,(Csinensis.pep_5628:1,(Graimondii.pep_15253:1,Athaliana.pep_8750:1)N10_30:1)N8_5:1)N6_61:1,(Ppersica.pep_5798:1,(Mtruncatula.pep_23336:1,Csativus.pep_3576:1)N9_48:1)N7_12:1)N4_19:1)N2_96:1,Vvinifera.pep_7596:1)N1_535:1,Mguttatus.pep_8911:1)N0_1806;</span><br></pre></td></tr></table></figure>
-
-<ol start="9">
-<li><strong>Orthogroup_Sequences 文件夹</strong></li>
-</ol>
-<ul>
-<li>FASTA格式文件，每个正交群的 FASTA 文件给出了正交群中每个基因的氨基酸序列。</li>
-</ul>
-<ol start="10">
-<li><strong>Single_Copy_Orthologue_Sequences 文件夹</strong></li>
-</ol>
-<ul>
-<li>FASTA格式文件，包含每个单拷贝 Orthogroup 所包含的氨基酸的序列信息。</li>
-</ul>
-<ol start="11">
-<li><strong>WorkingDirectory 文件夹</strong></li>
-</ol>
-<ul>
-<li>OrthoFinder 运行所需的必需中间文件，如 DIAMOND 比对结果，STAG 输出的无根物种树等。</li>
-</ul>
-<ol start="12">
-<li><strong>MultipleSequenceAlignments 文件夹</strong></li>
-</ol>
-<ul>
-<li>此文件夹仅在 -M msa 模式下输出，均为 FASTA 格式文件。</li>
-<li>记录了每个 orthogroup 中序列间的多序列比对结果。</li>
-<li>记录了程序通过 CMSA 算法过滤后的 orthogroup 中各序列串联后的多序列比对结果，同时比对结果中空位数 &gt; 50% 的列已被删除。</li>
-</ul>
-<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
-<li>tutorial：<a href="https://davidemms.github.io/">https://davidemms.github.io/</a></li>
-<li>github：<a href="https://github.com/davidemms/OrthoFinder">https://github.com/davidemms/OrthoFinder</a></li>
-<li>paper：<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y</a></li>
-<li>xuzhougeng’s blog：<a href="https://xuzhougeng.top/archives/OrthoFinder2-fast-and-accurate-phylogenomic-orthology-analysis-from-gene-sequences">https://xuzhougeng.top/archives/OrthoFinder2-fast-and-accurate-phylogenomic-orthology-analysis-from-gene-sequences</a></li>
-<li><a href="https://blog.csdn.net/weixin_44614936/article/details/101640473">https://blog.csdn.net/weixin_44614936/article/details/101640473</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>orthology inference</category>
-      </categories>
-      <tags>
-        <tag>OrthoFinder2</tag>
-        <tag>orthology</tag>
-        <tag>OrthoMCL</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用软件Quartet Sampling来计算分支支持率，评估系统发育树不一致程度，区分强冲突和弱支持</title>
-    <url>/2022/11/08/bioinfo_phylogeny_QuartetSampling/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105143&auto=1&height=32"></iframe></div>
-
-
-<h1 id="1-Quartet-Sampling（QS）method"><a href="#1-Quartet-Sampling（QS）method" class="headerlink" title="1. Quartet Sampling（QS）method"></a>1. Quartet Sampling（QS）method</h1><p>Quartet Sampling（QS）是为系统发育树量化分支的支持率的一种方法，用于区分植物系统发育树上的强烈拓扑冲突和信息不足（缺乏支持）导致的支持率低，即区分强冲突（不一致）和弱支持（不确定性）。</p>
-<h2 id="1-1-原理"><a href="#1-1-原理" class="headerlink" title="1.1. 原理"></a>1.1. 原理</h2><h3 id="1-1-1-四重采样"><a href="#1-1-1-四重采样" class="headerlink" title="1.1.1. 四重采样"></a>1.1.1. 四重采样</h3><ol>
-<li>对于给定的系统发育树，每个内部枝将树划分为四个不重叠的分类群子集（图A）。</li>
-<li>这四组分类群（也被称为元-四重奏，meta-quartet）可以以三种拓扑关系存在：与给定拓扑一致的关系，以及两种替代的不一致关系（Replicate 1和Replicate 2）。</li>
-<li>QS方法从四个子集中的每一个子集中重复随机采样一个分类单元，然后基于给定序列数据（完全比对或随机采样的基因分区），评估跨越该特定分支的随机选择的四条序列（即四重奏，quartet）的所有三个可能系统发育拓扑的似然值。</li>
-<li>对于同一分支采样的所有四重奏，评估这四个采样分类群可以采用的所有三种可能拓扑的可能性。</li>
-<li>然后记录所有重复中具有最佳可能性的四重拓扑并制成表格。</li>
-<li>此过程生成一组计数（跨每个分支的所有复制），其中一致或两个不一致关系中的每一个都具有最佳可能性。</li>
-<li>该过程可以通过评估每个四重奏的完全比对的可能性（即在单矩阵框架中）或通过从多基因或全基因组比对（即在多基因树中）的单个基因&#x2F;分区比对中随机抽样来执行聚结框架）。</li>
-</ol>
-<img src="https://bsapubs.onlinelibrary.wiley.com/cms/asset/e55c64b7-cc9d-49bb-92c9-6e59e0661dde/ajb21016-fig-0001-m.jpg" width=90% title="四重采样方法的描述" align=center/>
-
-<p><strong><p align="center">图1. 四重采样方法的描述</strong><br>图源：<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/ajb2.1016">https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/ajb2.1016</a></p></p>
-<h3 id="1-1-2-评估分数"><a href="#1-1-2-评估分数" class="headerlink" title="1.1.2. 评估分数"></a>1.1.2. 评估分数</h3><p>QS 方法使用这些重新采样的四重树计数来计算焦点树的每个内部分支的三个分数(QC,QD,QI）和评估分类单元的QF分数。</p>
-<p>总的来说，这四个分数提供了区分信息一致性（Quartet Concordance，QC），次生进化历史的存在（Quartet Differential，QD），信息量（Quartet Informativeness，QI）和树中单个分类群的可靠性（Quartet Fidelity，QF）的方法。 </p>
-<p>QS方法把这些影响分开，而不是像系统发育支持的标准测量那样将它们混为一个单一的汇总支持率。</p>
-<ol>
-<li>Quartet Concordance (QC) score - 四重奏一致性分数</li>
-</ol>
-<ul>
-<li>QC分数是一种类似熵的度量，量化了四种分类群的三种拓扑之间的相对支持度。</li>
-<li>即支持率在三种拓扑中分配越平均，QC值越接近0；越不均，约远离0。</li>
-<li>当频率最高的拓扑与输入树一致时，QC 值在 (0,1] 范围内为正。</li>
-<li>当所有四重树与输入树的焦点分支一致时，QC 等于 1。</li>
-<li>当其中一个不一致的拓扑是最常见的采样四重奏，QC 值在 [-1,0) 范围内为负。</li>
-<li>当所有四重奏树都是两个不一致的系统发育拓扑之一时接近 -1。</li>
-<li>当三个拓扑中平均分配时，QC 等于 0（或者如果三个可能中只有两个被注册为在所有复制中具有最佳可能性，则在两个之间平均分配）。</li>
-</ul>
-<ol start="2">
-<li>Quartet Differential (QD) score - 四重奏差异分数</li>
-</ol>
-<ul>
-<li>QD分数使用基因渐渗的f和D统计量的逻辑（尽管使用四重拓扑比例，而不是位点频率），并测量两个不一致拓扑的采样比例之间的差异。</li>
-<li>QD 分数没有具体量化基因渗入，也没有识别基因渗入的分类群，但确实表明一种拓扑的频率高于另一种拓扑。</li>
-<li>QD 的低值表明在两个不一致的拓扑结构之间存在一个倾向性的拓扑结构，这也可能表明在给定分支上有背景谱系分选之外的有偏（biased）生物过程的，包括混杂变量，例如基因渗入、强烈的异质性、异质碱基组成等。</li>
-<li>QD 表示支持率在两种替代拓扑上的差异水平，在 [0,1] 范围内变化，值为 1 表示两棵替代拓扑树的比例没有偏斜（skew），值为0 表示采样的所有不一致树仅来自两种替代关系之一。</li>
-</ul>
-<ol start="3">
-<li>Quartet Informativeness (QI) score - 四重奏信息分数</li>
-</ol>
-<ul>
-<li>在给定最佳和次佳的差异阈值的基础上，最佳似然四重树的似然值超过了次优似然值的四重树。这种情况下，QI分数量化了给定分支的复制比例。</li>
-<li>该分数表明，当分子数据在拓扑上有效地模棱两可时（即，当三个可能的四重拓扑中的两个或所有三个具有几乎无法区分的似然分数时），重复不被视为一致或不一致。</li>
-<li>QI在 [0,1] 范围内变化，表示超过阈值的采样四重奏的比例。</li>
-<li>QI 值为 1 表示所有四重奏都提供信息，而值为 0 表示所有四重奏都不确定（即，对于给定分支没有重要信息）。</li>
-<li>QI 代表了分支的信息量，与 QC 和 QD 结合使用，以区分具有低信息的分支与具有冲突信息（即高度不一致）的分支。</li>
-</ul>
-<ol start="4">
-<li>Quartet Fidelity (QF) score - 四重奏保真度分数</li>
-</ol>
-<ul>
-<li>对于每个末端分类单元，计算 QF 分数以报告总复制的比例（跨所有分支），包括导致一致四重奏拓扑的给定分类单元。</li>
-<li>因此，QF 的使用与“rogue taxon”测试的方法相似。</li>
-<li>然而，一个重要的区别是使用分类群完整的引导复制来计算这些分数而不是重新采样的子树，因此在系统发育分析中会遇到与引导得分本身相同的问题（即，当所有引导时，RogueNaRok 算法不会报告rogue分类分数为 100）。</li>
-<li>对于给定的分类单元，QF 分数在 [0,1] 范围，作为涉及与焦点树分支一致的分类单元的四重拓扑的比例。</li>
-<li>因此，QF 值为 1 表示当一个给定的分类单元在四重奏中采样时，总是在所有内部分支中产生一致的拓扑结构。</li>
-<li>QF 值接近 0 表明涉及该分类单元的拓扑结构大多不一致，也可能表明序列质量或比对性（identity）较差，或者表明存在使系统无序的特定的谱系，或者分类单元在给定树中明显错位。</li>
-<li>请注意，QF 与 QC、QD 和 QI 的具体不同之处在于它是跨内部分支评估的<strong>特定分类单元</strong>的评估，而不是内部分支的评估。</li>
-</ul>
-<h2 id="1-2-程序"><a href="#1-2-程序" class="headerlink" title="1.2. 程序"></a>1.2. 程序</h2><p>主要包含以下几个脚本：</p>
-<ol>
-<li>pysrc&#x2F;quartet_sampling.py：主程序，用于实现Quartet Sampling（QS）方法。</li>
-<li>pysrc&#x2F;merge_output.py：合并quartet_sampling.py程序的输出文件RESULT.node.scores.csv，当使用同一拓扑树运行多次时，用这个脚本合并多次运行的结果到一套结果。</li>
-<li>pysrc&#x2F;query_tree.py：树查询脚本，从运行后注释好的大型树中找到特定节点。</li>
-<li>pysrc&#x2F;utils&#x2F;calc_qstats.py：用于对quartet_sampling.py程序的输出文件RESULT.node.scores.csv进行基础统计。</li>
-<li>pysrc&#x2F;utils&#x2F;fasta2phy.py：用于把fasta格式序列转换成relaxed phylip格式。</li>
-</ol>
-<h2 id="1-3-安装"><a href="#1-3-安装" class="headerlink" title="1.3. 安装"></a>1.3. 安装</h2><p>无需安装，在已安装python3和RAxML的基础上，脚本可直接运行。</p>
-<h2 id="1-4-quartet-sampling-py程序"><a href="#1-4-quartet-sampling-py程序" class="headerlink" title="1.4. quartet_sampling.py程序"></a>1.4. quartet_sampling.py程序</h2><p>quartet_sampling.py脚本用于进行Quartet Sampling（QS）计算。</p>
-<h3 id="1-4-1-输入"><a href="#1-4-1-输入" class="headerlink" title="1.4.1. 输入"></a>1.4.1. 输入</h3><p>拓扑树和比对序列中的样品名称必须一致和对应。可以有多余的序列（拓扑树上没有的），程序会忽略；但不能有拓扑树上出现但缺失序列的情况。</p>
-<ol>
-<li>拓扑树(sample.tre)</li>
-</ol>
-<ul>
-<li>newick格式</li>
-<li>不需要枝长数据，如果有会被忽略</li>
-<li>建议去除方括号和方括号内的支持率</li>
-</ul>
-<ol start="2">
-<li>比对好的序列(sample_aligned.phy)</li>
-</ol>
-<ul>
-<li>relaxed phylip格式</li>
-<li>DNA核酸序列或者氨基酸序列，如果是氨基酸序列在参数中加上<code>--amino-acid</code></li>
-<li><code>utils/fasta2phy.py</code>脚本可以把fasta格式转为relaxed phylip格式</li>
-</ul>
-<ol start="3">
-<li>partition分区文件【可选】</li>
-</ol>
-<ul>
-<li>是可选的输入文件</li>
-<li>RAxML格式</li>
-<li>用作串联比对序列的分区，或者用作单基因树的基因分区。</li>
-<li>如果序列稀疏到某些分区包含空序列，建议用<code>--ignore-error</code>参数忽略空分区造成的RAxML错误。</li>
-</ul>
-<h3 id="1-4-2-运行"><a href="#1-4-2-运行" class="headerlink" title="1.4.2. 运行"></a>1.4.2. 运行</h3><ol>
-<li>运行命令</li>
-</ol>
-<ul>
-<li><code>python3 quartet_sampling.py --tree sample.tre --align sample_aligned.phy --reps 1000 --threads 4 --lnlike 2  --verbout</code></li>
-</ul>
-<ol start="2">
-<li>参数</li>
-</ol>
-<ul>
-<li>–tree：必需，指定拓扑树</li>
-<li>–align：必需，指定aligned序列</li>
-<li>–reps：必需，每个枝计算的重复次数，默认100，推荐1000</li>
-<li>–threads：必需，每个枝的重复的平行运算的线程，默认1</li>
-<li>–lnlike：log-likelihood阈值，默认2。用来指定最好似然树超过第二好似然树的最小差异（对每个枝来说）。推荐指定，如果不指定，则调用简单模式，用RAxML从比对序列推断树，不评估似然值，所有枝上的QI分数为0。</li>
-<li>–data-type：指定数据类型，默认nuc，可选amino，cat</li>
-<li>–engine：指定推断树和评估树模型似然值的软件，默认raxml-ng，可选raxml，paup，iqtree</li>
-<li>–partitions：指定分区文件（RAxML格式）。</li>
-<li>–genetrees：指定分区文件，用分区文件划分比对好的序列成分离的基因树区域。</li>
-<li>–ignore-errors：忽略RAxML和PAUP运行错误。</li>
-<li>–clade：执行CSV taxon list指定的特定枝的分析。</li>
-<li>–calc-qdstats：实验性参数，为QD树频率做卡方检验。只有脚本可用的时候才能用。</li>
-<li>–result-prefix：指定结果文件前缀</li>
-<li>–verbose：指定后提供更详尽的输出，生成RESULT.verbose文件。</li>
-<li>–verbout：指定后提供每个topology和QC的频率的输出，生成RESULT.verbout文件。</li>
-<li>–min-overlap 20000：在四分体中所有类群被采样需要的最少位点数，默认不设置，推荐20000。</li>
-</ul>
-<ol start="3">
-<li>速度</li>
-</ol>
-<ul>
-<li>速度很快，6Mb多的snp序列数据40分钟跑完。</li>
-</ul>
-<h3 id="1-4-3-结果文件"><a href="#1-4-3-结果文件" class="headerlink" title="1.4.3. 结果文件"></a>1.4.3. 结果文件</h3><ol>
-<li>RESULT.node.scores.csv</li>
-</ol>
-<ul>
-<li>csv格式文件，包含以下信息：</li>
-<li>node_label</li>
-</ul>
-<ol start="2">
-<li>RESULT.node.counts.csv</li>
-</ol>
-<ul>
-<li>tsv格式文件，包含以下信息：</li>
-</ul>
-<ol start="3">
-<li>RESULT.labeled.tre</li>
-</ol>
-<ul>
-<li>一棵newick树，每个中间枝都含有QS##标识符</li>
-</ul>
-<ol start="4">
-<li>RESULT.labeled.tre.freq&#x2F;qc&#x2F;qd&#x2F;qi</li>
-</ol>
-<ul>
-<li>一棵newick树，每个中间枝都含有一致性重复频率(frequency of concordant replicates)&#x2F;QC&#x2F;QD&#x2F;QI分数标识符</li>
-</ul>
-<ol start="5">
-<li>RESULT.labeled.tre.figtree</li>
-</ol>
-<ul>
-<li>FigTree格式的树，包含所有QS分数和中间枝的QC&#x2F;QD&#x2F;QI分数。</li>
-</ul>
-<ol start="6">
-<li>RESULT.run.stats</li>
-</ol>
-<ul>
-<li>记录了nonoverlapping_count</li>
-</ul>
-<ol start="7">
-<li>RESULT.verbout</li>
-</ol>
-<ul>
-<li>csv格式文件，每个topology和QC的频率，标题行是topo1,topo2,topo3,topou,qc,qd,qi。</li>
-</ul>
-<h2 id="1-5-calc-qstats-py程序"><a href="#1-5-calc-qstats-py程序" class="headerlink" title="1.5. calc_qstats.py程序"></a>1.5. calc_qstats.py程序</h2><ol>
-<li>运行</li>
-</ol>
+<h2 id="1-5-calc-qstats-py程序"><a href="#1-5-calc-qstats-py程序" class="headerlink" title="1.5. calc_qstats.py程序"></a>1.5. calc_qstats.py程序</h2><ol>
+<li>运行</li>
+</ol>
 <ul>
 <li><code>/path/to/pysrc/utils/calc_qstats.py --data RESULT.node.scores.csv</code></li>
 </ul>
@@ -7067,302 +7022,247 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用PseudogenePipeline鉴定假基因及后续分析</title>
-    <url>/2022/09/07/bioinfo_pseudogene_PseudogenePipeline/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=95370&auto=1&height=32"></iframe></div>
+    <title>基因组WGD事件的鉴定和时间估算 —— MCScanX,KaKa_Calculator</title>
+    <url>/2021/11/27/bioinfo_synteny_MCScanX/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=29808885&auto=1&height=32"></iframe></div>
 
-<h1 id="1-假基因"><a href="#1-假基因" class="headerlink" title="1. 假基因"></a>1. 假基因</h1><p>假基因(pseudogene)是类似与功能基因的非功能性DNA片段。大多数是功能基因的多余拷贝，通常是由于移码突变或提前的终止密码子被鉴定为假基因。</p>
-<p>由于可能含有启动子元件，假基因有可能产生低水平的转录生成RNA，但通常无法产生功能性的最终蛋白产物。</p>
-<h1 id="2-PseudogenePipeline-简介"><a href="#2-PseudogenePipeline-简介" class="headerlink" title="2. PseudogenePipeline 简介"></a>2. PseudogenePipeline 简介</h1><p>PseudogenePipeline是Shiulab开发的一个鉴定假基因的流程包，v1.0.0版本基于python2，v2.0.0版本基于python3。</p>
-<h1 id="3-PseudogenePipeline-原理"><a href="#3-PseudogenePipeline-原理" class="headerlink" title="3. PseudogenePipeline 原理"></a>3. PseudogenePipeline 原理</h1><p>在基因组未被注释到基因的区域（基因间隔区）查找与基因相似的片段，大多是没有完整ORF区域或者基因的片段。使用tblastn进行基因转录翻译生成的氨基酸序列和基因组上基因间隔区的比对。</p>
-<h1 id="4-PseudogenePipeline-安装"><a href="#4-PseudogenePipeline-安装" class="headerlink" title="4. PseudogenePipeline 安装"></a>4. PseudogenePipeline 安装</h1><h2 id="4-1-依赖-requirements"><a href="#4-1-依赖-requirements" class="headerlink" title="4.1. 依赖-requirements"></a>4.1. 依赖-requirements</h2><ol>
-<li>python</li>
-<li>RepeatMasker</li>
-<li>tfasty：是fasta36包的一部分，version 36及以上 （<a href="https://faculty.virginia.edu/wrpearson/fasta/%EF%BC%89%EF%BC%8C%E5%AE%9E%E6%B5%8B%E7%94%A8%E7%9A%84fasta-36.3.8h%E7%89%88%E6%9C%AC%E3%80%82%E4%B8%8B%E8%BD%BD%E8%A7%A3%E5%8E%8B%E5%8D%B3%E5%8F%AF%E3%80%82">https://faculty.virginia.edu/wrpearson/fasta/），实测用的fasta-36.3.8h版本。下载解压即可。</a></li>
-</ol>
-<h2 id="4-2-下载"><a href="#4-2-下载" class="headerlink" title="4.2. 下载"></a>4.2. 下载</h2><p>在 <a href="https://github.com/ShiuLab/PseudogenePipeline">https://github.com/ShiuLab/PseudogenePipeline</a> 直接下载PseudogenePipeline，是python脚本包，无需安装即可使用。</p>
-<h1 id="5-PseudogenePipeline-使用"><a href="#5-PseudogenePipeline-使用" class="headerlink" title="5. PseudogenePipeline 使用"></a>5. PseudogenePipeline 使用</h1><h2 id="5-1-文件准备"><a href="#5-1-文件准备" class="headerlink" title="5.1. 文件准备"></a>5.1. 文件准备</h2><p>文件包括：</p>
-<ol>
-<li>genome.fa：基因组文件，建议用repeats.gff为基因组的重复序列做mask后使用genome.repeatmask.fa，以减少计算量</li>
-<li>sample.protein.fa：注释的基因对应的蛋白组</li>
-<li>tblastn.out：genome.repeatmask.fa和sample.protein.fa进行tblastn获得的输出文件</li>
-<li>sample.gff3：基因组的蛋白编码基因注释文件，有具体的格式要求</li>
-</ol>
-<h3 id="5-1-1-genome-mask文件"><a href="#5-1-1-genome-mask文件" class="headerlink" title="5.1.1. genome mask文件"></a>5.1.1. genome mask文件</h3><p>用注释的重复序列repeats.gff为基因组做genome mask，把重复序列所在位置的序列替换成N，减少假基因注释的计算量。获得masked的基因组文件genome.repeatmask.fa。</p>
-<h3 id="5-1-2-tblastn-out文件"><a href="#5-1-2-tblastn-out文件" class="headerlink" title="5.1.2. tblastn.out文件"></a>5.1.2. tblastn.out文件</h3><p>用<code>tblastn</code>为mask的基因组和注释的基因组蛋白序列做比对，比对结果用作PseudogenePipeline的输入。</p>
+<h1 id="1-MCScanX介绍"><a href="#1-MCScanX介绍" class="headerlink" title="1. MCScanX介绍"></a>1. MCScanX介绍</h1><p>MCScanX调整<a href="https://yanzhongsino.github.io/2021/11/05/bioinfo_MCscan/">MCScan算法</a>进行检测基因组间或基因组内的同线性，并附加了14个下游分析和可视化的脚本。</p>
+<h1 id="2-MCScanX同线性分析"><a href="#2-MCScanX同线性分析" class="headerlink" title="2. MCScanX同线性分析"></a>2. MCScanX同线性分析</h1><h2 id="2-1-种内同线性分析MCScanX"><a href="#2-1-种内同线性分析MCScanX" class="headerlink" title="2.1. 种内同线性分析MCScanX"></a>2.1. 种内同线性分析MCScanX</h2><h3 id="2-1-1-准备输入文件"><a href="#2-1-1-准备输入文件" class="headerlink" title="2.1.1. 准备输入文件"></a>2.1.1. 准备输入文件</h3><p>MCScanX做同线性分析需要两个输入文件sample.gff(四列数据)和sample.blast。</p>
+<p>如果是多个物种，则把多个物种的gff3文件和pep.fa文件合并后再准备sample.gff和sample.blast输入文件。</p>
 <ol>
-<li>运行</li>
-</ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">makeblastdb -in genome.repeatmask.fa -dbtype nucl -out sample # 建库</span><br><span class="line">tblastn -db dample -query sample.proteins.fa -num_threads 4 -out tblastn.out -outfmt 6 # tblastn比对，输出tblastn.out</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>notes</li>
+<li>sample.gff</li>
 </ol>
 <ul>
-<li>可能由于基因组大(6G)，多线程会中断，只能单线程，可以把蛋白文件sample.proteins.fa切分成多份，分别单线程tblastn，然后把结果合并。</li>
-<li>注意蛋白文件和基因组文件的序列id不能有空格，否则自动去除空格和空格后内容。</li>
-</ul>
-<h3 id="5-1-3-sample-gff3文件"><a href="#5-1-3-sample-gff3文件" class="headerlink" title="5.1.3. sample.gff3文件"></a>5.1.3. sample.gff3文件</h3><p>gff3文件格式类似普通的gff3格式，共九列。但有很具体的要求，主要是对第九列内容的要求，可参照软件给的例子进行增添修改以符合要求。</p>
-<p>具体格式要求：</p>
-<ul>
-<li>gene行，第九列包含<code>Name=信息</code>，值可与<code>ID=信息</code>一致</li>
-<li>mRNA行，第九列包含<code>longest=1</code>或者<code>longest=0</code>信息，用于可变剪切筛选，1代表最长可变剪切，0代表非最长可变剪切。</li>
-<li>mRNA,exon,CDS,UTR行，第九列都包含<code>pacid=信息</code>，值可与<code>Parent=信息</code>一致，一个gene内部的所有pacid值应相同。</li>
+<li>与gff3文件格式不一样，这里的sample.gff包含四列数据，第一列染色体ID，第二列基因ID，第三和第四列分别是起始和终止位置。</li>
+<li><code>cat sample.gene.gff3 |awk &#39;&#123;if($3==&quot;gene&quot;)&#123;print $1,$9,$4,$5&#125;&#125;&#39;|sed &quot;s/;.*;//g&quot;|sed &quot;s/ID=//g&quot;|sed &quot;s/ /\t/g&quot; &gt;sample.gff</code> 从gff3文件准备sample.gff文件</li>
 </ul>
-<h2 id="5-2-用PseudogenePipeline鉴定假基因"><a href="#5-2-用PseudogenePipeline鉴定假基因" class="headerlink" title="5.2. 用PseudogenePipeline鉴定假基因"></a>5.2. 用PseudogenePipeline鉴定假基因</h2><ol>
-<li>参数文件parameter_file示例</li>
-</ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">b_out=/path/to/tblastn.out # 前面准备的tblastn.out文件，&lt;tblastn_output_file&gt;</span><br><span class="line">p_seq=/path/to/sample.proteins.fa # 蛋白序列，&lt;protein_sequence_FASTA_file&gt;</span><br><span class="line">g_seq=/genome.repeatmask.fa # 基因组序列（最好是mask过的） &lt;genome_sequence_FASTA_File&gt;</span><br><span class="line">b_filter=y # 是否做blastout的filter，&lt;filter blastouput? y/n&gt;</span><br><span class="line">b_data=tabular # &lt;define type of blastdata: tabular, blastall, blastplus&gt;</span><br><span class="line">p_codes=/path/to/PseudogenePipeline-master/_pipeline_scripts #&lt;path to ../_pipeline_scripts&gt;</span><br><span class="line">o_codes=/path/to/PseudogenePipeline-master/_pipeline_scripts #&lt;path to ../_pipeline_scripts&gt;</span><br><span class="line">f_dir=/path/to/fasta-36.3.8h/bin #&lt;path to FASTA package install&gt;</span><br><span class="line">f_prog=tfasty36 #&lt;tfasty program name&gt;</span><br><span class="line">blosum=/path/to/PseudogenePipeline-master/_example_files/blosum50.matrix #&lt;path to blosum matrilx file&gt;</span><br><span class="line">gff=/path/to/sample.gff3 # gff3注释文件 &lt;gff_file&gt;</span><br><span class="line">repCut=300 #&lt;RepeatMasker cutoff; integer, recommended 300&gt;</span><br><span class="line">repDiv=30 #&lt;RepeatMakser divergence; integer, recommended 30&gt;</span><br></pre></td></tr></table></figure>
-
 <ol start="2">
-<li>运行</li>
+<li>sample.blast</li>
 </ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">makeblastdb -in sample.pep.fa -dbtype prot -out index/sample.pep #给蛋白序列建库</span><br><span class="line">blastp -query sample.pep.fa -db index/sample.pep -out sample.blast -evalue 1e-5 -num_threads 12 -outfmt 6 -num_alignments 5 &amp; #进行自我比对，生成6号格式的比对结果sample.blast</span><br></pre></td></tr></table></figure>
+
+<h3 id="2-1-2-运行MCScanX"><a href="#2-1-2-运行MCScanX" class="headerlink" title="2.1.2. 运行MCScanX"></a>2.1.2. 运行MCScanX</h3><p>在有sample.gff和sample.blast两个文件的目录下，指定前缀sample运行<code>MCScanX sample</code>。</p>
+<p>重要参数解释：</p>
 <ul>
-<li><code>python2 PseudogenePipeline-master/_wrapper_scripts/CombinedPseudoWrapper.py parameter_file</code></li>
+<li>-s MATCH_SIZE,default: 5。每个共线性区块包含的基因数量的下限。</li>
+<li>-m MAX_GAPS，default：25。在共线性区块中允许的最大gaps数量。</li>
+<li>-b patterns of collinear blocks。0:intra- and inter-species (default); 1:intra-species; 2:inter-species。</li>
 </ul>
-<h2 id="5-3-PseudogenePipeline运行结果"><a href="#5-3-PseudogenePipeline运行结果" class="headerlink" title="5.3. PseudogenePipeline运行结果"></a>5.3. PseudogenePipeline运行结果</h2><h3 id="5-3-1-结果文件"><a href="#5-3-1-结果文件" class="headerlink" title="5.3.1. 结果文件"></a>5.3.1. 结果文件</h3><p>在生成的结果目录result下，保存了结果文件，包括三个文件夹。result&#x2F;_intermediate保存着中间文件，result&#x2F;_logs保存着log文件，result&#x2F;_results保存着高置信并通过RepeatMasker过滤的鉴定出来的假基因结果文件。</p>
-<h4 id="5-3-1-1-results文件夹中的文件"><a href="#5-3-1-1-results文件夹中的文件" class="headerlink" title="5.3.1.1. _results文件夹中的文件"></a>5.3.1.1. _results文件夹中的文件</h4><p>result&#x2F;_results中的结果文件包括以下文件，每个文件第一行都是以<code>#python</code>开始的生成此文件的命令，其余行才保存了假基因的信息：</p>
+<h2 id="2-2-种间同线性分析MCScanX-h"><a href="#2-2-种间同线性分析MCScanX-h" class="headerlink" title="2.2. 种间同线性分析MCScanX_h"></a>2.2. 种间同线性分析MCScanX_h</h2><p>MCScanX_h是MCScanX软件下的一个程序，输入文件和运行方式与MCScanX很像。</p>
 <ol>
-<li>sample.4col.true.RMfilt.hiConf</li>
+<li>原理</li>
 </ol>
 <ul>
-<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
-<li>其余行是假基因和位置信息，共四列。第一列是假基因对应的真基因的ID（gene38168）加上假基因位置信息（ctg947|625093-625785）加上其他比对信息，分号分隔；第二列是染色体ID；第三第四列是start和end的坐标。</li>
-<li><code>gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0</code>表示在染色体<code>ctg947</code>的<code>625093-625785</code>位置有一个假基因pseudogene。</li>
-<li>对应的真基因是<code>gene38168</code>；<code>336-566</code>和<code>693-4</code>分别是指真基因序列和假基因序列比对上的区域；比对的信息是从tblastn的比对结果中获取的。</li>
-<li>真基因<code>gene38168</code>的<strong>氨基酸</strong>序列的<code>336-566</code>位置。</li>
-<li>假基因的<code>693-4</code>位置是指在染色体<code>ctg947</code>的<code>625093-625785</code>位置的假基因核酸序列上的第4到693个碱基，由于起始位置比终止位置大，所以是4-693位置的反向互补序列。</li>
-<li>下面是文件的部分内容。</li>
+<li>可用MCScanX_h分析指定基因对的同线性关系，用于确认orthofinder或者OrthoMCL等软件鉴定的物种间的orthologs基因对。</li>
+<li>如果要分析物种间orthologs的Ks分布来确定物种分化时间，直接用MCScanX做物种间共线性分析会同时得出orthologs和paralogs的结果，但MCScanX_h则可以指定orthofinder或者OrthoMCL鉴定的orthologs基因对。</li>
+<li>可以这样理解，orthofinder鉴定出物种A和物种B的orthologs（物种A和B各只有一个基因的那些行），然后用MCScanX_h确认鉴定的orthologs的同线性关系，从而更加确认orthologs的可靠性。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0	ctg947	625785	625096</span><br><span class="line">gene04393;ctg8706|272998-273192;228-290:190-4;2|0|1|0	ctg8706	273187	273001</span><br><span class="line">gene10546;ctg5903|105682-105807;115-156:126-4;1|0|0|0	ctg5903	105807	105685</span><br></pre></td></tr></table></figure>
-
 <ol start="2">
-<li>sample.4col.true.RMfilt.hiConf.cdnm</li>
+<li>运行的区别</li>
 </ol>
 <ul>
-<li>同文件sample.4col.true.RMfilt.hiConf的内容基本一致，区别在于第一列的内容，这里是假基因ID（Ps000001）和对应的真基因ID（gene38168）。</li>
-<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
-<li>其余行是假基因和位置信息，共四列。</li>
-<li>下面是文件的部分内容。</li>
+<li>区别是输入文件用sample.homology代替sample.blast。</li>
+<li>sample.homology是tab分隔的成对基因ID的list。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Ps000001_gene38168	ctg947	625785	625096</span><br><span class="line">Ps000003_gene04393	ctg8706	273187	273001</span><br><span class="line">Ps000004_gene10546	ctg5903	105807	105685</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>sample.4col.true.RMfilt.hiConf.cdnm.gff</li>
+<h3 id="2-2-1-输入文件"><a href="#2-2-1-输入文件" class="headerlink" title="2.2.1. 输入文件"></a>2.2.1. 输入文件</h3><ol>
+<li>sample.gff：格式同上面MCScanX的说明。</li>
+<li>sample.homology</li>
 </ol>
 <ul>
-<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
-<li>其余行是假基因的注释信息，gff格式，共九列。</li>
-<li>下面是文件的部分内容。</li>
+<li>包含两列数据，是两个物种的基因对，代表一一对应的同源关系。这里用来计算物种间分化，所以指定的是两个物种间一一对应的直系同源关系。</li>
+<li>可从orthofinder或者OrthoMCL等软件鉴定的物种间提取。</li>
+<li>从orthofinder的<code>./Results_Apr01/Orthogroups/Orthogroups.txt</code>文件提取只包含物种A和物种B各有一个基因的行。如果是多物种做的orthofinder，则提取只包含物种A和物种B各有一个基因的行后把其他物种删除。提取后把列间分隔符改成tab。（ps：用不同物种跑的orthofinder提取结果会有一点差异）</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">ctg947	ShiuLab	pseudogene	625096	625785	.	-	.	ID=Ps000001;Name=Ps000001_gene38168;Derives_from=gene38168;Note=pseudogene_evidence_code_4,0,0,0</span><br><span class="line">ctg8706	ShiuLab	pseudogene	273001	273187	.	-	.	ID=Ps000003;Name=Ps000003_gene04393;Derives_from=gene04393;Note=pseudogene_evidence_code_2,0,1,0</span><br><span class="line">ctg5903	ShiuLab	pseudogene	105685	105807	.	-	.	ID=Ps000004;Name=Ps000004_evm.model.ctg10546.26;Derives_from=gene10546;Note=pseudogene_evidence_code_1,0,0,0</span><br></pre></td></tr></table></figure>
-
-<ol start="4">
-<li>sample.4col.true.fa.RMfilt.hiConf</li>
-</ol>
+<h3 id="2-2-2-运行MCScanX-h"><a href="#2-2-2-运行MCScanX-h" class="headerlink" title="2.2.2. 运行MCScanX_h"></a>2.2.2. 运行MCScanX_h</h3><p>与运行MCScanX一样:</p>
 <ul>
-<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
-<li>其余行是假基因的序列信息，fasta格式，序列ID是假基因对应的真基因的ID（gene38168）加上假基因位置信息（ctg947|625093-625785）加上其他比对信息，分号分隔。序列ID同文件sample.4col.true.RMfilt.hiConf的第一列内容一致。</li>
-<li>下面是文件的部分内容。</li>
+<li>在有sample.gff和sample.homology两个文件的目录下，指定前缀sample运行<code>MCScanX_h sample</code>。</li>
+<li>参数也一致。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&gt;gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0</span><br><span class="line">TTGCAGGTTGCAATTTTGATGTCCAAGGACCAAGCACTATATTACGCATGGTTGTTCTCCCTAATAATTTATGAATTACTTAGAAAACCTACCTTGCGCAATGTAGAAGCGTTAGTGCAAACATGGCTCCGTGTCACACAATGCATGTCACGGTCAATTAACTTTAAGAAGGTAGCAAATCGTAAAAGGAGTTGGGAGCGTAGGAGAGCATGAGCTTATCCATGACCCCAGAGGTTTCTTGAGTGCTTTTTGTTGAGTGCATATAATGCAAACATGTTCAAATGTCGCTTGTGAGTGAGTAGAGAAACCTTCCAATACGTATGTGGCAAGGTCTCTCTTGCAATGAAGAAGGTGAGCACAAACATGCGTGTTCCACTTAGTATGGAAACAAAACTGGCAATAGCTCTCAGTAGGCTAGCTTTAGGTGCAGGGCTTGAGATGTTGGGTGGTTTGCATGGCTGTGCAAAGAGCACAAGTTGTAAAGTAGTGATTGATTTTTGTAAGGCAATTGTTACATCAGGTCTTAGGGATTTGTACATTAGGTGGCCTGCCCTCTCATGGCTAGAGACACTAGCAAGTGAGTTTCAAGCTTCAAGATGCATCCCCTTTGTTGCTAGAGCCATAGACAGTTCTCACATCCCTATCATCGCCCCAAGAGACAATCACGTAGATTATTTCAATTGAAAAGGG</span><br><span class="line">&gt;gene04393;ctg8706|272998-273192;228-290:190-4;2|0|1|0</span><br><span class="line">TATATGTCGTGATATCACAGCAATCAATCTATCATTAGAGAACTTAGATGATTTGTATAAGGTGTGTGGGTTCATACATGGCTTGGACATGAAGTACAAGCACAACATACGTACTCAAAACCCCAAAGACCCCATAGATGCTATTTAAGGTTCTCAAATCTATAATGACTCTCTTGATAAGAGGAGC</span><br><span class="line">&gt;gene10546;ctg5903|105682-105807;115-156:126-4;1|0|0|0</span><br><span class="line">GGCATGGATGATGACCAATTTCTTTCTTTCTTGGACCTCAAGCAGAGGAGGTGGTATGAGGACCGCCATTGCTCCAAAGCATAGTCACGTTGTTCCACTCCTTCAGAGTCTCCAACTGCTTTG</span><br></pre></td></tr></table></figure>
-
-<ol start="5">
-<li>sample.4col.true.fa.RMfilt.hiConf.cdnm</li>
+<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><ol>
+<li>sample.collinearity</li>
 </ol>
+<p>共线性结果文件，包括三部分内容：</p>
 <ul>
-<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
-<li>其余行是假基因的序列信息，fasta格式，序列ID是假基因ID（Ps000001）和对应的真基因ID（gene38168）。序列ID同文件sample.4col.true.RMfilt.hiConf.cdnm的第一列内容一致。</li>
-<li>下面是文件的部分内容。</li>
+<li>参数(parameters)</li>
+<li>基本统计信息(statistics)：共线性基因的总数，总基因数，共线性基因占比。</li>
+<li>共线性区块(block)信息：一个Alignment代表一个共线性区块（0起始编号）。后面跟着这个共线性区块的基因对的信息。第一列：block编号；第二列：基因对编号；第三列和第四列：基因对名称；第五列：blast比对的e_value值。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&gt;Ps000001_gene38168</span><br><span class="line">TTGCAGGTTGCAATTTTGATGTCCAAGGACCAAGCACTATATTACGCATGGTTGTTCTCCCTAATAATTTATGAATTACTTAGAAAACCTACCTTGCGCAATGTAGAAGCGTTAGTGCAAACATGGCTCCGTGTCACACAATGCATGTCACGGTCAATTAACTTTAAGAAGGTAGCAAATCGTAAAAGGAGTTGGGAGCGTAGGAGAGCATGAGCTTATCCATGACCCCAGAGGTTTCTTGAGTGCTTTTTGTTGAGTGCATATAATGCAAACATGTTCAAATGTCGCTTGTGAGTGAGTAGAGAAACCTTCCAATACGTATGTGGCAAGGTCTCTCTTGCAATGAAGAAGGTGAGCACAAACATGCGTGTTCCACTTAGTATGGAAACAAAACTGGCAATAGCTCTCAGTAGGCTAGCTTTAGGTGCAGGGCTTGAGATGTTGGGTGGTTTGCATGGCTGTGCAAAGAGCACAAGTTGTAAAGTAGTGATTGATTTTTGTAAGGCAATTGTTACATCAGGTCTTAGGGATTTGTACATTAGGTGGCCTGCCCTCTCATGGCTAGAGACACTAGCAAGTGAGTTTCAAGCTTCAAGATGCATCCCCTTTGTTGCTAGAGCCATAGACAGTTCTCACATCCCTATCATCGCCCCAAGAGACAATCACGTAGATTATTTCAATTGAAAAGGG</span><br><span class="line">&gt;Ps000003_gene04393</span><br><span class="line">TATATGTCGTGATATCACAGCAATCAATCTATCATTAGAGAACTTAGATGATTTGTATAAGGTGTGTGGGTTCATACATGGCTTGGACATGAAGTACAAGCACAACATACGTACTCAAAACCCCAAAGACCCCATAGATGCTATTTAAGGTTCTCAAATCTATAATGACTCTCTTGATAAGAGGAGC</span><br><span class="line">&gt;Ps000004_gene10546</span><br><span class="line">GGCATGGATGATGACCAATTTCTTTCTTTCTTGGACCTCAAGCAGAGGAGGTGGTATGAGGACCGCCATTGCTCCAAAGCATAGTCACGTTGTTCCACTCCTTCAGAGTCTCCAACTGCTTTG</span><br></pre></td></tr></table></figure>
+<p>sample.collinearity示例：</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">############### Parameters ###############</span><br><span class="line"># MATCH_SCORE: 50</span><br><span class="line"># MATCH_SIZE: 5</span><br><span class="line"># GAP_PENALTY: -1</span><br><span class="line"># OVERLAP_WINDOW: 5</span><br><span class="line"># E_VALUE: 1e-05</span><br><span class="line"># MAX GAPS: 25</span><br><span class="line">############### Statistics ###############</span><br><span class="line"># Number of collinear genes: 21371, Percentage: 56.25</span><br><span class="line"># Number of all genes: 37996</span><br><span class="line">##########################################</span><br><span class="line">## Alignment 0: score=352.0 e_value=1.2e-17 N=8 scaf014&amp;scaf014 plus</span><br><span class="line">477-  0:	bv37796	bv37889	  3e-06</span><br><span class="line">477-  1:	bv37820	bv37905	  6e-24</span><br><span class="line">477-  2:	bv37822	bv37926	  9e-29</span><br><span class="line">477-  3:	bv37826	bv37930	  4e-62</span><br><span class="line">477-  4:	bv37828	bv37931	  1e-74</span><br><span class="line">477-  5:	bv37829	bv37932	 3e-142</span><br><span class="line">477-  6:	bv37830	bv37933	  6e-78</span><br><span class="line">477-  7:	bv37832	bv37934	  3e-76</span><br><span class="line">## Alignment 1: score=300.0 e_value=6.1e-08 N=6 scaf014&amp;scaf014 minus</span><br><span class="line">478-  0:	bv35514	bv35525	      0</span><br><span class="line">478-  1:	bv35515	bv35524	      0</span><br><span class="line">478-  2:	bv35516	bv35523	  1e-81</span><br><span class="line">478-  3:	bv35517	bv35522	      0</span><br><span class="line">478-  4:	bv35518	bv35521	 1e-115</span><br><span class="line">478-  5:	bv35519	bv35520	      0</span><br><span class="line">... ...</span><br><span class="line">... ...</span><br></pre></td></tr></table></figure>
 
-<h4 id="5-3-1-2-intermediate文件夹中的文件"><a href="#5-3-1-2-intermediate文件夹中的文件" class="headerlink" title="5.3.1.2. _intermediate文件夹中的文件"></a>5.3.1.2. _intermediate文件夹中的文件</h4><p><code>result/_intermediate</code>中的结果文件包括以下文件，许多文件第一行都是以<code>#python</code>开始的生成此文件的命令，其余行才保存了信息：</p>
-<ol>
-<li>sample.fullyFiltered.disable_count</li>
-</ol>
-<ul>
-<li>第一行都是以<code>#python</code>开始的生成此文件的命令</li>
-<li>保存着假基因和对应真基因的氨基酸的比对好的序列</li>
-</ul>
 <ol start="2">
-<li>sample.fullyFiltered.disable_count.RMfilt</li>
+<li>sample.html</li>
 </ol>
+<p>网页文件所在的文件夹，里面有每条染色体一个html文件。html文件用浏览器打开，包含三列信息。</p>
 <ul>
-<li>前两行都是以<code>#python</code>开始的生成此文件的命令</li>
-<li>保存着假基因和对应真基因的氨基酸的比对好的序列</li>
-</ul>
-<h3 id="5-3-2-notes"><a href="#5-3-2-notes" class="headerlink" title="5.3.2. notes"></a>5.3.2. notes</h3><ul>
-<li>注释到的假基因之间是没有重叠的，一个位置的序列只能被注释为一个假基因。</li>
-<li>建议做一个长度的过滤，比如长度大于300bp的假基因才被鉴定为假基因，进入到下一步的分析流程。</li>
-<li>result&#x2F;_intermediate文件夹中保存着额外的文件，比如sample.fullyFiltered.disable_count.RMfilt文件保存着假基因和对应真基因比对好的蛋白质序列。</li>
-</ul>
-<h2 id="5-4-PseudogenePipeline运行的一个例子"><a href="#5-4-PseudogenePipeline运行的一个例子" class="headerlink" title="5.4. PseudogenePipeline运行的一个例子"></a>5.4. PseudogenePipeline运行的一个例子</h2><p>运行相关数据：</p>
-<ul>
-<li>6Gb大小的蕨类基因组，跑了70h。</li>
-<li>70000个注释的真基因，软件结果中有222,372个假基因，其中长度大于300bp的为93594条。</li>
+<li>第一列是复制深度。</li>
+<li>第二列是这条染色体上所有基因的排列顺序，串联重复基因的背景为红色。</li>
+<li>第三列和之后列是对应的比对上的基因名称。</li>
 </ul>
-<h1 id="6-假基因后续分析"><a href="#6-假基因后续分析" class="headerlink" title="6. 假基因后续分析"></a>6. 假基因后续分析</h1><p>鉴定假基因后，可以通过进一步的分析来获取更多认识。</p>
-<h2 id="6-1-批量计算pseudogene和功能性gene之间的Ks"><a href="#6-1-批量计算pseudogene和功能性gene之间的Ks" class="headerlink" title="6.1. 批量计算pseudogene和功能性gene之间的Ks"></a>6.1. 批量计算pseudogene和功能性gene之间的Ks</h2><p>通过计算pseudogene和功能性基因（真基因）间的Ks，可以判断是否与其他进化事件的Ks重合（比如WGD），也可以通过Ks计算假基因产生的时间的分布。</p>
-<h3 id="6-1-1-背景"><a href="#6-1-1-背景" class="headerlink" title="6.1.1. 背景"></a>6.1.1. 背景</h3><ol>
-<li>Ks只能计算ORF区域的（即完整密码子，3的倍数的碱基），并遇到stop codon停止。</li>
-<li>根据假基因的注释信息，找到pseudogene和对应真基因的序列，并把pseudogene和对应真基因的ORF区域提取出来。</li>
-<li>若pseudogene比对到了intron区域，删除这部分，并从真基因密码子首位开始匹配pseudogene，提取完整密码子（3的倍数个碱基）。</li>
-<li>只需要比对上的密码子区域，并要求3的倍数的碱基；而假基因常常包含许多中间的终止密码子，所以这些中间终止密码子和对应位置的真基因的三个碱基也需要删除。（还有一种策略是从第一个终止密码子处截断，之后的序列全都不要，但这种策略常常会使得提取出来的序列太短，造成更大误差，所以不推荐。）</li>
-</ol>
-<p>准备以下三个输入文件，就可以根据 <strong>博客：批量计算Ka和Ks：<a href="https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks">https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks</a></strong> 进行Ks的批量计算。</p>
-<h3 id="6-1-2-准备sample-pairs文件"><a href="#6-1-2-准备sample-pairs文件" class="headerlink" title="6.1.2. 准备sample.pairs文件"></a>6.1.2. 准备sample.pairs文件</h3><p>sample.pairs保存着两列数据，tab分隔，第一列是假基因的ID，第二列是对应的真基因的ID。</p>
-<p>可以用下面的命令从结果文件中提取假基因和对应的真基因的ID，获得sample.pairs。</p>
-<p><code>cat ./_results/sample.4col.true.RMfilt.hiConf.cdnm  |grep -v &quot;#&quot;|sed -e &quot;s/\t.*//g&quot; -e &quot;s/_/\t/&quot; &gt;sample.pairs </code></p>
-<h3 id="6-1-3-准备用于计算Ks的CDS序列文件-all-cds-和氨基酸序列文件-all-pep"><a href="#6-1-3-准备用于计算Ks的CDS序列文件-all-cds-和氨基酸序列文件-all-pep" class="headerlink" title="6.1.3. 准备用于计算Ks的CDS序列文件(all.cds)和氨基酸序列文件(all.pep)"></a>6.1.3. 准备用于计算Ks的CDS序列文件(all.cds)和氨基酸序列文件(all.pep)</h3><p>用于计算Ks的CDS序列文件需要包括所有假基因和对应真基因的CDS序列，用于计算Ks的氨基酸序列文件需要包括所有假基因和对应真基因的氨基酸序列。</p>
-<p>注意：all.cds和all.pep可以包含多余的序列但不能少。因为是根据sample.pairs文件的指定序列对计算Ks的。</p>
-<ol>
-<li>提取cds序列</li>
+<ol start="3">
+<li>sample.tandem</li>
 </ol>
+<p>包含基因组内<strong>串联重复</strong>的基因ID的list。</p>
+<p>notes：MCScanX 会根据 gff 文件中染色体号的前缀（前2个字符）将染色体划分为不同的物种，若 MCScanX 识别到输入数据中包含多个物种，则不会生成 tandem 文件。</p>
+<h1 id="3-MCScanX下游分析"><a href="#3-MCScanX下游分析" class="headerlink" title="3. MCScanX下游分析"></a>3. MCScanX下游分析</h1><h2 id="3-1-提取block位置"><a href="#3-1-提取block位置" class="headerlink" title="3.1. 提取block位置"></a>3.1. 提取block位置</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">cat sample.collinearity|grep -C 1 &quot;Alignment&quot;|sed &#x27;/^--$/d&#x27; &gt;block.tem #提取block首尾基因对</span><br><span class="line">tail -1 sample.collinearity &gt;&gt;block.tem #提取block首尾基因对</span><br><span class="line">cat block.tem |sed -e &quot;s/## //g&quot; -e &quot;s/.*gene/gene/g&quot;|tr &#x27;\n&#x27; &#x27; &#x27;|sed &quot;s/Alignment /\nAlignment/g&quot; |sed -e &quot;/^$/d&quot; -e &quot;s/ /\t/g&quot;|sed -e &quot;s/\t\+/\t/g&quot; -e &quot;s/\://g&quot;|awk -v FS=&quot;\t&quot; -v OFS=&quot;\t&quot; &#x27;&#123;print $1,$2,$3,$4,$5,$6,$7,$10,$8,$11&#125;&#x27; &gt;block.txt #整理成一个block一行信息的格式，其中&quot;s/.*gene/gene/g&quot;部分的gene修改成geneID的前缀用来去除多余信息。</span><br></pre></td></tr></table></figure>
+
+<h2 id="3-2-绘图脚本"><a href="#3-2-绘图脚本" class="headerlink" title="3.2. 绘图脚本"></a>3.2. 绘图脚本</h2><p>MCScanX的downstream_analyses目录包含一些下游脚本。</p>
+<p>许多java脚本可以实现绘图功能，包括：</p>
 <ul>
-<li>用cufflinks的gffread工具从gff文件提取CDS序列: <code>gffread sample.4col.true.RMfilt.hiConf.cdnm.gff -g genome.fa -x pseudo.cds</code></li>
-<li>另一种方法直接从结果文件中提取cds序列：<code>cat sample.4col.true.fa.RMfilt.hiConf.cdnm |grep -v &quot;#&quot; &gt;pseudo.cds</code></li>
+<li>共线性点图：dot_plotter</li>
+<li>双染色体共线性图：dual_synteny_plotter</li>
+<li>共线性圈图：circle_plotter</li>
+<li>染色体块图：bar_plotter</li>
+<li>…</li>
 </ul>
+<h2 id="3-3-复制事件分类"><a href="#3-3-复制事件分类" class="headerlink" title="3.3. 复制事件分类"></a>3.3. 复制事件分类</h2><h3 id="3-3-1-duplicate-gene-classifier"><a href="#3-3-1-duplicate-gene-classifier" class="headerlink" title="3.3.1. duplicate_gene_classifier"></a>3.3.1. duplicate_gene_classifier</h3><ol>
+<li>duplicate_gene_classifier</li>
+</ol>
+<p>MCScanX的<code>duplicate_gene_classifier</code>脚本用来分析与各种复制类别相关的基因数量</p>
 <ol start="2">
-<li>合并假基因CDS序列和基因组所有真基因的CDS序列（sample.cds）到一个文件all.cds</li>
+<li>输入</li>
 </ol>
-<ul>
-<li><code>cat pseudo.cds sample.cds &gt; all.cds</code></li>
-</ul>
+<p><code>duplicate_gene_classifier sample</code> # 输入与MCScanX一致，读取当前目录下的sample.gff和sample.blast作为输入</p>
 <ol start="3">
-<li>用cufflinks的gffread工具从gff文件提取pep序列</li>
+<li>输出示例</li>
 </ol>
-<ul>
-<li><code>gffread sample.4col.true.RMfilt.hiConf.cdnm.gff -g genome.fa -y pseudo.pep</code></li>
-</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Type of dup	Code	Number</span><br><span class="line">Singleton	0	88907</span><br><span class="line">Dispersed	1	13573</span><br><span class="line">Proximal	2	463</span><br><span class="line">Tandem	3	847</span><br><span class="line">WGD or segmental	4	1055</span><br></pre></td></tr></table></figure>
+
 <ol start="4">
-<li>合并假基因氨基酸序列和基因组所有真基因的氨基酸序列（sample.pep）到一个文件all.pep</li>
+<li>结果解释</li>
 </ol>
+<p>其中 Singleton 表示单拷贝重复，Proximal 表示在相同染色体上相近但不相连的重复，Dispersed 表示除 Tandem、WGD、Proximal 以外的重复。Number代表相应复制类别相关的基因数量。</p>
+<h2 id="3-4-解析结果文件sample-collinearity的脚本"><a href="#3-4-解析结果文件sample-collinearity的脚本" class="headerlink" title="3.4. 解析结果文件sample.collinearity的脚本"></a>3.4. 解析结果文件sample.collinearity的脚本</h2><p>推荐这里<a href="https://github.com/reubwn/collinearity%E6%9C%89%E8%AE%B8%E5%A4%9A%E5%8F%AF%E4%BB%A5%E8%A7%A3%E6%9E%90%E6%9E%9C%E6%96%87%E4%BB%B6sample.collinearity%E7%9A%84%E8%84%9A%E6%9C%AC">https://github.com/reubwn/collinearity有许多可以解析果文件sample.collinearity的脚本</a></p>
+<h2 id="3-5-计算共线性区块Ks"><a href="#3-5-计算共线性区块Ks" class="headerlink" title="3.5. 计算共线性区块Ks"></a>3.5. 计算共线性区块Ks</h2><p>tips:需要检查得到的结果中Ks值是否有负值或NA等无效数据，并过滤无效数据。</p>
+<h3 id="3-5-1-方案一：add-ka-and-ks-to-collinearity-pl（MCScanX的downstream-analyses目录下自带脚本）"><a href="#3-5-1-方案一：add-ka-and-ks-to-collinearity-pl（MCScanX的downstream-analyses目录下自带脚本）" class="headerlink" title="3.5.1. 方案一：add_ka_and_ks_to_collinearity.pl（MCScanX的downstream_analyses目录下自带脚本）"></a>3.5.1. 方案一：add_ka_and_ks_to_collinearity.pl（MCScanX的downstream_analyses目录下自带脚本）</h3><p><code>add_ka_and_ks_to_collinearity.pl -i sample.collinearity -d sample.cds.fa -o sample.kaks &gt; out.log 2&gt;&amp;1</code> #注意算出的部分kaks值为-2的问题，未找到解决方案</p>
+<h3 id="3-5-2-方案二：ParaAT-pl-KaKs-Calculator2-0"><a href="#3-5-2-方案二：ParaAT-pl-KaKs-Calculator2-0" class="headerlink" title="3.5.2. 方案二：ParaAT.pl+KaKs_Calculator2.0"></a>3.5.2. 方案二：ParaAT.pl+KaKs_Calculator2.0</h3><ul>
+<li>ParaAT.pl用于根据同源基因对list生成比对的gene对cds序列，并可以指定输出格式，如axt格式；</li>
+<li>KaKs_Calculator用于计算基因对的kaks。</li>
+</ul>
+<p>用还会用到两个脚本:</p>
 <ul>
-<li><code>cat pseudo.pep sample.pep &gt; all.pep</code></li>
+<li><a href="https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py">axt2one-line.py</a>转换axt格式为单行</li>
+<li><a href="https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl">calculate_4DTV_correction.pl</a>计算4dtv。</li>
 </ul>
-<h3 id="6-1-4-计算Ks"><a href="#6-1-4-计算Ks" class="headerlink" title="6.1.4. 计算Ks"></a>6.1.4. 计算Ks</h3><p>准备好以上三个文件：sample.pair, all.cds, all.pep 即可用ParaAT.pl和KaKs_Calculator2.0两个软件，参考<strong>博客：批量计算Ka和Ks：<a href="https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks">https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks</a></strong>的步骤计算假基因和对应真基因间的Ks，从而获知遗传上的分化及分布。</p>
-<h2 id="6-2-检测假基因和共线性区块的关系"><a href="#6-2-检测假基因和共线性区块的关系" class="headerlink" title="6.2. 检测假基因和共线性区块的关系"></a>6.2. 检测假基因和共线性区块的关系</h2><p>在2022年08月在GBE上发表的荷叶铁线蕨基因组的文章（<a href="https://academic.oup.com/gbe/article/14/8/evac127/6659224%EF%BC%89%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E5%BA%94%E7%94%A8%EF%BC%8C%E5%88%A4%E6%96%AD%E6%98%AF%E5%90%A6%E6%98%AFpseudogene%E9%80%A0%E6%88%90%E5%85%B1%E7%BA%BF%E6%80%A7blocks%E5%8F%98%E7%9F%AD%EF%BC%8C%E7%94%A8%E5%88%B0%E7%9A%84%E6%96%B9%E6%B3%95%E7%BB%86%E8%8A%82%E8%AE%B0%E5%BD%95%E5%9C%A8%E6%AD%A4%E3%80%82">https://academic.oup.com/gbe/article/14/8/evac127/6659224）中的一个应用，判断是否是pseudogene造成共线性blocks变短，用到的方法细节记录在此。</a></p>
 <ol>
-<li>分析MCScanX结果中找到的共线性blocks，看blocks是否有成对出现在同一个contig上的，两对出现在同一contig上的blocks是否分别有共线性对应。</li>
+<li>安装ParaAT.pl<br>参考<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align.pep2cds/">blog.ParaAT</a></li>
 </ol>
-<ul>
-<li>结果是不存在符合条件的两对blocks。</li>
-<li>原因是contig的长度短造成的，contig长度在1-2Mbp，一个block含5个及以上基因，就占据了一条contig的全长，所以同一个contig上有多个block出现的概率变小。</li>
-</ul>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget ftp://download.big.ac.cn/bigd/tools/ParaAT2.0.tar.gz</span><br><span class="line">tar -zxf ParaAT2.0.tar.gz</span><br><span class="line">cd ParaAT2.0</span><br><span class="line">ParaAT.pl -h</span><br></pre></td></tr></table></figure>
+
 <ol start="2">
-<li>在最长的blocks里找pseudogene造成blocks内部gene无共线性的例子。即block内部无共线性的gene对应block上对面位置是否有这个gene的pseudogene存在。</li>
+<li>安装KaKs_Calculator2.0<br><a href="https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect">KaKs_Calculator2.0下载地址</a></li>
 </ol>
-<ul>
-<li>结果是在超过7个gene的blocks里找到5个blocks存在这种情况。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Alignment 288: ctg4124-ctg7158：Ane23910-Ane23911之间有Ane41918的pseudogene(ctg4124 6381349 6381618)；Ane23909-Ane23910之间有Ane41900的pseudogene（重排）；</span><br><span class="line">Alignment 102： ctg11329-ctg6741：Ane66914-Ane66915之间有Ane38759的pseudogene；Ane66920-Ane66921之间有Ane38765的pseudogene(ctg11329        2818519 2817964)；</span><br><span class="line">Alignment 139: ctg1252-ctg4677：Ane27375-Ane27376之间有Ane07597的pseudogene；</span><br><span class="line">Alignment 422: ctg7700-ctg9700：Ane46157-Ane461158之间有Ane57929的pseudogene（重排）(ctg7700 2107090 2106980)；</span><br><span class="line">Alignment 396: ctg6709-ctg8004：Ane38599-Ane38600之间有Ane47496的pseudogene（重排）(ctg6709 1117953 1117655)；</span><br></pre></td></tr></table></figure>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://altushost-swe.dl.sourceforge.net/project/kakscalculator2/KaKs_Calculator2.0.tar.gz</span><br><span class="line">tar -zxf KaKs_Calculator2.0.tar.gz</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/bin/Linux/KaKs_Calculator</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/src/AXTConvertor</span><br></pre></td></tr></table></figure>
 
-<h2 id="6-3-非批量计算Ks"><a href="#6-3-非批量计算Ks" class="headerlink" title="6.3. 非批量计算Ks"></a>6.3. 非批量计算Ks</h2><p>如果只有少量样本对（关注的假基因和对应真基因）需要计算Ks，可以手动进行。比如上面的共线性区块分析中找到的5个blocks中的少量样本对。</p>
-<h3 id="6-3-1-提取序列"><a href="#6-3-1-提取序列" class="headerlink" title="6.3.1. 提取序列"></a>6.3.1. 提取序列</h3><p>通过PseudogenePipeline运行结果的比对信息提取假基因和对应的真基因比对上的ORF区域，并提取3的倍数的碱基，以计算Ks。</p>
-<ol>
-<li>拿<code>_results/sample.4col.true.fa.RMfilt.hiConf</code>中的一条pseudogene序列的ID举例说明位置的含义</li>
+<p>然后把.&#x2F;KaKs_Calculator2.0&#x2F;bin&#x2F;Linux&#x2F;和.&#x2F;KaKs_Calculator2.0&#x2F;src&#x2F;添加到环境变量即可使用KaKs_Calculator和AXTConvertor命令。</p>
+<ol start="3">
+<li>用ParaAT.pl获取共线性基因对比对序列<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cat sample.collinearity |grep -v &quot;^#&quot;|cut -f2,3 &gt;sample.homolog #提取blocks的同源gene对</span><br><span class="line">echo &quot;24&quot; &gt;proc #指定ParaAT.pl使用线程</span><br><span class="line">ParaAT.pl -g -t -h sample.homolog -n sample.cds.fa -a sample.pep.fa -m mafft -p proc -f axt -o sample.paraat 2&gt; paraat.log &amp; #用ParaAT.pl调用mafft做每对共线性基因的蛋白比对和蛋白转cds比对，输出axt格式。-g移除比对有gap的密码子，-t移除mismatched codons；；-o指定生成目录；</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">加上-k参数可以在获得axt文件后自动调用KaKs_Calculator计算kaks值，使用MA模型，比YN模型慢很多，推荐手动用KaKs_Calculator的YN模型，生成sample.axt_yn.kaks文件。</span></span><br></pre></td></tr></table></figure></li>
 </ol>
 <ul>
-<li><code>gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0    ctg947    625785    625096</code>表示在染色体<code>ctg947</code>的<code>625093-625785</code>位置有一个假基因pseudogene。</li>
-<li>对应的真基因是<code>gene38168</code>；<code>336-566</code>和<code>693-4</code>分别是指真基因序列和假基因序列比对上的区域；比对的信息是从tblastn的比对结果中获取的。</li>
-<li>真基因<code>gene38168</code>的<strong>氨基酸</strong>序列的<code>336-566</code>位置。</li>
-<li>假基因的<code>693-4</code>位置是指在染色体<code>ctg947</code>的<code>625093-625785</code>位置的假基因核酸序列上的第4到693个碱基，由于起始位置比终止位置大，所以是4-693位置的反向互补序列。</li>
+<li>建议加上-g和-t，免得后面计算Ks时报错Error. The size of two sequences in ‘ctg00816-ctg08844’ is not equal。</li>
+<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
 </ul>
-<ol start="2">
-<li>根据pseudogene的序列位置和对应的真基因的位置信息，提取两者比对上的cds序列</li>
+<ol start="4">
+<li>用KaKs_Calculator手动计算共线性基因对的KaKs和4dtv值<br>ParaAT.pl的-k参数只能指定KaKs_Calculator的MA模型计算kaks值，如果需要指定其他的模型，则可以手动运行计算。</li>
 </ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat</span><br><span class="line">for i in `ls *axt`;do KaKs_Calculator -i $i -o $&#123;i&#125;_yn.kaks -m YN;done #用YN模型计算每个gene对的KaKs，生成四列数据，gene对，Ka，Ks，Ka/Ks</span><br><span class="line">for i in `ls |grep &quot;_yn.kaks&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&quot;\t&quot;$4&quot;\t&quot;$5&#125;&#x27; $i &gt;&gt;../all.kaks;done #合并kaks到all.kaks文件</span><br><span class="line">- 计算4dtv</span><br><span class="line">for i in `ls *axt`;do axt2one-line.py $i $&#123;i&#125;.one-line;done #多行axt文件转换成单行</span><br><span class="line">ls |grep &quot;axt.one-line&quot;|while read id;do calculate_4DTV_correction.pl $id &gt; $&#123;id%%one-line&#125;4dtv;done #计算4dtv值，生成两列数据，gene对，4dtv</span><br><span class="line">for i in `ls |grep &quot;4dtv&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&#125;&#x27; $i &gt;&gt;../all.4dtv;done #合并4dtv值到all.4dtv</span><br><span class="line">cd ..</span><br><span class="line">join all.kaks all.4dtv |sed &quot;s/ /\t/g&quot; |awk &#x27;$3 != &quot;NA&quot; &#123;print $0&#125;&#x27; |sed &#x27;1i\genepair\tKa\tKs\tKa/Ks\t4dtv_corrected&#x27; &gt;all.results #以gene对为基准，join合并kaks和4dtv值到一个文件，然后过滤Ks值为NA的无效数据，添加标题行。</span><br><span class="line"></span><br><span class="line">rm all.kaks* all.4dtv* #删除中间文件</span><br></pre></td></tr></table></figure>
+
+<h2 id="3-6-ggplot2画密度分布图和峰值"><a href="#3-6-ggplot2画密度分布图和峰值" class="headerlink" title="3.6. ggplot2画密度分布图和峰值"></a>3.6. ggplot2画密度分布图和峰值</h2><h3 id="3-6-1-ggplot2画密度分布图"><a href="#3-6-1-ggplot2画密度分布图" class="headerlink" title="3.6.1. ggplot2画密度分布图"></a>3.6.1. ggplot2画密度分布图</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ggplot2<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>ggpmisc<span class="punctuation">)</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;all.results&quot;</span><span class="punctuation">,</span>header<span class="operator">=</span><span class="built_in">T</span><span class="punctuation">)</span></span><br><span class="line">p <span class="operator">&lt;-</span> ggplot<span class="punctuation">(</span>data<span class="punctuation">,</span> aes<span class="punctuation">(</span>Ks<span class="punctuation">)</span><span class="punctuation">)</span> <span class="operator">+</span> geom_density<span class="punctuation">(</span>size<span class="operator">=</span><span class="number">1</span><span class="punctuation">,</span>color<span class="operator">=</span><span class="string">&quot;black&quot;</span><span class="punctuation">)</span><span class="operator">+</span>xlab<span class="punctuation">(</span><span class="string">&quot;Synonymous substitution rate(Ks)&quot;</span><span class="punctuation">)</span><span class="operator">+</span>ylab<span class="punctuation">(</span><span class="string">&quot;Percent of Total Paralogs&quot;</span><span class="punctuation">)</span><span class="operator">+</span>theme_classic<span class="punctuation">(</span><span class="punctuation">)</span><span class="operator">+</span>scale_y_continuous<span class="punctuation">(</span>breaks<span class="operator">=</span>seq<span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span> <span class="number">2.5</span><span class="punctuation">,</span> <span class="number">0.2</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span>xlim<span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span><span class="number">3</span><span class="punctuation">)</span></span><br><span class="line">ggsave<span class="punctuation">(</span>file<span class="operator">=</span><span class="string">&quot;Ks.pdf&quot;</span><span class="punctuation">,</span>plot<span class="operator">=</span>p<span class="punctuation">,</span>width<span class="operator">=</span><span class="number">10</span><span class="punctuation">,</span>height<span class="operator">=</span><span class="number">5</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<h3 id="3-6-2-ggplot2峰值标定"><a href="#3-6-2-ggplot2峰值标定" class="headerlink" title="3.6.2. ggplot2峰值标定"></a>3.6.2. ggplot2峰值标定</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">pb <span class="operator">&lt;-</span> ggplot_build<span class="punctuation">(</span>p<span class="punctuation">)</span></span><br><span class="line">pic <span class="operator">&lt;-</span> p <span class="operator">+</span> stat_peaks<span class="punctuation">(</span>data <span class="operator">=</span> pb<span class="punctuation">[[</span><span class="string">&#x27;data&#x27;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">[[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">,</span> aes<span class="punctuation">(</span>x<span class="operator">=</span>x<span class="punctuation">,</span> y<span class="operator">=</span>density<span class="punctuation">)</span><span class="punctuation">,</span> geom<span class="operator">=</span> <span class="string">&#x27;text&#x27;</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> <span class="punctuation">,</span>vjust<span class="operator">=</span><span class="operator">-</span><span class="number">0.5</span><span class="punctuation">)</span></span><br><span class="line">ggsave<span class="punctuation">(</span>file<span class="operator">=</span><span class="string">&quot;Ks_peaks.pdf&quot;</span><span class="punctuation">,</span>plot<span class="operator">=</span>pic<span class="punctuation">,</span>width<span class="operator">=</span><span class="number">10</span><span class="punctuation">,</span>height<span class="operator">=</span><span class="number">5</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
+
+<p>在密度分布图里得到红色标记的峰值。</p>
+<h3 id="3-6-3-多个密度图"><a href="#3-6-3-多个密度图" class="headerlink" title="3.6.3. 多个密度图"></a>3.6.3. 多个密度图</h3><p>同时展示多组Ks数据分布，可以把数据合并，添加一列作为分类标签，通过colour颜色参数指定这个分类标签列，则可以实现一张图上展示多个密度图。</p>
+<p><code>ggplot(data)+geom_density(aes(x=V3,colour=V5),adjust=2)+xlim(0,0.5)+theme_classic()</code> #指定第三列V3为数据，第五列V5为分类依据并赋予不同颜色。</p>
+<h3 id="3-6-4-通过密度分布图判断WGD"><a href="#3-6-4-通过密度分布图判断WGD" class="headerlink" title="3.6.4. 通过密度分布图判断WGD"></a>3.6.4. 通过密度分布图判断WGD</h3><p>一般认为，Ks密度分布图如果有明显的峰，一个峰代表对应一次WGD事件。<br>通过峰的数量和对应的Ks大小可以判断WGD事件的次数和发生时间。</p>
+<h2 id="3-7-WGD发生的时间"><a href="#3-7-WGD发生的时间" class="headerlink" title="3.7. WGD发生的时间"></a>3.7. WGD发生的时间</h2><p>通过Ks的密度分布图鉴定得到WGD事件发生的证据之后，有多种方法可以估算WGD发生的时间。</p>
+<h3 id="3-7-1-通过Ks-x3D-2μT计算时间"><a href="#3-7-1-通过Ks-x3D-2μT计算时间" class="headerlink" title="3.7.1. 通过Ks&#x3D;2μT计算时间"></a>3.7.1. 通过Ks&#x3D;2μT计算时间</h3><p>依据公式Ks&#x3D;2μT来计算时间T，依赖准确的进化速率μ，μ通常引用近缘类群的已有权威研究。</p>
+<p>进化速率μ：</p>
 <ul>
-<li>先从<code>_results/sample.4col.true.fa.RMfilt.hiConf</code>中提取出<code>gene38168</code>对应的假基因pseudogene；</li>
-<li>根据假基因的比对位置<code>693-4</code>提取染色体<code>ctg947</code>的<code>625093-625785</code>位置的<code>4-693</code>的核酸，由于起始位置比终止位置大，所以是<code>4-693</code>位置的反向互补序列。</li>
-<li>再根据真基因的ID<code>gene38168</code>提取真基因的cds序列；</li>
-<li>根据真基因的蛋白比对位<code>336-566</code>，通过计算起始位置$$336*3-2&#x3D;1008$$，和终止位置$$566*3&#x3D;1698$$，找到cds对应的<code>1008-1698</code>位置提取出来。</li>
+<li>蕨类植物的同义突变率：4.79e-9 subst.&#x2F;syn. site&#x2F;year :the first estimate of fern nuclear genome evolutionary rates with polypodiaceous nuclear genomes (Barker 2009)</li>
+<li>水稻：2.5e-8 subst.&#x2F;syn. site&#x2F;year</li>
 </ul>
-<ol start="3">
-<li>比对和删改</li>
+<h3 id="3-7-2-通过进化树估算时间"><a href="#3-7-2-通过进化树估算时间" class="headerlink" title="3.7.2. 通过进化树估算时间"></a>3.7.2. 通过进化树估算时间</h3><ul>
+<li>用代表WGD的Ks值附近的同线性基因对制作两套样品的单倍型数据，然后选取一到多个近缘种的蛋白序列与其中一套单倍型做直系同源基因分析，用找到的直系同源单拷贝基因建树（把两套单倍型数据当作两个物种来建树），通过化石或者二次标定的方法用paml的mcmctree模块计算两套单倍型的分化时间，即为WGD发生的时间。</li>
+<li>如果物种分化时间的尺度太大而不能准确估算WGD时间，可以先用大尺度的系统发育树做近缘种和目标物种的分化时间的估算，然后在做WGD时间估算时只用一个近缘种加上近缘种与目标物种的分化时间标定来获得更准确的WGD时间估计结果。</li>
+</ul>
+<h4 id="3-7-2-1-获取sample发生WGD的两套基因"><a href="#3-7-2-1-获取sample发生WGD的两套基因" class="headerlink" title="3.7.2.1. 获取sample发生WGD的两套基因"></a>3.7.2.1. 获取sample发生WGD的两套基因</h4><ol>
+<li>前面分析中计算Ks的分布峰值假设在<strong>0.05</strong>，取Ks值在0.05正负25%的范围，即[0.0375-0.0625]之间的基因对。<br><code>cat all.results |awk &#39;$3&gt;0.0375 &amp;&amp; $3&gt;0.0625 &#123;print $1&#125; |sed &quot;s/-/\t/g&quot; &gt;wgd.homologs</code></li>
+<li>基因对中若有一个基因对多个基因的情况，则选取Ks最接近0.05的那对。</li>
+<li>获取两套基因list。<br><code>cut -f1 wgd.homologs &gt;wgd_H1.homologs</code>;<br><code>cut -f2 wgd.homologs &gt;wgd_H2.homologs</code></li>
+</ol>
+<h4 id="3-7-2-2-选近缘种和分化时间"><a href="#3-7-2-2-选近缘种和分化时间" class="headerlink" title="3.7.2.2. 选近缘种和分化时间"></a>3.7.2.2. 选近缘种和分化时间</h4><ol>
+<li>参考近年发表的权威组学系统发育文章选取近缘种和物种分化时间数据。</li>
+<li>这里假设选取了两个近缘物种sampleA和sampleB。sample发生WGD形成的两套单倍型H1和H2，目标物种sample与近缘种sampleA的分化时间已知为88Ma。</li>
+<li>确定物种树拓扑结构：<code>(((sampleH1,sampleH2),sampleA)&#39;&lt;0.88&gt;0.88),sampleB;</code>，以sampleB为外类群。</li>
+</ol>
+<h4 id="3-7-2-3-orthofinder找直系同源基因"><a href="#3-7-2-3-orthofinder找直系同源基因" class="headerlink" title="3.7.2.3. orthofinder找直系同源基因"></a>3.7.2.3. orthofinder找直系同源基因</h4><ol>
+<li>根据基因对提取蛋白序列，任意用一个（这里用wgd_H1.homologs)提取蛋白序列：<code>seqkit grep -n -f wgd_H1.homologs sample.pep &gt; wgd_H1.pep</code></li>
+<li>与其他两个近缘种的蛋白序列一起，用orthofinder找直系同源基因。</li>
 </ol>
 <ul>
-<li>把两者序列保存在一个fasta文件，并用mafft&#x2F;muscle等工具做align；</li>
-<li>以真基因序列为参考，根据align结果对提取的假基因序列进行删除和修改。比如删除比对到intron的部分；若是假基因中包含indel，则在indel处找到上一个密码子的第三位，在这个位置删除对应密码子；最终使得两条序列都包含3的倍数的对应密码子的orf序列。</li>
-<li>删改好后再做一次align，每个假基因和对应真基因保存成一个文件；</li>
+<li>orthofinder使用方法：把所有物种的pep蛋白序列（两个近缘物种加上wgd_H1.pep）放进一个文件夹(directory)，注意蛋白序列中不能有.&#x2F;*等符号（常用来代表终止密码子）。</li>
+<li>运行<code>orthofinder -f directory -t 24</code> # -t指定线程</li>
+<li>结果文件的Orthogroups目录下，Orthogroups.txt包含所有直系同源基因，Orthogroups_SingleCopyOrthologues.txt包含单拷贝的直系同源基因。</li>
+<li>根据Orthogroups_SingleCopyOrthologues.txt把Orthogroups.txt里基因信息提取出来,<code>grep -f Orthogroups_SingleCopyOrthologues.txt Orthogroups.tsv &gt;OG.txt</code>。OG.txt文件内容是singlecopyorthologues列表，第一列orthogroupID，后面3列每套蛋白序列ID，共4列，这个文件的每行数据为一个homologs对应的每套数据的蛋白ID。</li>
+<li>合并wgd.homologs和OG.txt文件为OG.list：<code>join -1 1 -2 2 wgd.homologs OG.txt &gt; OG.list</code>。OG.list文件是在OG.txt基础上增加了wgd_H2.homologs的一套蛋白ID数据，共有四套蛋白ID数据。</li>
 </ul>
-<h3 id="6-3-2-计算Ks"><a href="#6-3-2-计算Ks" class="headerlink" title="6.3.2. 计算Ks"></a>6.3.2. 计算Ks</h3><ol>
-<li>改格式</li>
+<h4 id="3-7-2-4-建单基因树"><a href="#3-7-2-4-建单基因树" class="headerlink" title="3.7.2.4. 建单基因树"></a>3.7.2.4. 建单基因树</h4><ol>
+<li>建树脚本<br>用下面的脚本singlegenetree.sh，提取每个homologs序列，并通过raxml-ng（raxmlHPC建氨基酸树只有bootstrap结果，没有bestTree）为每个homologs基因比对（mafft）和建树。<br>OG.list文件包含第一列ogID和后4列4套样本ID。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">singlegenetree.sh</span></span><br><span class="line">cat ./OG.list | while read line</span><br><span class="line">do</span><br><span class="line">        sample_id=$(echo $line |awk &#x27;&#123;print $1&#125;&#x27;)</span><br><span class="line">        sample_a=$(echo $line |awk &#x27;&#123;print $2&#125;&#x27;)</span><br><span class="line">        sample_b=$(echo $line |awk &#x27;&#123;print $3&#125;&#x27;)</span><br><span class="line">        sample_c=$(echo $line |awk &#x27;&#123;print $4&#125;&#x27;)        </span><br><span class="line">        sample_d=$(echo $line |awk &#x27;&#123;print $5&#125;&#x27;)</span><br><span class="line">        mkdir ./singlegenetree/$&#123;sample_id&#125;</span><br><span class="line">        seqkit grep -n $&#123;sample_a&#125; sample.pep &gt;./singlegenetree/$&#123;sample_id&#125;/H1_$&#123;sample_a&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_b&#125; sample.pep &gt;./singlegenetree/$&#123;sample_id&#125;/H2_$&#123;sample_b&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_c&#125; sampleA.pep &gt;./singlegenetree/$&#123;sample_id&#125;/$&#123;sample_c&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_d&#125; sampleB.pep &gt;./singlegenetree/$&#123;sample_id&#125;/$&#123;sample_d&#125;.pep</span><br><span class="line">        cd ./singlegenetree/$&#123;sample_id&#125;/</span><br><span class="line">        cat H1_$&#123;sample_a&#125;.pep H2_$&#123;sample_b&#125;.pep $&#123;sample_c&#125;.pep $&#123;sample_d&#125;.pep &gt; $&#123;sample_id&#125;.pep</span><br><span class="line">        mafft $&#123;sample_id&#125;.pep &gt;$&#123;sample_id&#125;.mafft.pep</span><br><span class="line">        raxml-ng --all --msa $&#123;sample_id&#125;.mafft.pep --data-type AA --threads 8 --bs-trees 100 --model LG+G8+F --tree pars&#123;10&#125; --outgroup $&#123;sample_d&#125; --redo</span><br><span class="line">        sed -E &quot;s/:[0-9]\.[0-9]*//g&quot; $&#123;sample_id&#125;.mafft.pep.raxml.bestTree  &gt;besttree.$&#123;sample_id&#125; #删除不必要的枝长等信息</span><br><span class="line">        cd ../../</span><br><span class="line">done</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>筛选homologs<br>判断生成的每个OG的树文件besttree.${sample_id}，只保留符合物种树拓扑结构<code>(((sampleH1,sampleH2),sampleA)),sampleB;</code>的基因，保存成OG.filtered。</li>
+</ol>
+<h4 id="3-7-2-5-paml估计分化时间"><a href="#3-7-2-5-paml估计分化时间" class="headerlink" title="3.7.2.5. paml估计分化时间"></a>3.7.2.5. paml估计分化时间</h4><p>此节参考博客<a href="https://yanzhongsino.github.io/2021/03/25/bioinfo_caculate.divergence.time/">估算系统树分歧时间</a>的paml部分。</p>
+<ol>
+<li>在OG.filtered最后一列添加每个OG的序列长度信息，保存成OG.filtered.list。</li>
+<li>准备输入文件sample.phy<br>上一步脚本提取的每个homologs的比对后的四套数据蛋白序列${sample_id}.mafft.pep，用脚本fa2phylip.sh换成phylip格式（用python的SeqIO库换的格式mcmctree识别不了）。<br>并合并提取的所有homologs序列。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">fa2phylip.sh</span></span><br><span class="line">cat ./OG.filtered.list | while read line</span><br><span class="line">do</span><br><span class="line">        sample_id=$(echo $line |awk &#x27;&#123;print $1&#125;&#x27;) #获取OG.ID</span><br><span class="line">        sample_a=$(echo $line |awk &#x27;&#123;print $6&#125;&#x27;) #获取序列长度</span><br><span class="line">        sed &quot;s/_.*//g&quot; ../singlegenetree/$&#123;sample_id&#125;/$&#123;sample_id&#125;.mafft.pep|seqkit seq -w 0|sed -E &quot;:a;N;s/\n/ /g;ta&quot; |sed &quot;s/ &gt;/\n/g&quot; |sed &quot;s/&gt;//g&quot;|sed &quot;s/ /  /g&quot;|sed &quot;1i\5  $&#123;sample_a&#125;&quot; |sed &#x27;1i\ &#x27; &gt; ./phy/$&#123;sample_id&#125;.phy #把上一步获取的$&#123;sample_id&#125;.mafft.pep改为phylip格式，并在首行添加空格行（为了合并后每个OG用空行隔开）</span><br><span class="line"></span><br><span class="line">done</span><br><span class="line">cat ./phy/*phy &gt;sample.phy #合并所有phy文件为一个sample.phy文件，假设共有261个子phy文件被合并</span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>准备输入文件sample.tre</li>
 </ol>
 <ul>
-<li><code>AXTConvertor gene_pseudo.fa gene_pseudo.axt</code></li>
-<li>KaKs_Calculator的改格式工具，改比对后的fas格式（gene_pseudo.fa）为axt（gene_pseudo.axt）。</li>
-<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
+<li>sample.tre包含两行，第一行表述树中有5个样本，共计1个树，两个数值之间用空格分割；<br>第二行则是Newick格式树信息。</li>
+<li>其中第二行Newick格式树包含有校准点信息，校准点信息一般指95%HPD（Highest Posterior Density）对应的置信区间，这里用的校准点时间只有一个点，所以写成’&lt;0.88&gt;0.88’形式；校准点单位是100MYA（软件说明文档中使用该单位，也推荐使用该单位，若使用其它单位，后续配置文件中的相关参数也需要对应修改）。</li>
+<li>此外，Newick格式的树尾部一定要有分号，没有的话程序可能不能正常运行。</li>
+<li>sample.tre内容为：</li>
 </ul>
-<ol start="2">
-<li>计算Ks</li>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">4 1</span><br><span class="line">(((sampleH1,sampleH2),sampleA)&#x27;&lt;0.88&gt;0.88),sampleB;</span><br></pre></td></tr></table></figure>
+
+<ol start="4">
+<li>准备配置文件mcmctree3.ctl<br>ndata &#x3D; 261表示有261个数据，与sample.phy包含的子phy数量对应；seqtype &#x3D; 2表示这里用的氨基酸序列；model &#x3D; 0暂时选用0，后面要改；</li>
+</ol>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">          seed = -1</span><br><span class="line">       seqfile = sample.phy</span><br><span class="line">      treefile = sample.tre</span><br><span class="line">       outfile = mcmc.out</span><br><span class="line">      mcmcfile = mcmc.txt</span><br><span class="line"></span><br><span class="line">       seqtype = 2    *0: DNA data; 1: codon data; 2: AAs data</span><br><span class="line">        ndata = 261</span><br><span class="line">       usedata = 3    * 0: no data; 1:seq like; 2:normal approximation</span><br><span class="line">         clock = 3    * 1: global clock; 2: independent rates; 3: correlated rates</span><br><span class="line">*      TipDate = 1 100</span><br><span class="line">        RootAge = &#x27;&lt;10&#x27;  * constraint on root age, used if no fossil for root.</span><br><span class="line"></span><br><span class="line">         model = 0    * 0:JC69, 1:K80, 2:F81, 3:F84, 4:HKY85</span><br><span class="line">         alpha = 0.5   * alpha for gamma rates at sites</span><br><span class="line">         ncatG = 4    * No. categories in discrete gamma</span><br><span class="line"></span><br><span class="line">     cleandata = 0    * remove sites with ambiguity data (1:yes, 0:no)?</span><br><span class="line"></span><br><span class="line">       BDparas = 1 1 0.1   * birth, death, sampling</span><br><span class="line">   kappa_gamma = 6 2      * gamma prior for kappa</span><br><span class="line">   alpha_gamma = 1 1      * gamma prior for alpha</span><br><span class="line"></span><br><span class="line">   rgene_gamma = 2 20 1   * gamma prior for rate for genes</span><br><span class="line">  sigma2_gamma = 1 10 1    * gamma prior for sigma^2     (for clock=2 or 3)</span><br><span class="line"></span><br><span class="line">      finetune = 1: .1 .1 .1 .1 .1 .1  * times, rates, mixing, paras, RateParas</span><br><span class="line"></span><br><span class="line">         print = 1</span><br><span class="line">        burnin = 1000000</span><br><span class="line">      sampfreq = 10</span><br><span class="line">       nsample = 500000</span><br><span class="line"></span><br><span class="line">*** Note: Make your window wider (100 columns) when running this program.</span><br></pre></td></tr></table></figure>
+
+<ol start="5">
+<li>运行<code>mcmctree mcmctree3.ctl</code></li>
 </ol>
 <ul>
-<li><code>KaKs_Calculator -i gene_pseudo.axt -o gene_pseudo.kaks -m PN</code></li>
-<li>计算KaKs，用PN模型；结果就在gene_pseudo.kaks文件中。</li>
+<li>此步骤的目的是为了获取in.BV文件；</li>
+<li>用命令<code>mcmctree mcmctree3.ctl</code>运行起来后，ctrl+C多次中断运行（共261个序列，会依次调用261次codeml，中断后会自动进入下一次调用，目的是完成261次调用生成261个序列的临时文件）；由于单个序列短，很快会完成分析，也可以不ctrl+C中断，等待完成，只要生成261套tmp*文件即可。</li>
+<li>删除rst2文件和mcmc.out,mcmc.txt,out.BV文件；把paml软件下的wag.dat文件复制到当前目录下<code>cp ~/software/anaconda3/dat/wag.dat ./</code>；</li>
+<li>用命令<code>sed -i -e &quot;s/model = 0/model =2\naaRatefile = wag.dat/&quot; -e &quot;s/method = 0/method = 1/&quot; tmp*.ctl</code>把所有tmp*.ctl文件的model修改成model &#x3D; 2，method修改成method &#x3D; 1,并添加一行aaRatefile &#x3D; wag.dat；</li>
+<li>用命令<code>for i in $(ls tmp*.ctl);do codeml $&#123;i&#125; ; mv rst2 $&#123;i&#125;.rst2; done</code>调用codeml进行261次分析（每次codeml都生成rst2文件，所以每次生成后把rst2改名，否则rst2被覆盖）；</li>
+<li>然后<code>cat *rst2 &gt;in.BV</code>合并所有rst2文件到in.BV文件；</li>
 </ul>
-<ol start="3">
-<li>合并所有计算的ks结果</li>
+<ol start="6">
+<li>运行<code>mcmctree mcmctree2.ctl</code></li>
 </ol>
-<table>
-<thead>
-<tr>
-<th>gene-pseudo</th>
-<th>Ka</th>
-<th>Ks</th>
-<th>Ka&#x2F;Ks</th>
-</tr>
-</thead>
-<tbody><tr>
-<td>Ane07597-Ane07597.pseudo</td>
-<td>0.166453</td>
-<td>0.830702</td>
-<td>0.200376</td>
-</tr>
-<tr>
-<td>Ane38759-Ane38759.pseudo</td>
-<td>0.0068327</td>
-<td>0.0579878</td>
-<td>0.11783</td>
-</tr>
-<tr>
-<td>Ane38765-Ane38765.pseudo</td>
-<td>0.0181923</td>
-<td>0.0594878</td>
-<td>0.305815</td>
-</tr>
-<tr>
-<td>Ane41900-Ane41900.pseudo</td>
-<td>0.356588</td>
-<td>1.23697</td>
-<td>0.288276</td>
-</tr>
-<tr>
-<td>Ane41918-Ane41918.pseudo</td>
-<td>0.0304071</td>
-<td>0.033778</td>
-<td>0.900203</td>
-</tr>
-<tr>
-<td>Ane47496-Ane47496.pseudo</td>
-<td>0.025231</td>
-<td>0.210919</td>
-<td>0.119624</td>
-</tr>
-<tr>
-<td>Ane57929-Ane57929.pseudo</td>
-<td>0.0380011</td>
-<td>0.0703679</td>
-<td>0.540034</td>
-</tr>
-</tbody></table>
 <ul>
-<li>从结果中看出，Ane38759，Ane38765，Ane579290三个基因和它们的假基因之间的Ks接近WGD分析中计算的WGD所在的Ks&#x3D;0.06；可以作为共线性blocks内部假基因的证据。</li>
+<li>新建目录并复制四个文件sample.phy,sample.tre,mcmctree3.ctl,in.BV到新目录<code>mkdir mcmctree2 &amp;&amp; cd mcmctree2 &amp;&amp; cp ../Ane.* ./ &amp;&amp; cp ../mcmctree3.ctl ./ &amp;&amp; cp ../in.BV ./</code>;</li>
+<li>把mcmctree3.ctl文件内的usedata &#x3D; 3改为usedata &#x3D; 2，并重命名成mcmctree2.ctl；</li>
+<li>然后运行<code>mcmctree mcmctree2.ctl</code>；</li>
+<li>等待两三天获取结果，FigTree.tre文件包含了所有节点的95%HPD的时间，是nexus格式。</li>
+<li>根据FigTree.tre结果：<code>((sampleA: 0.408365, (sampleH1: 0.107333, sampleH2: 0.107333) [&amp;95%=&#123;0.0749432, 0.126365&#125;]: 0.301032) [&amp;95%=&#123;0.285365, 0.478163&#125;]: 0.362000, sampleB: 0.770366) [&amp;95%=&#123;0.53864, 0.90012&#125;];</code>的sampleH1和sampleH2分化的时间，可以看出WGD发生的时间在**10.7Ma[7.5-12.6]**之间。</li>
 </ul>
-<h1 id="7-references"><a href="#7-references" class="headerlink" title="7. references"></a>7. references</h1><ol>
-<li>wiki:pseudogene:<a href="https://en.wikipedia.org/wiki/Pseudogene">https://en.wikipedia.org/wiki/Pseudogene</a></li>
-<li>PseudogenePipeline github：<a href="https://github.com/ShiuLab/PseudogenePipeline">https://github.com/ShiuLab/PseudogenePipeline</a></li>
-<li>2022年08月在GBE上发表的荷叶铁线蕨基因组的文章：<a href="https://academic.oup.com/gbe/article/14/8/evac127/6659224">https://academic.oup.com/gbe/article/14/8/evac127/6659224</a></li>
+<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
+<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326336/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326336/</a></li>
+<li><a href="https://github.com/wyp1125/MCScanX">https://github.com/wyp1125/MCScanX</a></li>
+<li><a href="https://its201.com/article/sinat_41621566/113359074">https://its201.com/article/sinat_41621566/113359074</a></li>
 </ol>
 <hr>
 <ul>
@@ -7372,13 +7272,20 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
         <category>bioinfo</category>
-        <category>pseudogene</category>
+        <category>synteny</category>
       </categories>
       <tags>
+        <tag>biosoft</tag>
+        <tag>ggplot2</tag>
+        <tag>WGD</tag>
         <tag>Ks</tag>
-        <tag>pseudogene</tag>
-        <tag>PseudogenePipeline</tag>
-        <tag>假基因</tag>
+        <tag>ParaAT</tag>
+        <tag>KaKs_Calculator</tag>
+        <tag>divergence time</tag>
+        <tag>paml</tag>
+        <tag>synteny</tag>
+        <tag>colinearity</tag>
+        <tag>MCScanX</tag>
       </tags>
   </entry>
   <entry>
@@ -7496,247 +7403,354 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>基因组WGD事件的鉴定和时间估算 —— MCScanX,KaKa_Calculator</title>
-    <url>/2021/11/27/bioinfo_synteny_MCScanX/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=29808885&auto=1&height=32"></iframe></div>
+    <title>基因组间的同线性分析 —— MCscan</title>
+    <url>/2021/11/05/bioinfo_synteny_MCscan/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5250805&auto=1&height=32"></iframe></div>
 
-<h1 id="1-MCScanX介绍"><a href="#1-MCScanX介绍" class="headerlink" title="1. MCScanX介绍"></a>1. MCScanX介绍</h1><p>MCScanX调整<a href="https://yanzhongsino.github.io/2021/11/05/bioinfo_MCscan/">MCScan算法</a>进行检测基因组间或基因组内的同线性，并附加了14个下游分析和可视化的脚本。</p>
-<h1 id="2-MCScanX同线性分析"><a href="#2-MCScanX同线性分析" class="headerlink" title="2. MCScanX同线性分析"></a>2. MCScanX同线性分析</h1><h2 id="2-1-种内同线性分析MCScanX"><a href="#2-1-种内同线性分析MCScanX" class="headerlink" title="2.1. 种内同线性分析MCScanX"></a>2.1. 种内同线性分析MCScanX</h2><h3 id="2-1-1-准备输入文件"><a href="#2-1-1-准备输入文件" class="headerlink" title="2.1.1. 准备输入文件"></a>2.1.1. 准备输入文件</h3><p>MCScanX做同线性分析需要两个输入文件sample.gff(四列数据)和sample.blast。</p>
-<p>如果是多个物种，则把多个物种的gff3文件和pep.fa文件合并后再准备sample.gff和sample.blast输入文件。</p>
+<h1 id="1-MCscan背景"><a href="#1-MCscan背景" class="headerlink" title="1. MCscan背景"></a>1. MCscan背景</h1><h2 id="1-1-jcvi介绍"><a href="#1-1-jcvi介绍" class="headerlink" title="1.1. jcvi介绍"></a>1.1. jcvi介绍</h2><p><a href="https://github.com/tanghaibao/jcvi">jcvi</a>是美国佐治亚大学唐海宝开发的一个生物信息学python库。主要有几个常见pipeline用到了jcvi库，一个是做基因组scaffoldding的<a href="https://github.com/tanghaibao/jcvi/wiki/ALLMAPS">ALLMAPS</a>，另一个是做基因组同线性分析的<a href="https://github.com/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan</a>，以及提取植物表型数据的<a href="https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS">GRABSEEDS</a>。</p>
+<h2 id="1-2-MCscan介绍"><a href="#1-2-MCscan介绍" class="headerlink" title="1.2. MCscan介绍"></a>1.2. MCscan介绍</h2><p>用MCscan pipeline做两个基因组间的同线性分析，包括成对同线性(pairwise synteny)分析，宏观同线性(macrosynteny)分析，微观同线性(microsynteny)分析和结果的可视化(visualization)。</p>
+<p>成对同线性分析主要是制作同线性点图(dotplot),同线性深度直方图；宏观同线性分析主要是制作同线性染色体图。其中同线性深度直方图，同线性染色体图需要用到同线性点图的结果文件作为输入。</p>
+<p>微观同线性分析等后面用到再补坑。</p>
+<h1 id="2-MCscan使用"><a href="#2-MCscan使用" class="headerlink" title="2. MCscan使用"></a>2. MCscan使用</h1><h2 id="2-1-安装jcvi"><a href="#2-1-安装jcvi" class="headerlink" title="2.1. 安装jcvi"></a>2.1. 安装jcvi</h2><h3 id="2-1-1-依赖"><a href="#2-1-1-依赖" class="headerlink" title="2.1.1. 依赖"></a>2.1.1. 依赖</h3><p>jcvi依赖包括biopython,numpy,matplotlib。</p>
+<p>MCscan依赖包括LASTAL。</p>
+<p>LASTAL可以用<code>conda install -c bioconda last</code>安装，但我用conda安装的lastal和lastdb不能多线程运行，所以在运行<code>python -m jcvi.compara.catalog ortholog --cpu=1</code>时用参数<code>--cpu=1</code>来限制单线程运行。</p>
+<h3 id="2-1-2-安装jcvi"><a href="#2-1-2-安装jcvi" class="headerlink" title="2.1.2. 安装jcvi"></a>2.1.2. 安装jcvi</h3><p>任意选一种安装方式</p>
 <ol>
-<li>sample.gff</li>
+<li><code>pip install jcvi</code></li>
+<li><code>pip install git+git://github.com/tanghaibao/jcvi.git</code></li>
+<li><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">cd jcvi</span><br><span class="line">git clone git://github.com/tanghaibao/jcvi.git</span><br><span class="line">pip install -e .</span><br></pre></td></tr></table></figure></li>
 </ol>
-<ul>
-<li>与gff3文件格式不一样，这里的sample.gff包含四列数据，第一列染色体ID，第二列基因ID，第三和第四列分别是起始和终止位置。</li>
-<li><code>cat sample.gene.gff3 |awk &#39;&#123;if($3==&quot;gene&quot;)&#123;print $1,$9,$4,$5&#125;&#125;&#39;|sed &quot;s/;.*;//g&quot;|sed &quot;s/ID=//g&quot;|sed &quot;s/ /\t/g&quot; &gt;sample.gff</code> 从gff3文件准备sample.gff文件</li>
-</ul>
+<h2 id="2-2-准备输入文件"><a href="#2-2-准备输入文件" class="headerlink" title="2.2. 准备输入文件"></a>2.2. 准备输入文件</h2><p>输入文件包括两个基因组的CDS&#x2F;氨基酸序列文件（fa格式），两个基因组的注释坐标文件（bed格式）。</p>
+<h3 id="2-2-1-jcvi支持直接从phytozome下载数据"><a href="#2-2-1-jcvi支持直接从phytozome下载数据" class="headerlink" title="2.2.1. jcvi支持直接从phytozome下载数据"></a>2.2.1. jcvi支持直接从phytozome下载数据</h3><ol>
+<li><p>连接phytozome（需要登录，提前注册账号）<br><code>python -m jcvi.apps.fetch phytozome</code> 输入账户登录信息后，出现可以下载的基因组list。</p>
+</li>
+<li><p>下载CDS序列和注释文件(gff格式)，例如下载这两个物种:grape and peach<br><code>python -m jcvi.apps.fetch phytozome Vvinifera,Ppersica</code></p>
+</li>
+<li><p>转换GFF为BED格式<br><code>python -m jcvi.formats.gff bed --type=mRNA --key=Name  --primary_only Vvinifera_145_gene.gff3.gz -o grape.bed</code></p>
+</li>
+</ol>
+<p><code>python -m jcvi.formats.gff bed --type=mRNA --key=Name  --primary_only Ppersica_139_gene.gff3.gz -o peach.bed</code></p>
+<p>其中<code>--primary_only</code>是用于去除多个转录本的，只保留一个。（原文是多个isoforms）一般不去也不影响后续步骤，加上更好吧。</p>
+<ol start="4">
+<li>整理一下，获得CDS文件<br><code>python -m jcvi.formats.fasta format Vvinifera_145_Genoscope.12X.cds.fa.gz grape.cds</code></li>
+</ol>
+<p><code>python -m jcvi.formats.fasta format Ppersica_298_v2.1.cds.fa.gz peach.cds</code></p>
+<h3 id="2-2-2-自己的数据"><a href="#2-2-2-自己的数据" class="headerlink" title="2.2.2. 自己的数据"></a>2.2.2. 自己的数据</h3><ol>
+<li>gff2bed<br><code>python -m jcvi.formats.gff bed --type=mRNA --key=ID --primary_only sampleA.gff3.gz &gt; sampleA.bed</code></li>
+</ol>
+<p><code>python -m jcvi.formats.gff bed --type=mRNA --key=ID --primary_only sampleB.gff3.gz &gt; sampleB.bed</code></p>
+<p>其中<code>--primary_only</code>是用于去除多个转录本的，只保留一个。（原文是多个isoforms）一般不去也不影响后续步骤，加上更好吧。</p>
 <ol start="2">
-<li>sample.blast</li>
+<li><p>去重，会生成sampleA.uniq.bed和sampleB.uniq.bed，然后重命名</p>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">python -m jcvi.formats.bed uniq sampleA.bed</span><br><span class="line">mv sampleA.uniq.bed sampleA.bed</span><br><span class="line">python -m jcvi.formats.bed uniq sampleB.bed</span><br><span class="line">mv sampleB.uniq.bed sampleB.bed</span><br></pre></td></tr></table></figure>
+</li>
+<li><p>提取cds和pep</p>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">seqkit grep -f &lt;(cut -f 4 sampleA.uniq.bed ) sampleA.cdna.all.fa.gz | seqkit seq -i &gt; sampleA.cds</span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleA.uniq.bed ) sampleB.pep.all.fa.gz | seqkit seq -i &gt; sampleA.pep </span><br><span class="line"></span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleB.uniq.bed )  sampeB.cdna.fa.gz | seqkit seq -i  &gt; sampleB.cds</span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleB.uniq.bed ) sampleB.pep.fa.gz | seqkit seq -i  &gt; sampleB.pep</span><br></pre></td></tr></table></figure></li>
 </ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">makeblastdb -in sample.pep.fa -dbtype prot -out index/sample.pep #给蛋白序列建库</span><br><span class="line">blastp -query sample.pep.fa -db index/sample.pep -out sample.blast -evalue 1e-5 -num_threads 12 -outfmt 6 -num_alignments 5 &amp; #进行自我比对，生成6号格式的比对结果sample.blast</span><br></pre></td></tr></table></figure>
-
-<h3 id="2-1-2-运行MCScanX"><a href="#2-1-2-运行MCScanX" class="headerlink" title="2.1.2. 运行MCScanX"></a>2.1.2. 运行MCScanX</h3><p>在有sample.gff和sample.blast两个文件的目录下，指定前缀sample运行<code>MCScanX sample</code>。</p>
-<p>重要参数解释：</p>
+<h2 id="2-3-同线性点图"><a href="#2-3-同线性点图" class="headerlink" title="2.3. 同线性点图"></a>2.3. 同线性点图</h2><h3 id="2-3-1-CDS的同线性点图dotplot"><a href="#2-3-1-CDS的同线性点图dotplot" class="headerlink" title="2.3.1. CDS的同线性点图dotplot"></a>2.3.1. CDS的同线性点图dotplot</h3><ol>
+<li>确保<code>sampleA.cds</code>,<code>sampleA.bed</code>,<code>sampleB.cds</code>,<code>sampleB.bed</code>四个文件在同一目录下。</li>
+<li>运行<code>python -m jcvi.compara.catalog ortholog --no_strip_names --cpu=1 sampleA sampleB</code>制作同线性点图。</li>
+</ol>
+<p><code>--cpu=1</code>是因为conda安装的lastal不支持多线程，所以用单线程。</p>
+<ol start="3">
+<li>运行时如果报错<code>ERROR savefig failed. Reset usetex to False</code>是缺少dvipng包没能生成png格式图，但看看结果文件sampleA.sampleB.anchors还是完整生成了就可以进行下一步。</li>
+</ol>
+<h3 id="2-3-2-蛋白的同线性点图"><a href="#2-3-2-蛋白的同线性点图" class="headerlink" title="2.3.2. 蛋白的同线性点图"></a>2.3.2. 蛋白的同线性点图</h3><p>与CDS一样，cds数据换成pep数据，在运行参数里加上数据类型<code>--dbtype prot</code>即可。</p>
+<p><code>python -m jcvi.compara.catalog ortholog --dbtype prot --no_strip_names --cpu=1 sampleA sampleB</code></p>
+<p><code>--cpu=1</code>是因为conda安装的lastal不支持多线程，所以用单线程。</p>
+<h3 id="2-3-3-结果"><a href="#2-3-3-结果" class="headerlink" title="2.3.3. 结果"></a>2.3.3. 结果</h3><p>会生成五个结果文件。</p>
 <ul>
-<li>-s MATCH_SIZE,default: 5。每个共线性区块包含的基因数量的下限。</li>
-<li>-m MAX_GAPS，default：25。在共线性区块中允许的最大gaps数量。</li>
-<li>-b patterns of collinear blocks。0:intra- and inter-species (default); 1:intra-species; 2:inter-species。</li>
+<li>sampleA.sampleB.last: 基于LAST的比对结果</li>
+<li>sampleA.sampleB.last.filtered: LAST的比对结果过滤串联重复和低分比对</li>
+<li>sampleA.sampleB.anchors: 高质量的同线性区块</li>
+<li>sampleA.sampleB.lifted.anchors:增加了额外的锚点，形成最终的同线性区块</li>
+<li>sampleA.sampleB.pdf:同线性点图</li>
 </ul>
-<h2 id="2-2-种间同线性分析MCScanX-h"><a href="#2-2-种间同线性分析MCScanX-h" class="headerlink" title="2.2. 种间同线性分析MCScanX_h"></a>2.2. 种间同线性分析MCScanX_h</h2><p>MCScanX_h是MCScanX软件下的一个程序，输入文件和运行方式与MCScanX很像。</p>
-<ol>
-<li>原理</li>
+<p>同线性点图一般可以看点连成的比较长的线，在同一横坐标范围有几根线，或者在同一纵坐标范围有几根线，从而判断两个基因组的倍数关系。</p>
+<img src="https://www.dropbox.com/s/32mmo0kfhtx5b8g/grape.peach.cscore.99.png?raw=1" width=100% title="同线性点图" alt="同线性点图" align=center/>
+
+<p><strong><p align="center">Figure 1. 同线性点图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi viki</a></p></p>
+<h2 id="2-4-同线性深度直方图"><a href="#2-4-同线性深度直方图" class="headerlink" title="2.4. 同线性深度直方图"></a>2.4. 同线性深度直方图</h2><ol>
+<li>在同线性点图运行成功获得sampleA.sampleB.anchors结果文件的前提下</li>
+<li>运行<code>python -m jcvi.compara.synteny depth --histogram sampleA.sampleB.anchors</code></li>
+<li>生成sampleA.sampleB.depth.pdf，显示了同线性深度比例。</li>
+</ol>
+<img src="https://www.dropbox.com/s/hhx2dtryrum2gyo/grape.peach.depth.png?raw=1" width=100% title="同线性深度直方图" alt="同线性深度直方图" align=center/>
+
+<p><strong><p align="center">Figure 2. 同线性深度直方图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi viki</a></p></p>
+<h2 id="2-5-同线性染色体图"><a href="#2-5-同线性染色体图" class="headerlink" title="2.5. 同线性染色体图"></a>2.5. 同线性染色体图</h2><h3 id="2-5-1-输入文件"><a href="#2-5-1-输入文件" class="headerlink" title="2.5.1. 输入文件"></a>2.5.1. 输入文件</h3><ol>
+<li>seqids文件</li>
 </ol>
 <ul>
-<li>可用MCScanX_h分析指定基因对的同线性关系，用于确认orthofinder或者OrthoMCL等软件鉴定的物种间的orthologs基因对。</li>
-<li>如果要分析物种间orthologs的Ks分布来确定物种分化时间，直接用MCScanX做物种间共线性分析会同时得出orthologs和paralogs的结果，但MCScanX_h则可以指定orthofinder或者OrthoMCL鉴定的orthologs基因对。</li>
-<li>可以这样理解，orthofinder鉴定出物种A和物种B的orthologs（物种A和B各只有一个基因的那些行），然后用MCScanX_h确认鉴定的orthologs的同线性关系，从而更加确认orthologs的可靠性。</li>
+<li>指定展示的染色体ID，两行对应两个物种，每行的染色体之间逗号隔开，不能有空行。</li>
+<li>seqids文件染色体的顺序对应同线性染色体图的顺序，可以根据前面得到的同线性点图来调整顺序使两个基因组的同线性对应，使图更清晰。</li>
 </ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">scaf001,scaf002,scaf003,scaf004,scaf005,scaf006,scaf007,scaf008,scaf009,scaf010,scaf011,scaf012,scaf013,scaf014</span><br><span class="line">scaffold1,scaffold2,scaffold3,scaffold4,scaffold5,scaffold6,scaffold7</span><br></pre></td></tr></table></figure>
+
 <ol start="2">
-<li>运行的区别</li>
+<li>.simple文件</li>
 </ol>
 <ul>
-<li>区别是输入文件用sample.homology代替sample.blast。</li>
-<li>sample.homology是tab分隔的成对基因ID的list。</li>
+<li>每行代表一个同线性区块</li>
+<li>一共六列，前四列是两个物种的同线性区块的上下限基因，第五列是评分，第六列是方向。</li>
+<li>用前面分析同线性点图的结果文件.anchors创建。</li>
+<li>运行<code>python -m jcvi.compara.synteny screen --minspan=30 --simple sampleA.sampleB.anchors sampleA.sampleB.anchors.new</code>会生成sampleA.sampleB.anchors.simple文件。</li>
 </ul>
-<h3 id="2-2-1-输入文件"><a href="#2-2-1-输入文件" class="headerlink" title="2.2.1. 输入文件"></a>2.2.1. 输入文件</h3><ol>
-<li>sample.gff：格式同上面MCScanX的说明。</li>
-<li>sample.homology</li>
+<ol start="3">
+<li>layout文件</li>
 </ol>
 <ul>
-<li>包含两列数据，是两个物种的基因对，代表一一对应的同源关系。这里用来计算物种间分化，所以指定的是两个物种间一一对应的直系同源关系。</li>
-<li>可从orthofinder或者OrthoMCL等软件鉴定的物种间提取。</li>
-<li>从orthofinder的<code>./Results_Apr01/Orthogroups/Orthogroups.txt</code>文件提取只包含物种A和物种B各有一个基因的行。如果是多物种做的orthofinder，则提取只包含物种A和物种B各有一个基因的行后把其他物种删除。提取后把列间分隔符改成tab。（ps：用不同物种跑的orthofinder提取结果会有一点差异）</li>
+<li>设置绘制参数。前三列是设置图的位置坐标信息，整个图的x和y轴都是[0-1]范围。第四列是设置旋转角度，第五列是设置染色体颜色，第六列是展示的标签，第七列是对齐方式，第八列指定bed文件。</li>
+<li>注意, #edges下的每一行开头都不能有空格。用于指定simple文件。<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"># y, xstart, xend, rotation, color, label, va,  bed</span><br><span class="line"> .6,     .2,    .8,       0,      lightblue, sample_A, top, sampleA.bed</span><br><span class="line"> .4,     .2,    .8,       0,      lightpink, sample_B, top, sampleB.bed</span><br><span class="line"># edges</span><br><span class="line">e, 0, 1, sampleA.sampleB.anchors.simple</span><br></pre></td></tr></table></figure></li>
 </ul>
-<h3 id="2-2-2-运行MCScanX-h"><a href="#2-2-2-运行MCScanX-h" class="headerlink" title="2.2.2. 运行MCScanX_h"></a>2.2.2. 运行MCScanX_h</h3><p>与运行MCScanX一样:</p>
+<h3 id="2-5-2-运行"><a href="#2-5-2-运行" class="headerlink" title="2.5.2. 运行"></a>2.5.2. 运行</h3><p><code>python -m jcvi.graphics.karyotype seqids layout</code></p>
+<p>生成karyotype.pdf文件，为同线性双染色体图</p>
+<img src="https://www.dropbox.com/s/51k8jujyzage3oa/grape.peach.karyotype.png?raw=1" width=100% title="同线性双染色体图" alt="同线性双染色体图" align=center/>
+
+<p><strong><p align="center">Figure 3. 同线性双染色体图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi wiki</a></p></p>
+<h3 id="2-5-3-tips"><a href="#2-5-3-tips" class="headerlink" title="2.5.3. tips"></a>2.5.3. tips</h3><ul>
+<li>运行虽然报错<code>ERROR    savefig failed. Reset usetex to False.</code>，应该是没能生成png图，但还是生成了pdf图文件。</li>
+<li>当label用的名称过长时，label和图容易重叠，没有好的办法解决，最好用短一点的label。</li>
+</ul>
+<h3 id="2-5-4-个性化"><a href="#2-5-4-个性化" class="headerlink" title="2.5.4. 个性化"></a>2.5.4. 个性化</h3><h4 id="2-5-4-1-highlight指定同线性区块"><a href="#2-5-4-1-highlight指定同线性区块" class="headerlink" title="2.5.4.1. highlight指定同线性区块"></a>2.5.4.1. highlight指定同线性区块</h4><p>如果要突出显示指定的同线性区块（一般用于展示染色体倍数关系），则在sampleA.sampleB.anchors.sample文件中的对应同线性区域行的前面加上<code>yellowgreen*</code>,表示这行的颜色用yellowgreen展示。</p>
+<p>然后再运行<code>python -m jcvi.graphics.karyotype seqids layout</code>就会生成突出显示的图。</p>
+<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
+<li><a href="https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)">https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)</a></li>
+<li><a href="https://xuzhougeng.top/archives/Comparative-genomics-using-JCVI-part-one">https://xuzhougeng.top/archives/Comparative-genomics-using-JCVI-part-one</a></li>
+</ol>
+<hr>
 <ul>
-<li>在有sample.gff和sample.homology两个文件的目录下，指定前缀sample运行<code>MCScanX_h sample</code>。</li>
-<li>参数也一致。</li>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
 </ul>
-<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><ol>
-<li>sample.collinearity</li>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>synteny</category>
+      </categories>
+      <tags>
+        <tag>biosoft</tag>
+        <tag>synteny</tag>
+        <tag>colinearity</tag>
+        <tag>MCscan</tag>
+        <tag>jcvi</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>使用Rldeogram的ideogram函数画两物种的同线性图</title>
+    <url>/2022/08/03/bioinfo_synteny_plot_Rldeogram/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=20707476&auto=1&height=32"></iframe><music URL></div>
+
+<h1 id="1-使用Rldeogram的ideogram函数画两物种的共线性图"><a href="#1-使用Rldeogram的ideogram函数画两物种的共线性图" class="headerlink" title="1. 使用Rldeogram的ideogram函数画两物种的共线性图"></a>1. 使用Rldeogram的ideogram函数画两物种的共线性图</h1><h2 id="1-1-输入文件"><a href="#1-1-输入文件" class="headerlink" title="1.1. 输入文件"></a>1.1. 输入文件</h2><ol>
+<li>karyotype.txt</li>
 </ol>
-<p>共线性结果文件，包括三部分内容：</p>
 <ul>
-<li>参数(parameters)</li>
-<li>基本统计信息(statistics)：共线性基因的总数，总基因数，共线性基因占比。</li>
-<li>共线性区块(block)信息：一个Alignment代表一个共线性区块（0起始编号）。后面跟着这个共线性区块的基因对的信息。第一列：block编号；第二列：基因对编号；第三列和第四列：基因对名称；第五列：blast比对的e_value值。</li>
+<li>Chr: 染色体号</li>
+<li>Start: 起始</li>
+<li>End: 终止</li>
+<li>fill: 染色体填充色</li>
+<li>species：物种名</li>
+<li>size: 物种名字体大小</li>
+<li>color: 物种名字体颜色</li>
 </ul>
-<p>sample.collinearity示例：</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">############### Parameters ###############</span><br><span class="line"># MATCH_SCORE: 50</span><br><span class="line"># MATCH_SIZE: 5</span><br><span class="line"># GAP_PENALTY: -1</span><br><span class="line"># OVERLAP_WINDOW: 5</span><br><span class="line"># E_VALUE: 1e-05</span><br><span class="line"># MAX GAPS: 25</span><br><span class="line">############### Statistics ###############</span><br><span class="line"># Number of collinear genes: 21371, Percentage: 56.25</span><br><span class="line"># Number of all genes: 37996</span><br><span class="line">##########################################</span><br><span class="line">## Alignment 0: score=352.0 e_value=1.2e-17 N=8 scaf014&amp;scaf014 plus</span><br><span class="line">477-  0:	bv37796	bv37889	  3e-06</span><br><span class="line">477-  1:	bv37820	bv37905	  6e-24</span><br><span class="line">477-  2:	bv37822	bv37926	  9e-29</span><br><span class="line">477-  3:	bv37826	bv37930	  4e-62</span><br><span class="line">477-  4:	bv37828	bv37931	  1e-74</span><br><span class="line">477-  5:	bv37829	bv37932	 3e-142</span><br><span class="line">477-  6:	bv37830	bv37933	  6e-78</span><br><span class="line">477-  7:	bv37832	bv37934	  3e-76</span><br><span class="line">## Alignment 1: score=300.0 e_value=6.1e-08 N=6 scaf014&amp;scaf014 minus</span><br><span class="line">478-  0:	bv35514	bv35525	      0</span><br><span class="line">478-  1:	bv35515	bv35524	      0</span><br><span class="line">478-  2:	bv35516	bv35523	  1e-81</span><br><span class="line">478-  3:	bv35517	bv35522	      0</span><br><span class="line">478-  4:	bv35518	bv35521	 1e-115</span><br><span class="line">478-  5:	bv35519	bv35520	      0</span><br><span class="line">... ...</span><br><span class="line">... ...</span><br></pre></td></tr></table></figure>
+<p>文件示例：</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr Start      End   fill species size  color</span><br><span class="line">1   1 23037639  FF8C00   Grape   12 252525</span><br><span class="line">2   1 18779884  FF8C00   Grape   12 252525</span><br><span class="line">3   1 17934068  FF8C00   Grape   12 252525</span><br><span class="line">4   1 17349521  FF8C00   Grape   12 252525</span><br><span class="line">1   1 22042719  4682B4   Populus   12 252525</span><br><span class="line">2   1 19858802  4682B4   Populus   12 252525</span><br><span class="line">3   1 19278319  4682B4   Populus   12 252525</span><br></pre></td></tr></table></figure>
 
 <ol start="2">
-<li>sample.html</li>
+<li>synteny.txt</li>
 </ol>
-<p>网页文件所在的文件夹，里面有每条染色体一个html文件。html文件用浏览器打开，包含三列信息。</p>
 <ul>
-<li>第一列是复制深度。</li>
-<li>第二列是这条染色体上所有基因的排列顺序，串联重复基因的背景为红色。</li>
-<li>第三列和之后列是对应的比对上的基因名称。</li>
+<li>Species_1：物种1染色体号</li>
+<li>Start_1，End_1：物种1染色体区域位置</li>
+<li>Species_2：物种2染色体号</li>
+<li>Start_2，End_2：物种2染色体区域位置</li>
 </ul>
-<ol start="3">
-<li>sample.tandem</li>
-</ol>
-<p>包含基因组内<strong>串联重复</strong>的基因ID的list。</p>
-<p>notes：MCScanX 会根据 gff 文件中染色体号的前缀（前2个字符）将染色体划分为不同的物种，若 MCScanX 识别到输入数据中包含多个物种，则不会生成 tandem 文件。</p>
-<h1 id="3-MCScanX下游分析"><a href="#3-MCScanX下游分析" class="headerlink" title="3. MCScanX下游分析"></a>3. MCScanX下游分析</h1><h2 id="3-1-提取block位置"><a href="#3-1-提取block位置" class="headerlink" title="3.1. 提取block位置"></a>3.1. 提取block位置</h2><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">cat sample.collinearity|grep -C 1 &quot;Alignment&quot;|sed &#x27;/^--$/d&#x27; &gt;block.tem #提取block首尾基因对</span><br><span class="line">tail -1 sample.collinearity &gt;&gt;block.tem #提取block首尾基因对</span><br><span class="line">cat block.tem |sed -e &quot;s/## //g&quot; -e &quot;s/.*gene/gene/g&quot;|tr &#x27;\n&#x27; &#x27; &#x27;|sed &quot;s/Alignment /\nAlignment/g&quot; |sed -e &quot;/^$/d&quot; -e &quot;s/ /\t/g&quot;|sed -e &quot;s/\t\+/\t/g&quot; -e &quot;s/\://g&quot;|awk -v FS=&quot;\t&quot; -v OFS=&quot;\t&quot; &#x27;&#123;print $1,$2,$3,$4,$5,$6,$7,$10,$8,$11&#125;&#x27; &gt;block.txt #整理成一个block一行信息的格式，其中&quot;s/.*gene/gene/g&quot;部分的gene修改成geneID的前缀用来去除多余信息。</span><br></pre></td></tr></table></figure>
+<p>文件示例：</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Species_1  Start_1    End_1 Species_2 Start_2   End_2   fill</span><br><span class="line">1   12226377    12267836    1   5900307 5827251 cccccc</span><br><span class="line">1  5635667 5667377 2 4459512  4393226 cccccc</span><br><span class="line">1   7916366 7945659 3 8618518   8486865 cccccc</span><br><span class="line">2   8214553 8242202 1 5964233  6027199 cccccc</span><br><span class="line">3  2330522 2356593 1 6224069  6138821 cccccc</span><br><span class="line">3  10861038    10886821    2  8099058 8011502 cccccc</span><br><span class="line">4  9487312    9540261    3  7657579 7701112 cccccc</span><br></pre></td></tr></table></figure>
 
-<h2 id="3-2-绘图脚本"><a href="#3-2-绘图脚本" class="headerlink" title="3.2. 绘图脚本"></a>3.2. 绘图脚本</h2><p>MCScanX的downstream_analyses目录包含一些下游脚本。</p>
-<p>许多java脚本可以实现绘图功能，包括：</p>
+<h2 id="1-2-运行"><a href="#1-2-运行" class="headerlink" title="1.2. 运行"></a>1.2. 运行</h2><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&#x27;RIdeogram&#x27;</span><span class="punctuation">)</span> <span class="comment">#安装RIdeogram</span></span><br><span class="line">library<span class="punctuation">(</span><span class="string">&#x27;RIdeogram&#x27;</span><span class="punctuation">)</span> <span class="comment">#载入RIdeogram</span></span><br><span class="line">ka <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;karyotype.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">,</span>header <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span>stringsAsFactors <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span> <span class="comment">#读取karyotype.txt文件</span></span><br><span class="line">sy <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;synteny.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">,</span>header <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span>stringsAsFactors <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span> <span class="comment">#读取synteny.txt文件</span></span><br><span class="line">ideogram<span class="punctuation">(</span>karyotype <span class="operator">=</span> ka<span class="punctuation">,</span> synteny <span class="operator">=</span> sy<span class="punctuation">)</span> <span class="comment">#使用ideogram函数，生成chromosome.svg文件用于绘图</span></span><br><span class="line">convertSVG<span class="punctuation">(</span><span class="string">&quot;chromosome.svg&quot;</span><span class="punctuation">,</span> device <span class="operator">=</span> <span class="string">&quot;pdf&quot;</span><span class="punctuation">,</span>dpi<span class="operator">=</span><span class="number">1600</span><span class="punctuation">)</span> <span class="comment">#转化成chromosome.pdf文件，还可选择转化的格式：tiff，png，jpg，分辨率1600。</span></span><br></pre></td></tr></table></figure>
+
+<h2 id="1-3-结果"><a href="#1-3-结果" class="headerlink" title="1.3. 结果"></a>1.3. 结果</h2><p>结果如下图：</p>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/ideogram.png?raw=true" title="Rldeogram绘制的同线性图" width="80%" />
+
+<p><strong><p align="center">Figure 1. Rldeogram绘制的同线性图</p></strong></p>
+<h1 id="2-references"><a href="#2-references" class="headerlink" title="2. references"></a>2. references</h1><ol>
+<li><a href="https://www.jianshu.com/p/07ae1fe18071">https://www.jianshu.com/p/07ae1fe18071</a></li>
+</ol>
+<hr>
 <ul>
-<li>共线性点图：dot_plotter</li>
-<li>双染色体共线性图：dual_synteny_plotter</li>
-<li>共线性圈图：circle_plotter</li>
-<li>染色体块图：bar_plotter</li>
-<li>…</li>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
 </ul>
-<h2 id="3-3-复制事件分类"><a href="#3-3-复制事件分类" class="headerlink" title="3.3. 复制事件分类"></a>3.3. 复制事件分类</h2><h3 id="3-3-1-duplicate-gene-classifier"><a href="#3-3-1-duplicate-gene-classifier" class="headerlink" title="3.3.1. duplicate_gene_classifier"></a>3.3.1. duplicate_gene_classifier</h3><ol>
-<li>duplicate_gene_classifier</li>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>synteny</category>
+        <category>plot</category>
+      </categories>
+      <tags>
+        <tag>R package</tag>
+        <tag>R</tag>
+        <tag>plot</tag>
+        <tag>synteny</tag>
+        <tag>Rldeogram</tag>
+        <tag>ideogram</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>转录因子（transcription factor，TF）基础及WGD后保留的TF分析</title>
+    <url>/2022/10/18/bioinfo_transcriptionFactor/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105140&auto=1&height=32"></iframe></div>
+
+
+<h1 id="1-转录因子（transcription-factor）"><a href="#1-转录因子（transcription-factor）" class="headerlink" title="1. 转录因子（transcription factor）"></a>1. 转录因子（transcription factor）</h1><p>转录因子（transcription factor，TF）是一种蛋白质，它通过与特定DNA序列结合来控制遗传信息从DNA到信使RNA的转录速率。</p>
+<p>TFs 的功能是调节——打开和关闭——基因，以确保它们在所需的细胞中在正确的时间和正确的数量表达。TF 组以协调的方式发挥作用，在整个生命过程中指导细胞分裂、细胞生长和细胞死亡；胚胎发育过程中的细胞迁移和组织；并且间歇性地响应来自细胞外的信号，例如激素。人类基因组中有多达 1600 个 TF 。转录因子是蛋白质组和调节组的成员。</p>
+<h1 id="2-植物转录因子数据库PlantTFDB"><a href="#2-植物转录因子数据库PlantTFDB" class="headerlink" title="2. 植物转录因子数据库PlantTFDB"></a>2. 植物转录因子数据库PlantTFDB</h1><p>植物转录因子数据库PlantTFDB是北京大学生物信息学中心研发的数据库和网站，目前包括165个植物物种的转录因子。</p>
+<p>目前数据库已更新到v5.0，在网站<a href="http://planttfdb.gao-lab.org/index.php%E5%8F%AF%E4%BB%A5%E6%9F%A5%E7%9C%8B%E3%80%81%E4%B8%8B%E8%BD%BD%E5%92%8C%E4%BD%BF%E7%94%A8%E6%A4%8D%E7%89%A9%E8%BD%AC%E5%BD%95%E5%9B%A0%E5%AD%90%E6%95%B0%E6%8D%AE%E5%BA%93%E3%80%82">http://planttfdb.gao-lab.org/index.php可以查看、下载和使用植物转录因子数据库。</a></p>
+<p>网站的功能包括：</p>
+<ol>
+<li>上传核酸或蛋白质的fasta序列，在线做转录因子的注释。</li>
+<li>上传核酸或蛋白质的fasta序列，在线与数据库做blastx或blastp比对。</li>
+<li>下载特定植物的TF列表，CDS或蛋白质序列。</li>
+<li>查询特定TF和TF家族的功能描述。</li>
 </ol>
-<p>MCScanX的<code>duplicate_gene_classifier</code>脚本用来分析与各种复制类别相关的基因数量</p>
+<h1 id="3-转录因子相关分析"><a href="#3-转录因子相关分析" class="headerlink" title="3. 转录因子相关分析"></a>3. 转录因子相关分析</h1><p>转录因子分析可以应用的场景很多，这里介绍全基因组复制事件（WGD）后转录因子保留的分析。</p>
+<h2 id="3-1-WGD后保留TF的分析"><a href="#3-1-WGD后保留TF的分析" class="headerlink" title="3.1. WGD后保留TF的分析"></a>3.1. WGD后保留TF的分析</h2><h3 id="3-1-1-思路"><a href="#3-1-1-思路" class="headerlink" title="3.1.1. 思路"></a>3.1.1. 思路</h3><p>除了直接看WGD后保留的基因中包含了什么种类和多少数量的TF外，还可以通过利用转录因子数据库PlantTFDB来做WGD后保留的每种TF的保留模式的进一步分析。</p>
+<ol>
+<li>参考</li>
+</ol>
+<ul>
+<li>paper: <a href="https://www.sciencedirect.com/science/article/pii/S1674205219303594">https://www.sciencedirect.com/science/article/pii/S1674205219303594</a> 的 Retention Analysis of Transcription Factors部分。</li>
+<li>在博客<strong>鉴定全基因复制事件(WGD)后保留的复制基因</strong> <a href="https://yanzhongsino.github.io/2022/10/18/bioinfo_WGD_geneRetention/">https://yanzhongsino.github.io/2022/10/18/bioinfo_WGD_geneRetention/</a> 的基础上完成WGD后保留TF的分析</li>
+</ul>
 <ol start="2">
-<li>输入</li>
+<li>基本思路</li>
 </ol>
-<p><code>duplicate_gene_classifier sample</code> # 输入与MCScanX一致，读取当前目录下的sample.gff和sample.blast作为输入</p>
+<ul>
+<li>从PlantTFDB数据库下载已有物种（比如拟南芥）的TF家族，用下载的TF家族注释orthogroups。对每个WGD事件，确认每个TF家族的保留的orthogroups的数量。</li>
+<li>有些TF家族可能会被分到几个orthogroups，为了消除一个TF家族的orthogroups的大小不均的影响，文章通过标准化计算一个保留参数R值（retention value），R值用来反映WGD事件后每个TF的保留模式。</li>
+</ul>
 <ol start="3">
-<li>输出示例</li>
+<li>R值的计算公式：$$Rvalue&#x3D;(Rs⁄Ts)&#x2F;(Ra⁄Ta)&#x3D;Rs<em>Ta&#x2F;Ts</em>Ra$$，其中：</li>
 </ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Type of dup	Code	Number</span><br><span class="line">Singleton	0	88907</span><br><span class="line">Dispersed	1	13573</span><br><span class="line">Proximal	2	463</span><br><span class="line">Tandem	3	847</span><br><span class="line">WGD or segmental	4	1055</span><br></pre></td></tr></table></figure>
-
-<ol start="4">
-<li>结果解释</li>
+<ul>
+<li>Rs: Number of orthogroups with retention in specific TF</li>
+<li>Ts: Total number of orthogroups in specific TF</li>
+<li>Ra: Number of all TF orthogroups with retention</li>
+<li>Ta: Total number of TF orthogroups</li>
+<li>Rs&#x2F;Ts: 代表在WGD后特定TF家族保留的可能性</li>
+<li>Ra&#x2F;Ta: 代表在WGD后所有TF家族保留的可能性</li>
+<li>Rvalue: 用Rs&#x2F;Ts比上Ra&#x2F;Ta，代表相较TF家族平均水平，特定TF家族保留的可能性的高低。Rvalue越大，特定TF家族的保留率越高。</li>
+</ul>
+<h3 id="3-1-2-准备文件"><a href="#3-1-2-准备文件" class="headerlink" title="3.1.2. 准备文件"></a>3.1.2. 准备文件</h3><ol>
+<li>Orthogroups.txt</li>
 </ol>
-<p>其中 Singleton 表示单拷贝重复，Proximal 表示在相同染色体上相近但不相连的重复，Dispersed 表示除 Tandem、WGD、Proximal 以外的重复。Number代表相应复制类别相关的基因数量。</p>
-<h2 id="3-4-解析结果文件sample-collinearity的脚本"><a href="#3-4-解析结果文件sample-collinearity的脚本" class="headerlink" title="3.4. 解析结果文件sample.collinearity的脚本"></a>3.4. 解析结果文件sample.collinearity的脚本</h2><p>推荐这里<a href="https://github.com/reubwn/collinearity%E6%9C%89%E8%AE%B8%E5%A4%9A%E5%8F%AF%E4%BB%A5%E8%A7%A3%E6%9E%90%E6%9E%9C%E6%96%87%E4%BB%B6sample.collinearity%E7%9A%84%E8%84%9A%E6%9C%AC">https://github.com/reubwn/collinearity有许多可以解析果文件sample.collinearity的脚本</a></p>
-<h2 id="3-5-计算共线性区块Ks"><a href="#3-5-计算共线性区块Ks" class="headerlink" title="3.5. 计算共线性区块Ks"></a>3.5. 计算共线性区块Ks</h2><p>tips:需要检查得到的结果中Ks值是否有负值或NA等无效数据，并过滤无效数据。</p>
-<h3 id="3-5-1-方案一：add-ka-and-ks-to-collinearity-pl（MCScanX的downstream-analyses目录下自带脚本）"><a href="#3-5-1-方案一：add-ka-and-ks-to-collinearity-pl（MCScanX的downstream-analyses目录下自带脚本）" class="headerlink" title="3.5.1. 方案一：add_ka_and_ks_to_collinearity.pl（MCScanX的downstream_analyses目录下自带脚本）"></a>3.5.1. 方案一：add_ka_and_ks_to_collinearity.pl（MCScanX的downstream_analyses目录下自带脚本）</h3><p><code>add_ka_and_ks_to_collinearity.pl -i sample.collinearity -d sample.cds.fa -o sample.kaks &gt; out.log 2&gt;&amp;1</code> #注意算出的部分kaks值为-2的问题，未找到解决方案</p>
-<h3 id="3-5-2-方案二：ParaAT-pl-KaKs-Calculator2-0"><a href="#3-5-2-方案二：ParaAT-pl-KaKs-Calculator2-0" class="headerlink" title="3.5.2. 方案二：ParaAT.pl+KaKs_Calculator2.0"></a>3.5.2. 方案二：ParaAT.pl+KaKs_Calculator2.0</h3><ul>
-<li>ParaAT.pl用于根据同源基因对list生成比对的gene对cds序列，并可以指定输出格式，如axt格式；</li>
-<li>KaKs_Calculator用于计算基因对的kaks。</li>
+<ul>
+<li>Orthofinder的结果文件&#x2F;path&#x2F;to&#x2F;OrthoFinder&#x2F;Results_xx&#x2F;Orthogroups&#x2F;Orthogroups.txt</li>
+<li>Orthofinder运行时需要包含了下载TF的物种</li>
 </ul>
-<p>用还会用到两个脚本:</p>
+<ol start="2">
+<li>dup_wgd.og</li>
+</ol>
 <ul>
-<li><a href="https://github.com/scbgfengchao/4DTv/blob/master/axt2one-line.py">axt2one-line.py</a>转换axt格式为单行</li>
-<li><a href="https://github.com/JinfengChen/Scripts/blob/master/FFgenome/03.evolution/distance_kaks_4dtv/bin/calculate_4DTV_correction.pl">calculate_4DTV_correction.pl</a>计算4dtv。</li>
+<li>dup_wgd.og包含了前期分析的基因复制的、涉及特定WGD保留的那些orthogroups的ID列表。</li>
+<li>可以从WGD后保留基因的分析的结果文件<code>N5_filter_OG_dup.tsv</code>中提取第二列来获取：<code>cat N5_filter_OG_dup.tsv|cut -f2 &gt;dup_wgd.og</code>。</li>
 </ul>
-<ol>
-<li>安装ParaAT.pl<br>参考<a href="https://yanzhongsino.github.io/2021/10/29/bioinfo_align.pep2cds/">blog.ParaAT</a></li>
+<ol start="3">
+<li>下载Ath_TF_list.txt并转化成Ath_TF_list.og</li>
 </ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget ftp://download.big.ac.cn/bigd/tools/ParaAT2.0.tar.gz</span><br><span class="line">tar -zxf ParaAT2.0.tar.gz</span><br><span class="line">cd ParaAT2.0</span><br><span class="line">ParaAT.pl -h</span><br></pre></td></tr></table></figure>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cat Ath_TF_list.txt|sed &#x27;1d&#x27;|cut -f2 &gt;ath_2.tem # 提取第二列geneID</span><br><span class="line">cat Ath_TF_list.txt |sed &#x27;1d&#x27;|cut -f3|sort|uniq &gt;ath.tf # 提取第三列Family</span><br><span class="line">for i in $(cat ath_2.tem); do echo $i &gt;&gt;ath.og &amp;&amp; grep $i /path/to/OrthoFinder/Results_xx/Orthogroups/Orthogroups.txt|cut -d &quot;:&quot; -f1 &gt;&gt;ath.og ; done # 根据geneID提取orthogroups</span><br><span class="line">sed -i -e &#x27;1i\Gene_ID\torthogroups_ID&#x27; -e &quot;s/ /\t/g&quot; ath.og # 在ath.og文件首行前插入标题行，并把列间分隔的空格改成tab分隔。</span><br><span class="line">paste Ath_TF_list.txt ath.og &gt;Ath_TF_list.og # 横向拼接Ath_TF_list.txt和ath.og两个文件</span><br><span class="line">head Ath_TF_list.og &amp;&amp; tail Ath_TF_list.og # 检查一下首尾的第二列和第四列是不是一样，看有没有拼接错误</span><br></pre></td></tr></table></figure>
 
+<h3 id="3-1-3-统计R相关参数"><a href="#3-1-3-统计R相关参数" class="headerlink" title="3.1.3. 统计R相关参数"></a>3.1.3. 统计R相关参数</h3><ol>
+<li>这里的Ta、Ra、Ts、Rs可以用两种数量来代表，一种是统计TF_ID的数量，另一种是统计Orthogroups的数量。</li>
+</ol>
+<ul>
+<li>下面的是统计TF_ID的数量，如果想要统计Orthogroups的数量，则需要在每一个值统计命令<code>wc -l</code>前面加上<code>cut -f5|sort|uniq|</code>来提取Orthogroups并去重。</li>
+</ul>
 <ol start="2">
-<li>安装KaKs_Calculator2.0<br><a href="https://sourceforge.net/projects/kakscalculator2/?source=typ_redirect">KaKs_Calculator2.0下载地址</a></li>
+<li>对每一个ath.tf里的Family，统计Ta,Ra,Rs和Ts值</li>
 </ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">wget https://altushost-swe.dl.sourceforge.net/project/kakscalculator2/KaKs_Calculator2.0.tar.gz</span><br><span class="line">tar -zxf KaKs_Calculator2.0.tar.gz</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/bin/Linux/KaKs_Calculator</span><br><span class="line">chmod 777 ./KaKs_Calculator2.0/src/AXTConvertor</span><br></pre></td></tr></table></figure>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">for i in $(cat ath.tf);</span><br><span class="line">do</span><br><span class="line">    ta = $(($(cat Ath_TF_list.ogs|wc -l)-1)) # 统计Ta值（有标题行，结果需要减1）</span><br><span class="line">    ra = $(grep -f dup_wgd.og Ath_TF_list.ogs|wc -l) # 统计Ra值</span><br><span class="line">	rs=$(grep -f dup_wgd.og Ath_TF_list.ogs |awk -v awka=&quot;$i&quot; &#x27;$3 == awka &#123;print$0&#125;&#x27;|wc -l);</span><br><span class="line">	ts=$(awk -v awka=&quot;$i&quot; &#x27;$3 == awka &#123;print $0&#125;&#x27; Ath_TF_list.ogs |wc -l);</span><br><span class="line">	echo &quot;$&#123;i&#125; $&#123;rs&#125; $&#123;ts&#125; $&#123;ra&#125; $&#123;ta&#125;&quot; &gt;&gt; ath_r.tem</span><br><span class="line">done</span><br></pre></td></tr></table></figure>
 
-<p>然后把.&#x2F;KaKs_Calculator2.0&#x2F;bin&#x2F;Linux&#x2F;和.&#x2F;KaKs_Calculator2.0&#x2F;src&#x2F;添加到环境变量即可使用KaKs_Calculator和AXTConvertor命令。</p>
 <ol start="3">
-<li>用ParaAT.pl获取共线性基因对比对序列<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cat sample.collinearity |grep -v &quot;^#&quot;|cut -f2,3 &gt;sample.homolog #提取blocks的同源gene对</span><br><span class="line">echo &quot;24&quot; &gt;proc #指定ParaAT.pl使用线程</span><br><span class="line">ParaAT.pl -g -t -h sample.homolog -n sample.cds.fa -a sample.pep.fa -m mafft -p proc -f axt -o sample.paraat 2&gt; paraat.log &amp; #用ParaAT.pl调用mafft做每对共线性基因的蛋白比对和蛋白转cds比对，输出axt格式。-g移除比对有gap的密码子，-t移除mismatched codons；；-o指定生成目录；</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">加上-k参数可以在获得axt文件后自动调用KaKs_Calculator计算kaks值，使用MA模型，比YN模型慢很多，推荐手动用KaKs_Calculator的YN模型，生成sample.axt_yn.kaks文件。</span></span><br></pre></td></tr></table></figure></li>
+<li>有了Ta,Ra,Rs和Ts值，接下来就可以计算Rvalue&#x3D;(Rs⁄Ts)&#x2F;(Ra⁄Ta)了。</li>
 </ol>
 <ul>
-<li>建议加上-g和-t，免得后面计算Ks时报错Error. The size of two sequences in ‘ctg00816-ctg08844’ is not equal。</li>
-<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
+<li><code>cat ath_r.tem|sed &quot;s/ /\t/g&quot;|awk -F&quot;\t&quot; &#39;&#123;print $0,($1*$4)/($2*$3)&#125;&#39;|sed &#39;1i\TF\tRs\tTs\tRa\tTa\tRvalue&#39; &gt;ath_r.txt</code> 待检查是否有效</li>
 </ul>
-<ol start="4">
-<li>用KaKs_Calculator手动计算共线性基因对的KaKs和4dtv值<br>ParaAT.pl的-k参数只能指定KaKs_Calculator的MA模型计算kaks值，如果需要指定其他的模型，则可以手动运行计算。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cd sample.paraat</span><br><span class="line">for i in `ls *axt`;do KaKs_Calculator -i $i -o $&#123;i&#125;_yn.kaks -m YN;done #用YN模型计算每个gene对的KaKs，生成四列数据，gene对，Ka，Ks，Ka/Ks</span><br><span class="line">for i in `ls |grep &quot;_yn.kaks&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&quot;\t&quot;$4&quot;\t&quot;$5&#125;&#x27; $i &gt;&gt;../all.kaks;done #合并kaks到all.kaks文件</span><br><span class="line">- 计算4dtv</span><br><span class="line">for i in `ls *axt`;do axt2one-line.py $i $&#123;i&#125;.one-line;done #多行axt文件转换成单行</span><br><span class="line">ls |grep &quot;axt.one-line&quot;|while read id;do calculate_4DTV_correction.pl $id &gt; $&#123;id%%one-line&#125;4dtv;done #计算4dtv值，生成两列数据，gene对，4dtv</span><br><span class="line">for i in `ls |grep &quot;4dtv&quot;`;do awk &#x27;NR&gt;1&#123;print $1&quot;\t&quot;$3&#125;&#x27; $i &gt;&gt;../all.4dtv;done #合并4dtv值到all.4dtv</span><br><span class="line">cd ..</span><br><span class="line">join all.kaks all.4dtv |sed &quot;s/ /\t/g&quot; |awk &#x27;$3 != &quot;NA&quot; &#123;print $0&#125;&#x27; |sed &#x27;1i\genepair\tKa\tKs\tKa/Ks\t4dtv_corrected&#x27; &gt;all.results #以gene对为基准，join合并kaks和4dtv值到一个文件，然后过滤Ks值为NA的无效数据，添加标题行。</span><br><span class="line"></span><br><span class="line">rm all.kaks* all.4dtv* #删除中间文件</span><br></pre></td></tr></table></figure>
-
-<h2 id="3-6-ggplot2画密度分布图和峰值"><a href="#3-6-ggplot2画密度分布图和峰值" class="headerlink" title="3.6. ggplot2画密度分布图和峰值"></a>3.6. ggplot2画密度分布图和峰值</h2><h3 id="3-6-1-ggplot2画密度分布图"><a href="#3-6-1-ggplot2画密度分布图" class="headerlink" title="3.6.1. ggplot2画密度分布图"></a>3.6.1. ggplot2画密度分布图</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">library<span class="punctuation">(</span>ggplot2<span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>ggpmisc<span class="punctuation">)</span></span><br><span class="line">data <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;all.results&quot;</span><span class="punctuation">,</span>header<span class="operator">=</span><span class="built_in">T</span><span class="punctuation">)</span></span><br><span class="line">p <span class="operator">&lt;-</span> ggplot<span class="punctuation">(</span>data<span class="punctuation">,</span> aes<span class="punctuation">(</span>Ks<span class="punctuation">)</span><span class="punctuation">)</span> <span class="operator">+</span> geom_density<span class="punctuation">(</span>size<span class="operator">=</span><span class="number">1</span><span class="punctuation">,</span>color<span class="operator">=</span><span class="string">&quot;black&quot;</span><span class="punctuation">)</span><span class="operator">+</span>xlab<span class="punctuation">(</span><span class="string">&quot;Synonymous substitution rate(Ks)&quot;</span><span class="punctuation">)</span><span class="operator">+</span>ylab<span class="punctuation">(</span><span class="string">&quot;Percent of Total Paralogs&quot;</span><span class="punctuation">)</span><span class="operator">+</span>theme_classic<span class="punctuation">(</span><span class="punctuation">)</span><span class="operator">+</span>scale_y_continuous<span class="punctuation">(</span>breaks<span class="operator">=</span>seq<span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span> <span class="number">2.5</span><span class="punctuation">,</span> <span class="number">0.2</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="operator">+</span>xlim<span class="punctuation">(</span><span class="number">0</span><span class="punctuation">,</span><span class="number">3</span><span class="punctuation">)</span></span><br><span class="line">ggsave<span class="punctuation">(</span>file<span class="operator">=</span><span class="string">&quot;Ks.pdf&quot;</span><span class="punctuation">,</span>plot<span class="operator">=</span>p<span class="punctuation">,</span>width<span class="operator">=</span><span class="number">10</span><span class="punctuation">,</span>height<span class="operator">=</span><span class="number">5</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<h3 id="3-6-2-ggplot2峰值标定"><a href="#3-6-2-ggplot2峰值标定" class="headerlink" title="3.6.2. ggplot2峰值标定"></a>3.6.2. ggplot2峰值标定</h3><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">pb <span class="operator">&lt;-</span> ggplot_build<span class="punctuation">(</span>p<span class="punctuation">)</span></span><br><span class="line">pic <span class="operator">&lt;-</span> p <span class="operator">+</span> stat_peaks<span class="punctuation">(</span>data <span class="operator">=</span> pb<span class="punctuation">[[</span><span class="string">&#x27;data&#x27;</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">[[</span><span class="number">1</span><span class="punctuation">]</span><span class="punctuation">]</span><span class="punctuation">,</span> aes<span class="punctuation">(</span>x<span class="operator">=</span>x<span class="punctuation">,</span> y<span class="operator">=</span>density<span class="punctuation">)</span><span class="punctuation">,</span> geom<span class="operator">=</span> <span class="string">&#x27;text&#x27;</span><span class="punctuation">,</span> color<span class="operator">=</span><span class="string">&quot;red&quot;</span><span class="punctuation">,</span> <span class="punctuation">,</span>vjust<span class="operator">=</span><span class="operator">-</span><span class="number">0.5</span><span class="punctuation">)</span></span><br><span class="line">ggsave<span class="punctuation">(</span>file<span class="operator">=</span><span class="string">&quot;Ks_peaks.pdf&quot;</span><span class="punctuation">,</span>plot<span class="operator">=</span>pic<span class="punctuation">,</span>width<span class="operator">=</span><span class="number">10</span><span class="punctuation">,</span>height<span class="operator">=</span><span class="number">5</span><span class="punctuation">)</span></span><br></pre></td></tr></table></figure>
-
-<p>在密度分布图里得到红色标记的峰值。</p>
-<h3 id="3-6-3-多个密度图"><a href="#3-6-3-多个密度图" class="headerlink" title="3.6.3. 多个密度图"></a>3.6.3. 多个密度图</h3><p>同时展示多组Ks数据分布，可以把数据合并，添加一列作为分类标签，通过colour颜色参数指定这个分类标签列，则可以实现一张图上展示多个密度图。</p>
-<p><code>ggplot(data)+geom_density(aes(x=V3,colour=V5),adjust=2)+xlim(0,0.5)+theme_classic()</code> #指定第三列V3为数据，第五列V5为分类依据并赋予不同颜色。</p>
-<h3 id="3-6-4-通过密度分布图判断WGD"><a href="#3-6-4-通过密度分布图判断WGD" class="headerlink" title="3.6.4. 通过密度分布图判断WGD"></a>3.6.4. 通过密度分布图判断WGD</h3><p>一般认为，Ks密度分布图如果有明显的峰，一个峰代表对应一次WGD事件。<br>通过峰的数量和对应的Ks大小可以判断WGD事件的次数和发生时间。</p>
-<h2 id="3-7-WGD发生的时间"><a href="#3-7-WGD发生的时间" class="headerlink" title="3.7. WGD发生的时间"></a>3.7. WGD发生的时间</h2><p>通过Ks的密度分布图鉴定得到WGD事件发生的证据之后，有多种方法可以估算WGD发生的时间。</p>
-<h3 id="3-7-1-通过Ks-x3D-2μT计算时间"><a href="#3-7-1-通过Ks-x3D-2μT计算时间" class="headerlink" title="3.7.1. 通过Ks&#x3D;2μT计算时间"></a>3.7.1. 通过Ks&#x3D;2μT计算时间</h3><p>依据公式Ks&#x3D;2μT来计算时间T，依赖准确的进化速率μ，μ通常引用近缘类群的已有权威研究。</p>
-<p>进化速率μ：</p>
+<h3 id="3-1-4-绘制热图"><a href="#3-1-4-绘制热图" class="headerlink" title="3.1.4. 绘制热图"></a>3.1.4. 绘制热图</h3><p>热图绘制可以参考博客<a href="https://yanzhongsino.github.io/2022/11/06/R_plot_heatmap">https://yanzhongsino.github.io/2022/11/06/R_plot_heatmap</a></p>
 <ul>
-<li>蕨类植物的同义突变率：4.79e-9 subst.&#x2F;syn. site&#x2F;year :the first estimate of fern nuclear genome evolutionary rates with polypodiaceous nuclear genomes (Barker 2009)</li>
-<li>水稻：2.5e-8 subst.&#x2F;syn. site&#x2F;year</li>
-</ul>
-<h3 id="3-7-2-通过进化树估算时间"><a href="#3-7-2-通过进化树估算时间" class="headerlink" title="3.7.2. 通过进化树估算时间"></a>3.7.2. 通过进化树估算时间</h3><ul>
-<li>用代表WGD的Ks值附近的同线性基因对制作两套样品的单倍型数据，然后选取一到多个近缘种的蛋白序列与其中一套单倍型做直系同源基因分析，用找到的直系同源单拷贝基因建树（把两套单倍型数据当作两个物种来建树），通过化石或者二次标定的方法用paml的mcmctree模块计算两套单倍型的分化时间，即为WGD发生的时间。</li>
-<li>如果物种分化时间的尺度太大而不能准确估算WGD时间，可以先用大尺度的系统发育树做近缘种和目标物种的分化时间的估算，然后在做WGD时间估算时只用一个近缘种加上近缘种与目标物种的分化时间标定来获得更准确的WGD时间估计结果。</li>
+<li>如果只有一次WGD的TF保留结果，可以直接根据Rvalue判断哪些TF家族保留率高。</li>
+<li>如果有多次WGD的TF保留结果，或者做了多个物种的TF数据库保留结果，可以绘制热图相互比较。</li>
 </ul>
-<h4 id="3-7-2-1-获取sample发生WGD的两套基因"><a href="#3-7-2-1-获取sample发生WGD的两套基因" class="headerlink" title="3.7.2.1. 获取sample发生WGD的两套基因"></a>3.7.2.1. 获取sample发生WGD的两套基因</h4><ol>
-<li>前面分析中计算Ks的分布峰值假设在<strong>0.05</strong>，取Ks值在0.05正负25%的范围，即[0.0375-0.0625]之间的基因对。<br><code>cat all.results |awk &#39;$3&gt;0.0375 &amp;&amp; $3&gt;0.0625 &#123;print $1&#125; |sed &quot;s/-/\t/g&quot; &gt;wgd.homologs</code></li>
-<li>基因对中若有一个基因对多个基因的情况，则选取Ks最接近0.05的那对。</li>
-<li>获取两套基因list。<br><code>cut -f1 wgd.homologs &gt;wgd_H1.homologs</code>;<br><code>cut -f2 wgd.homologs &gt;wgd_H2.homologs</code></li>
-</ol>
-<h4 id="3-7-2-2-选近缘种和分化时间"><a href="#3-7-2-2-选近缘种和分化时间" class="headerlink" title="3.7.2.2. 选近缘种和分化时间"></a>3.7.2.2. 选近缘种和分化时间</h4><ol>
-<li>参考近年发表的权威组学系统发育文章选取近缘种和物种分化时间数据。</li>
-<li>这里假设选取了两个近缘物种sampleA和sampleB。sample发生WGD形成的两套单倍型H1和H2，目标物种sample与近缘种sampleA的分化时间已知为88Ma。</li>
-<li>确定物种树拓扑结构：<code>(((sampleH1,sampleH2),sampleA)&#39;&lt;0.88&gt;0.88),sampleB;</code>，以sampleB为外类群。</li>
-</ol>
-<h4 id="3-7-2-3-orthofinder找直系同源基因"><a href="#3-7-2-3-orthofinder找直系同源基因" class="headerlink" title="3.7.2.3. orthofinder找直系同源基因"></a>3.7.2.3. orthofinder找直系同源基因</h4><ol>
-<li>根据基因对提取蛋白序列，任意用一个（这里用wgd_H1.homologs)提取蛋白序列：<code>seqkit grep -n -f wgd_H1.homologs sample.pep &gt; wgd_H1.pep</code></li>
-<li>与其他两个近缘种的蛋白序列一起，用orthofinder找直系同源基因。</li>
+<p>用R包pheatmap绘制热图，简单快捷。(notes: 画热图这里的代码还需根据数据格式调整)</p>
+<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">df<span class="operator">&lt;-</span>read.table<span class="punctuation">(</span><span class="string">&quot;tf_rvalue.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span> <span class="string">&quot; &quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span>row.names <span class="operator">=</span> <span class="number">1</span><span class="punctuation">)</span></span><br><span class="line">df_row <span class="operator">&lt;-</span> hclust<span class="punctuation">(</span>dist<span class="punctuation">(</span>df<span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#对行聚类</span></span><br><span class="line">df <span class="operator">&lt;-</span> df<span class="punctuation">[</span>df_row<span class="operator">$</span>order<span class="punctuation">,</span><span class="punctuation">]</span> <span class="comment">#按行聚类结果排序</span></span><br><span class="line">df_column <span class="operator">&lt;-</span> hclust<span class="punctuation">(</span>dist<span class="punctuation">(</span>t<span class="punctuation">(</span>df<span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#对列聚类</span></span><br><span class="line">df <span class="operator">&lt;-</span> df<span class="punctuation">[</span><span class="punctuation">,</span>df_column<span class="operator">$</span>order<span class="punctuation">]</span> <span class="comment">#按列聚类结果排序</span></span><br><span class="line"></span><br><span class="line">BiocManager<span class="operator">::</span>install<span class="punctuation">(</span><span class="string">&quot;pheatmap&quot;</span><span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>pheatmap<span class="punctuation">)</span></span><br><span class="line">pheatmap<span class="punctuation">(</span>df<span class="punctuation">,</span>color <span class="operator">=</span> colorRampPalette<span class="punctuation">(</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;lightgreen&quot;</span><span class="punctuation">,</span> <span class="string">&quot;yellow&quot;</span><span class="punctuation">,</span><span class="string">&quot;orange&quot;</span><span class="punctuation">,</span><span class="string">&quot;red&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">(</span><span class="number">20</span><span class="punctuation">)</span><span class="punctuation">,</span>legend_breaks <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1</span><span class="operator">:</span><span class="number">4</span><span class="punctuation">)</span><span class="punctuation">,</span> legend_labels <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;1.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;2.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;3.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;4.0&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> border_color<span class="operator">=</span><span class="string">&quot;white&quot;</span><span class="punctuation">,</span>treeheight_row <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> treeheight_col <span class="operator">=</span> <span class="number">8</span><span class="punctuation">,</span> display_numbers <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span> number_color <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span>main <span class="operator">=</span> <span class="string">&quot;TF heatmap&quot;</span><span class="punctuation">,</span>cellwidth <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> cellheight <span class="operator">=</span> <span class="number">10</span><span class="punctuation">)</span></span><br><span class="line"><span class="comment"># 其中color = colorRampPalette(c(&quot;lightgreen&quot;, &quot;yellow&quot;,&quot;orange&quot;,&quot;red&quot;))(20) #设置颜色渐变，值从低到高依次是浅绿色-黄色-橙色-红色，共20个颜色。</span></span><br></pre></td></tr></table></figure>
+
+<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
+<li>wiki:transcription factor: <a href="https://en.wikipedia.org/wiki/Transcription_factor">https://en.wikipedia.org/wiki/Transcription_factor</a></li>
+<li>PlantTFDB: <a href="http://planttfdb.gao-lab.org/index.php">http://planttfdb.gao-lab.org/index.php</a></li>
+<li>paper: <a href="https://www.sciencedirect.com/science/article/pii/S1674205219303594">https://www.sciencedirect.com/science/article/pii/S1674205219303594</a></li>
 </ol>
+<hr>
 <ul>
-<li>orthofinder使用方法：把所有物种的pep蛋白序列（两个近缘物种加上wgd_H1.pep）放进一个文件夹(directory)，注意蛋白序列中不能有.&#x2F;*等符号（常用来代表终止密码子）。</li>
-<li>运行<code>orthofinder -f directory -t 24</code> # -t指定线程</li>
-<li>结果文件的Orthogroups目录下，Orthogroups.txt包含所有直系同源基因，Orthogroups_SingleCopyOrthologues.txt包含单拷贝的直系同源基因。</li>
-<li>根据Orthogroups_SingleCopyOrthologues.txt把Orthogroups.txt里基因信息提取出来,<code>grep -f Orthogroups_SingleCopyOrthologues.txt Orthogroups.tsv &gt;OG.txt</code>。OG.txt文件内容是singlecopyorthologues列表，第一列orthogroupID，后面3列每套蛋白序列ID，共4列，这个文件的每行数据为一个homologs对应的每套数据的蛋白ID。</li>
-<li>合并wgd.homologs和OG.txt文件为OG.list：<code>join -1 1 -2 2 wgd.homologs OG.txt &gt; OG.list</code>。OG.list文件是在OG.txt基础上增加了wgd_H2.homologs的一套蛋白ID数据，共有四套蛋白ID数据。</li>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
 </ul>
-<h4 id="3-7-2-4-建单基因树"><a href="#3-7-2-4-建单基因树" class="headerlink" title="3.7.2.4. 建单基因树"></a>3.7.2.4. 建单基因树</h4><ol>
-<li>建树脚本<br>用下面的脚本singlegenetree.sh，提取每个homologs序列，并通过raxml-ng（raxmlHPC建氨基酸树只有bootstrap结果，没有bestTree）为每个homologs基因比对（mafft）和建树。<br>OG.list文件包含第一列ogID和后4列4套样本ID。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">singlegenetree.sh</span></span><br><span class="line">cat ./OG.list | while read line</span><br><span class="line">do</span><br><span class="line">        sample_id=$(echo $line |awk &#x27;&#123;print $1&#125;&#x27;)</span><br><span class="line">        sample_a=$(echo $line |awk &#x27;&#123;print $2&#125;&#x27;)</span><br><span class="line">        sample_b=$(echo $line |awk &#x27;&#123;print $3&#125;&#x27;)</span><br><span class="line">        sample_c=$(echo $line |awk &#x27;&#123;print $4&#125;&#x27;)        </span><br><span class="line">        sample_d=$(echo $line |awk &#x27;&#123;print $5&#125;&#x27;)</span><br><span class="line">        mkdir ./singlegenetree/$&#123;sample_id&#125;</span><br><span class="line">        seqkit grep -n $&#123;sample_a&#125; sample.pep &gt;./singlegenetree/$&#123;sample_id&#125;/H1_$&#123;sample_a&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_b&#125; sample.pep &gt;./singlegenetree/$&#123;sample_id&#125;/H2_$&#123;sample_b&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_c&#125; sampleA.pep &gt;./singlegenetree/$&#123;sample_id&#125;/$&#123;sample_c&#125;.pep</span><br><span class="line">        seqkit grep -n $&#123;sample_d&#125; sampleB.pep &gt;./singlegenetree/$&#123;sample_id&#125;/$&#123;sample_d&#125;.pep</span><br><span class="line">        cd ./singlegenetree/$&#123;sample_id&#125;/</span><br><span class="line">        cat H1_$&#123;sample_a&#125;.pep H2_$&#123;sample_b&#125;.pep $&#123;sample_c&#125;.pep $&#123;sample_d&#125;.pep &gt; $&#123;sample_id&#125;.pep</span><br><span class="line">        mafft $&#123;sample_id&#125;.pep &gt;$&#123;sample_id&#125;.mafft.pep</span><br><span class="line">        raxml-ng --all --msa $&#123;sample_id&#125;.mafft.pep --data-type AA --threads 8 --bs-trees 100 --model LG+G8+F --tree pars&#123;10&#125; --outgroup $&#123;sample_d&#125; --redo</span><br><span class="line">        sed -E &quot;s/:[0-9]\.[0-9]*//g&quot; $&#123;sample_id&#125;.mafft.pep.raxml.bestTree  &gt;besttree.$&#123;sample_id&#125; #删除不必要的枝长等信息</span><br><span class="line">        cd ../../</span><br><span class="line">done</span><br></pre></td></tr></table></figure>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>transcription factor</category>
+      </categories>
+      <tags>
+        <tag>WGD</tag>
+        <tag>transcription factor</tag>
+        <tag>PlantTFDB</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>结构变异分析软件：Assemblytics</title>
+    <url>/2022/08/02/bioinfo_variation_SV_Assemblytics/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=20707476&auto=1&height=32"></iframe></div>
 
-<ol start="2">
-<li>筛选homologs<br>判断生成的每个OG的树文件besttree.${sample_id}，只保留符合物种树拓扑结构<code>(((sampleH1,sampleH2),sampleA)),sampleB;</code>的基因，保存成OG.filtered。</li>
-</ol>
-<h4 id="3-7-2-5-paml估计分化时间"><a href="#3-7-2-5-paml估计分化时间" class="headerlink" title="3.7.2.5. paml估计分化时间"></a>3.7.2.5. paml估计分化时间</h4><p>此节参考博客<a href="https://yanzhongsino.github.io/2021/03/25/bioinfo_caculate.divergence.time/">估算系统树分歧时间</a>的paml部分。</p>
-<ol>
-<li>在OG.filtered最后一列添加每个OG的序列长度信息，保存成OG.filtered.list。</li>
-<li>准备输入文件sample.phy<br>上一步脚本提取的每个homologs的比对后的四套数据蛋白序列${sample_id}.mafft.pep，用脚本fa2phylip.sh换成phylip格式（用python的SeqIO库换的格式mcmctree识别不了）。<br>并合并提取的所有homologs序列。</li>
+<h1 id="1-Assemblytics简介"><a href="#1-Assemblytics简介" class="headerlink" title="1. Assemblytics简介"></a>1. Assemblytics简介</h1><ol>
+<li>Assemblytics在2016年发表在Bioinformatics上，用于检测检测基因组间的结构变异（Structural Variation，SV）。</li>
+<li>Assemblytics检测的结构变异主要包括：INDELs，tandem和repeat的expansion和contraction；不包含倒位inversion和易位translocation的检测。</li>
+<li>Assemblytics建议使用基因组组装的contigs而非scaffolds，避免Ns对统计结构变异产生影响（假阳性）。</li>
 </ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">fa2phylip.sh</span></span><br><span class="line">cat ./OG.filtered.list | while read line</span><br><span class="line">do</span><br><span class="line">        sample_id=$(echo $line |awk &#x27;&#123;print $1&#125;&#x27;) #获取OG.ID</span><br><span class="line">        sample_a=$(echo $line |awk &#x27;&#123;print $6&#125;&#x27;) #获取序列长度</span><br><span class="line">        sed &quot;s/_.*//g&quot; ../singlegenetree/$&#123;sample_id&#125;/$&#123;sample_id&#125;.mafft.pep|seqkit seq -w 0|sed -E &quot;:a;N;s/\n/ /g;ta&quot; |sed &quot;s/ &gt;/\n/g&quot; |sed &quot;s/&gt;//g&quot;|sed &quot;s/ /  /g&quot;|sed &quot;1i\5  $&#123;sample_a&#125;&quot; |sed &#x27;1i\ &#x27; &gt; ./phy/$&#123;sample_id&#125;.phy #把上一步获取的$&#123;sample_id&#125;.mafft.pep改为phylip格式，并在首行添加空格行（为了合并后每个OG用空行隔开）</span><br><span class="line"></span><br><span class="line">done</span><br><span class="line">cat ./phy/*phy &gt;sample.phy #合并所有phy文件为一个sample.phy文件，假设共有261个子phy文件被合并</span><br></pre></td></tr></table></figure>
+<img src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bioinformatics/32/19/10.1093_bioinformatics_btw369/5/btw369f1p.gif?Expires=1662466735&Signature=XxOCD8I9su8WHNrORriUZmNxiCU219tKxNJr~iM-QEGM1pWydiON3YFnn~znOY~ibASOkh6T8rTksGBcPKFI-3ymeYLx2FDqR29phWp90a4of40ouDPqKsK5Crw5PVK5qNbbbV67n5hKpsaxUOtim-jDp5kyM8lZmPzQGm6qTRE5nclS6Lis-mQw96ZQFeguCbTS1k4Re7ULtzaMO1uLOEWG-401s~2JbBG07HI7cmt2UAP3zLV~n8CV8ISpi~W8SqvBaF7EjEqpPITGxSQQJCTP8EOg1erhp7Knf6jgTuwvnGShz4-cNNH2oRN3~2kw6aug3~B0qqPmhgFkW4JYGQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" width=100% title="Assemblytics" alt="Assemblytics" align=center/>
 
-<ol start="3">
-<li>准备输入文件sample.tre</li>
+<p><strong><p align="center">Figure 1. variant class and web results interface Assemblytics</strong><br>图片来源： <a href="https://academic.oup.com/bioinformatics/article/32/19/3021/2196631">Assemblytics paper</a></p></p>
+<h1 id="2-Assemblytics使用"><a href="#2-Assemblytics使用" class="headerlink" title="2. Assemblytics使用"></a>2. Assemblytics使用</h1><p>Assemblytics的使用很简单，网站也说得清楚明了。</p>
+<ol>
+<li>先用MUMmer把reference genome和query genome比对，得到共线性结果。</li>
 </ol>
+<p><code>nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT</code></p>
 <ul>
-<li>sample.tre包含两行，第一行表述树中有5个样本，共计1个树，两个数值之间用空格分割；<br>第二行则是Newick格式树信息。</li>
-<li>其中第二行Newick格式树包含有校准点信息，校准点信息一般指95%HPD（Highest Posterior Density）对应的置信区间，这里用的校准点时间只有一个点，所以写成’&lt;0.88&gt;0.88’形式；校准点单位是100MYA（软件说明文档中使用该单位，也推荐使用该单位，若使用其它单位，后续配置文件中的相关参数也需要对应修改）。</li>
-<li>此外，Newick格式的树尾部一定要有分号，没有的话程序可能不能正常运行。</li>
-<li>sample.tre内容为：</li>
+<li>-l 100：最小匹配长度设置成100</li>
+<li>-c 500：最小匹配序列簇的匹配长度为500</li>
+<li>-prefix OUT：输出结果文件前缀为OUT</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">4 1</span><br><span class="line">(((sampleH1,sampleH2),sampleA)&#x27;&lt;0.88&gt;0.88),sampleB;</span><br></pre></td></tr></table></figure>
-
-<ol start="4">
-<li>准备配置文件mcmctree3.ctl<br>ndata &#x3D; 261表示有261个数据，与sample.phy包含的子phy数量对应；seqtype &#x3D; 2表示这里用的氨基酸序列；model &#x3D; 0暂时选用0，后面要改；</li>
-</ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">          seed = -1</span><br><span class="line">       seqfile = sample.phy</span><br><span class="line">      treefile = sample.tre</span><br><span class="line">       outfile = mcmc.out</span><br><span class="line">      mcmcfile = mcmc.txt</span><br><span class="line"></span><br><span class="line">       seqtype = 2    *0: DNA data; 1: codon data; 2: AAs data</span><br><span class="line">        ndata = 261</span><br><span class="line">       usedata = 3    * 0: no data; 1:seq like; 2:normal approximation</span><br><span class="line">         clock = 3    * 1: global clock; 2: independent rates; 3: correlated rates</span><br><span class="line">*      TipDate = 1 100</span><br><span class="line">        RootAge = &#x27;&lt;10&#x27;  * constraint on root age, used if no fossil for root.</span><br><span class="line"></span><br><span class="line">         model = 0    * 0:JC69, 1:K80, 2:F81, 3:F84, 4:HKY85</span><br><span class="line">         alpha = 0.5   * alpha for gamma rates at sites</span><br><span class="line">         ncatG = 4    * No. categories in discrete gamma</span><br><span class="line"></span><br><span class="line">     cleandata = 0    * remove sites with ambiguity data (1:yes, 0:no)?</span><br><span class="line"></span><br><span class="line">       BDparas = 1 1 0.1   * birth, death, sampling</span><br><span class="line">   kappa_gamma = 6 2      * gamma prior for kappa</span><br><span class="line">   alpha_gamma = 1 1      * gamma prior for alpha</span><br><span class="line"></span><br><span class="line">   rgene_gamma = 2 20 1   * gamma prior for rate for genes</span><br><span class="line">  sigma2_gamma = 1 10 1    * gamma prior for sigma^2     (for clock=2 or 3)</span><br><span class="line"></span><br><span class="line">      finetune = 1: .1 .1 .1 .1 .1 .1  * times, rates, mixing, paras, RateParas</span><br><span class="line"></span><br><span class="line">         print = 1</span><br><span class="line">        burnin = 1000000</span><br><span class="line">      sampfreq = 10</span><br><span class="line">       nsample = 500000</span><br><span class="line"></span><br><span class="line">*** Note: Make your window wider (100 columns) when running this program.</span><br></pre></td></tr></table></figure>
-
-<ol start="5">
-<li>运行<code>mcmctree mcmctree3.ctl</code></li>
+<ol start="2">
+<li>把delta结果文件压缩，压缩后的结果OUT.delta.gz上传到Assemblytics网站。</li>
 </ol>
-<ul>
-<li>此步骤的目的是为了获取in.BV文件；</li>
-<li>用命令<code>mcmctree mcmctree3.ctl</code>运行起来后，ctrl+C多次中断运行（共261个序列，会依次调用261次codeml，中断后会自动进入下一次调用，目的是完成261次调用生成261个序列的临时文件）；由于单个序列短，很快会完成分析，也可以不ctrl+C中断，等待完成，只要生成261套tmp*文件即可。</li>
-<li>删除rst2文件和mcmc.out,mcmc.txt,out.BV文件；把paml软件下的wag.dat文件复制到当前目录下<code>cp ~/software/anaconda3/dat/wag.dat ./</code>；</li>
-<li>用命令<code>sed -i -e &quot;s/model = 0/model =2\naaRatefile = wag.dat/&quot; -e &quot;s/method = 0/method = 1/&quot; tmp*.ctl</code>把所有tmp*.ctl文件的model修改成model &#x3D; 2，method修改成method &#x3D; 1,并添加一行aaRatefile &#x3D; wag.dat；</li>
-<li>用命令<code>for i in $(ls tmp*.ctl);do codeml $&#123;i&#125; ; mv rst2 $&#123;i&#125;.rst2; done</code>调用codeml进行261次分析（每次codeml都生成rst2文件，所以每次生成后把rst2改名，否则rst2被覆盖）；</li>
-<li>然后<code>cat *rst2 &gt;in.BV</code>合并所有rst2文件到in.BV文件；</li>
-</ul>
-<ol start="6">
-<li>运行<code>mcmctree mcmctree2.ctl</code></li>
+<p><code>gzip OUT.delta</code></p>
+<ol start="3">
+<li>填写参数，提交即可在线运行和得到结果。参数包括：</li>
 </ol>
 <ul>
-<li>新建目录并复制四个文件sample.phy,sample.tre,mcmctree3.ctl,in.BV到新目录<code>mkdir mcmctree2 &amp;&amp; cd mcmctree2 &amp;&amp; cp ../Ane.* ./ &amp;&amp; cp ../mcmctree3.ctl ./ &amp;&amp; cp ../in.BV ./</code>;</li>
-<li>把mcmctree3.ctl文件内的usedata &#x3D; 3改为usedata &#x3D; 2，并重命名成mcmctree2.ctl；</li>
-<li>然后运行<code>mcmctree mcmctree2.ctl</code>；</li>
-<li>等待两三天获取结果，FigTree.tre文件包含了所有节点的95%HPD的时间，是nexus格式。</li>
-<li>根据FigTree.tre结果：<code>((sampleA: 0.408365, (sampleH1: 0.107333, sampleH2: 0.107333) [&amp;95%=&#123;0.0749432, 0.126365&#125;]: 0.301032) [&amp;95%=&#123;0.285365, 0.478163&#125;]: 0.362000, sampleB: 0.770366) [&amp;95%=&#123;0.53864, 0.90012&#125;];</code>的sampleH1和sampleH2分化的时间，可以看出WGD发生的时间在**10.7Ma[7.5-12.6]**之间。</li>
+<li>Description：物种名或样品名</li>
+<li>Unique Sequence length required：需要的独特序列长度，代表一个决定用于call variants的序列是否独特的一个锚定（anchor），用于代替读取比对（read alignment）的映射质量的过滤，默认是10000。</li>
+<li>Maximum variant size：最大变异的大小，默认是10000。</li>
+<li>Minimum variant size：最小变异的大小，默认是50。</li>
 </ul>
+<h1 id="3-结果"><a href="#3-结果" class="headerlink" title="3. 结果"></a>3. 结果</h1><p>运行结果包括可交互的可视化图的界面和可下载的压缩包。</p>
+<p>主要结果在文件<strong>Assemblytics_structural_variants.summary</strong>中，文件内容示例：</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Insertion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        835          91429</span><br><span class="line">     500-10,000 bp:        158         468629</span><br><span class="line">             Total:        993         560058</span><br><span class="line"></span><br><span class="line">Deletion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        779          85083</span><br><span class="line">     500-10,000 bp:        140         289652</span><br><span class="line">             Total:        919         374735</span><br><span class="line"></span><br><span class="line">Tandem_expansion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:         46           8957</span><br><span class="line">     500-10,000 bp:        117         444964</span><br><span class="line">             Total:        163         453921</span><br><span class="line"></span><br><span class="line">Tandem_contraction</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:          2            379</span><br><span class="line">     500-10,000 bp:          0              0</span><br><span class="line">             Total:          2            379</span><br><span class="line"></span><br><span class="line">Repeat_expansion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        373          80936</span><br><span class="line">     500-10,000 bp:        968        3666087</span><br><span class="line">             Total:       1341        3747023</span><br><span class="line"></span><br><span class="line">Repeat_contraction</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        411          92021</span><br><span class="line">     500-10,000 bp:        893        3378919</span><br><span class="line">             Total:       1304        3470940</span><br><span class="line"></span><br><span class="line">Total number of all variants: 4,722</span><br><span class="line">Total bases affected by all variants: 8.61 Mbp</span><br><span class="line">Total number of structural variants: 4,722</span><br><span class="line">Total bases affected by structural variants: 8.61 Mbp</span><br></pre></td></tr></table></figure>
+
 <h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326336/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326336/</a></li>
-<li><a href="https://github.com/wyp1125/MCScanX">https://github.com/wyp1125/MCScanX</a></li>
-<li><a href="https://its201.com/article/sinat_41621566/113359074">https://its201.com/article/sinat_41621566/113359074</a></li>
+<li><a href="http://assemblytics.com/">http://assemblytics.com/</a></li>
+<li><a href="https://github.com/marianattestad/assemblytics">https://github.com/marianattestad/assemblytics</a></li>
+<li>paper: <a href="https://academic.oup.com/bioinformatics/article/32/19/3021/2196631">https://academic.oup.com/bioinformatics/article/32/19/3021/2196631</a></li>
 </ol>
 <hr>
 <ul>
@@ -7746,287 +7760,93 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
         <category>bioinfo</category>
-        <category>synteny</category>
+        <category>variation</category>
+        <category>structural variation</category>
       </categories>
       <tags>
         <tag>biosoft</tag>
-        <tag>ggplot2</tag>
-        <tag>Ks</tag>
-        <tag>ParaAT</tag>
-        <tag>KaKs_Calculator</tag>
-        <tag>WGD</tag>
-        <tag>divergence time</tag>
-        <tag>paml</tag>
-        <tag>synteny</tag>
-        <tag>colinearity</tag>
-        <tag>MCScanX</tag>
+        <tag>structural variation</tag>
+        <tag>SV</tag>
+        <tag>insertion</tag>
+        <tag>deletion</tag>
+        <tag>Indel</tag>
+        <tag>tandem expansion</tag>
+        <tag>tandem contraction</tag>
+        <tag>repeat expansion</tag>
+        <tag>repeat contraction</tag>
+        <tag>Assemblytics</tag>
       </tags>
   </entry>
   <entry>
-    <title>基因组间的同线性分析 —— MCscan</title>
-    <url>/2021/11/05/bioinfo_synteny_MCscan/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5250805&auto=1&height=32"></iframe></div>
+    <title>分析基因组共线性、计算Ks和鉴定WGD —— WGDI</title>
+    <url>/2021/09/11/bioinfo_synteny_WGDI/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
 
-<h1 id="1-MCscan背景"><a href="#1-MCscan背景" class="headerlink" title="1. MCscan背景"></a>1. MCscan背景</h1><h2 id="1-1-jcvi介绍"><a href="#1-1-jcvi介绍" class="headerlink" title="1.1. jcvi介绍"></a>1.1. jcvi介绍</h2><p><a href="https://github.com/tanghaibao/jcvi">jcvi</a>是美国佐治亚大学唐海宝开发的一个生物信息学python库。主要有几个常见pipeline用到了jcvi库，一个是做基因组scaffoldding的<a href="https://github.com/tanghaibao/jcvi/wiki/ALLMAPS">ALLMAPS</a>，另一个是做基因组同线性分析的<a href="https://github.com/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan</a>，以及提取植物表型数据的<a href="https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS">GRABSEEDS</a>。</p>
-<h2 id="1-2-MCscan介绍"><a href="#1-2-MCscan介绍" class="headerlink" title="1.2. MCscan介绍"></a>1.2. MCscan介绍</h2><p>用MCscan pipeline做两个基因组间的同线性分析，包括成对同线性(pairwise synteny)分析，宏观同线性(macrosynteny)分析，微观同线性(microsynteny)分析和结果的可视化(visualization)。</p>
-<p>成对同线性分析主要是制作同线性点图(dotplot),同线性深度直方图；宏观同线性分析主要是制作同线性染色体图。其中同线性深度直方图，同线性染色体图需要用到同线性点图的结果文件作为输入。</p>
-<p>微观同线性分析等后面用到再补坑。</p>
-<h1 id="2-MCscan使用"><a href="#2-MCscan使用" class="headerlink" title="2. MCscan使用"></a>2. MCscan使用</h1><h2 id="2-1-安装jcvi"><a href="#2-1-安装jcvi" class="headerlink" title="2.1. 安装jcvi"></a>2.1. 安装jcvi</h2><h3 id="2-1-1-依赖"><a href="#2-1-1-依赖" class="headerlink" title="2.1.1. 依赖"></a>2.1.1. 依赖</h3><p>jcvi依赖包括biopython,numpy,matplotlib。</p>
-<p>MCscan依赖包括LASTAL。</p>
-<p>LASTAL可以用<code>conda install -c bioconda last</code>安装，但我用conda安装的lastal和lastdb不能多线程运行，所以在运行<code>python -m jcvi.compara.catalog ortholog --cpu=1</code>时用参数<code>--cpu=1</code>来限制单线程运行。</p>
-<h3 id="2-1-2-安装jcvi"><a href="#2-1-2-安装jcvi" class="headerlink" title="2.1.2. 安装jcvi"></a>2.1.2. 安装jcvi</h3><p>任意选一种安装方式</p>
+<h1 id="1-WGDI介绍"><a href="#1-WGDI介绍" class="headerlink" title="1. WGDI介绍"></a>1. WGDI介绍</h1><p>WGDI (Whole-Genome Duplication Integrated analysis)是python写的命令行工具，用于多倍化和跨物种基因组比对的综合分析，包括鉴定基因组内共线性区块，Ks计算，全基因组复制事件(WGD, whole genome duplication)的数量和时间的鉴定。</p>
+<p>WGDI主要的三个功能是：</p>
+<ul>
+<li>多倍体推断</li>
+<li>基因组同源性的层次推断</li>
+<li>祖先染色体核型分析</li>
+</ul>
+<h1 id="2-WGDI安装"><a href="#2-WGDI安装" class="headerlink" title="2. WGDI安装"></a>2. WGDI安装</h1><p>WGDI的依赖：PAML,MAFFT,MUSCLE,PAL2NAL,IQTREE。</p>
+<p>安装有三种方法:</p>
 <ol>
-<li><code>pip install jcvi</code></li>
-<li><code>pip install git+git://github.com/tanghaibao/jcvi.git</code></li>
-<li><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">cd jcvi</span><br><span class="line">git clone git://github.com/tanghaibao/jcvi.git</span><br><span class="line">pip install -e .</span><br></pre></td></tr></table></figure></li>
-</ol>
-<h2 id="2-2-准备输入文件"><a href="#2-2-准备输入文件" class="headerlink" title="2.2. 准备输入文件"></a>2.2. 准备输入文件</h2><p>输入文件包括两个基因组的CDS&#x2F;氨基酸序列文件（fa格式），两个基因组的注释坐标文件（bed格式）。</p>
-<h3 id="2-2-1-jcvi支持直接从phytozome下载数据"><a href="#2-2-1-jcvi支持直接从phytozome下载数据" class="headerlink" title="2.2.1. jcvi支持直接从phytozome下载数据"></a>2.2.1. jcvi支持直接从phytozome下载数据</h3><ol>
-<li><p>连接phytozome（需要登录，提前注册账号）<br><code>python -m jcvi.apps.fetch phytozome</code> 输入账户登录信息后，出现可以下载的基因组list。</p>
-</li>
-<li><p>下载CDS序列和注释文件(gff格式)，例如下载这两个物种:grape and peach<br><code>python -m jcvi.apps.fetch phytozome Vvinifera,Ppersica</code></p>
-</li>
-<li><p>转换GFF为BED格式<br><code>python -m jcvi.formats.gff bed --type=mRNA --key=Name  --primary_only Vvinifera_145_gene.gff3.gz -o grape.bed</code></p>
-</li>
-</ol>
-<p><code>python -m jcvi.formats.gff bed --type=mRNA --key=Name  --primary_only Ppersica_139_gene.gff3.gz -o peach.bed</code></p>
-<p>其中<code>--primary_only</code>是用于去除多个转录本的，只保留一个。（原文是多个isoforms）一般不去也不影响后续步骤，加上更好吧。</p>
-<ol start="4">
-<li>整理一下，获得CDS文件<br><code>python -m jcvi.formats.fasta format Vvinifera_145_Genoscope.12X.cds.fa.gz grape.cds</code></li>
+<li>conda<br><code>conda create -c bioconda -c conda-forge -n wgdi wgdi</code> 建议新建一个环境</li>
+<li>pip<br><code>pip install wgdi</code></li>
+<li>github<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/SunPengChuan/wgdi.git</span><br><span class="line">cd wgdi</span><br><span class="line">python setup.py install</span><br></pre></td></tr></table></figure></li>
 </ol>
-<p><code>python -m jcvi.formats.fasta format Ppersica_298_v2.1.cds.fa.gz peach.cds</code></p>
-<h3 id="2-2-2-自己的数据"><a href="#2-2-2-自己的数据" class="headerlink" title="2.2.2. 自己的数据"></a>2.2.2. 自己的数据</h3><ol>
-<li>gff2bed<br><code>python -m jcvi.formats.gff bed --type=mRNA --key=ID --primary_only sampleA.gff3.gz &gt; sampleA.bed</code></li>
+<h1 id="3-WGDI进行共线性分析"><a href="#3-WGDI进行共线性分析" class="headerlink" title="3. WGDI进行共线性分析"></a>3. WGDI进行共线性分析</h1><h2 id="3-1-初始输入文件"><a href="#3-1-初始输入文件" class="headerlink" title="3.1. 初始输入文件"></a>3.1. 初始输入文件</h2><h3 id="3-1-1-输入文件"><a href="#3-1-1-输入文件" class="headerlink" title="3.1.1. 输入文件"></a>3.1.1. 输入文件</h3><p>WGDI需要三个输入文件，分别是基因的位置信息文件sample.gff，染色体长度信息文件sample.len和BLAST的输出文件sample.blast.txt，要求格式如下：</p>
+<ol>
+<li>input.gff</li>
 </ol>
-<p><code>python -m jcvi.formats.gff bed --type=mRNA --key=ID --primary_only sampleB.gff3.gz &gt; sampleB.bed</code></p>
-<p>其中<code>--primary_only</code>是用于去除多个转录本的，只保留一个。（原文是多个isoforms）一般不去也不影响后续步骤，加上更好吧。</p>
+<ul>
+<li>一共七列，以tab分隔，分别为chr(chromosome number)，geneid(gene name)，start，end，direction(strand+&#x2F;-)，order(order of each chromosome,starting from 1, 每个chr内部从1开始的顺序），original(original id and not read,旧的id，不会读入)。</li>
+<li>和典型的GFF格式不一样</li>
+</ul>
 <ol start="2">
-<li><p>去重，会生成sampleA.uniq.bed和sampleB.uniq.bed，然后重命名</p>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">python -m jcvi.formats.bed uniq sampleA.bed</span><br><span class="line">mv sampleA.uniq.bed sampleA.bed</span><br><span class="line">python -m jcvi.formats.bed uniq sampleB.bed</span><br><span class="line">mv sampleB.uniq.bed sampleB.bed</span><br></pre></td></tr></table></figure>
-</li>
-<li><p>提取cds和pep</p>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">seqkit grep -f &lt;(cut -f 4 sampleA.uniq.bed ) sampleA.cdna.all.fa.gz | seqkit seq -i &gt; sampleA.cds</span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleA.uniq.bed ) sampleB.pep.all.fa.gz | seqkit seq -i &gt; sampleA.pep </span><br><span class="line"></span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleB.uniq.bed )  sampeB.cdna.fa.gz | seqkit seq -i  &gt; sampleB.cds</span><br><span class="line">seqkit grep -f &lt;(cut -f 4 sampleB.uniq.bed ) sampleB.pep.fa.gz | seqkit seq -i  &gt; sampleB.pep</span><br></pre></td></tr></table></figure></li>
-</ol>
-<h2 id="2-3-同线性点图"><a href="#2-3-同线性点图" class="headerlink" title="2.3. 同线性点图"></a>2.3. 同线性点图</h2><h3 id="2-3-1-CDS的同线性点图dotplot"><a href="#2-3-1-CDS的同线性点图dotplot" class="headerlink" title="2.3.1. CDS的同线性点图dotplot"></a>2.3.1. CDS的同线性点图dotplot</h3><ol>
-<li>确保<code>sampleA.cds</code>,<code>sampleA.bed</code>,<code>sampleB.cds</code>,<code>sampleB.bed</code>四个文件在同一目录下。</li>
-<li>运行<code>python -m jcvi.compara.catalog ortholog --no_strip_names --cpu=1 sampleA sampleB</code>制作同线性点图。</li>
+<li>input.len</li>
 </ol>
-<p><code>--cpu=1</code>是因为conda安装的lastal不支持多线程，所以用单线程。</p>
+<ul>
+<li>一共三列，以tab分隔，染色体长度信息和染色体上的基因个数，chr(chromosome number), chr_length(染色体长度bp), chr_gene_number(注释到单个染色体的基因数量)。</li>
+<li>后面分析作图，比如点图，图中显示的染色体顺序是依据这个文件的顺序来决定的。所以可以通过修改这个文件的染色体顺序来修改图的显示。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	35874591	5368</span><br><span class="line">LG02	29224816	3939</span><br><span class="line">LG03	27760256	3620</span><br></pre></td></tr></table></figure>
+
 <ol start="3">
-<li>运行时如果报错<code>ERROR savefig failed. Reset usetex to False</code>是缺少dvipng包没能生成png格式图，但看看结果文件sampleA.sampleB.anchors还是完整生成了就可以进行下一步。</li>
+<li>sample.blast.txt</li>
 </ol>
-<h3 id="2-3-2-蛋白的同线性点图"><a href="#2-3-2-蛋白的同线性点图" class="headerlink" title="2.3.2. 蛋白的同线性点图"></a>2.3.2. 蛋白的同线性点图</h3><p>与CDS一样，cds数据换成pep数据，在运行参数里加上数据类型<code>--dbtype prot</code>即可。</p>
-<p><code>python -m jcvi.compara.catalog ortholog --dbtype prot --no_strip_names --cpu=1 sampleA sampleB</code></p>
-<p><code>--cpu=1</code>是因为conda安装的lastal不支持多线程，所以用单线程。</p>
-<h3 id="2-3-3-结果"><a href="#2-3-3-结果" class="headerlink" title="2.3.3. 结果"></a>2.3.3. 结果</h3><p>会生成五个结果文件。</p>
 <ul>
-<li>sampleA.sampleB.last: 基于LAST的比对结果</li>
-<li>sampleA.sampleB.last.filtered: LAST的比对结果过滤串联重复和低分比对</li>
-<li>sampleA.sampleB.anchors: 高质量的同线性区块</li>
-<li>sampleA.sampleB.lifted.anchors:增加了额外的锚点，形成最终的同线性区块</li>
-<li>sampleA.sampleB.pdf:同线性点图</li>
+<li>蛋白质序列的自我比对，BLAST的-outfmt 6输出格式的文件sample.blast.txt</li>
 </ul>
-<p>同线性点图一般可以看点连成的比较长的线，在同一横坐标范围有几根线，或者在同一纵坐标范围有几根线，从而判断两个基因组的倍数关系。</p>
-<img src="https://www.dropbox.com/s/32mmo0kfhtx5b8g/grape.peach.cscore.99.png?raw=1" width=100% title="同线性点图" alt="同线性点图" align=center/>
-
-<p><strong><p align="center">Figure 1. 同线性点图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi viki</a></p></p>
-<h2 id="2-4-同线性深度直方图"><a href="#2-4-同线性深度直方图" class="headerlink" title="2.4. 同线性深度直方图"></a>2.4. 同线性深度直方图</h2><ol>
-<li>在同线性点图运行成功获得sampleA.sampleB.anchors结果文件的前提下</li>
-<li>运行<code>python -m jcvi.compara.synteny depth --histogram sampleA.sampleB.anchors</code></li>
-<li>生成sampleA.sampleB.depth.pdf，显示了同线性深度比例。</li>
+<h3 id="3-1-2-用开发者脚本准备input-gff和input-len"><a href="#3-1-2-用开发者脚本准备input-gff和input-len" class="headerlink" title="3.1.2. 用开发者脚本准备input.gff和input.len"></a>3.1.2. 用开发者脚本准备input.gff和input.len</h3><p>开发者准备的三个脚本来对原始的sample.gff进行处理，对基因名称进行排序和重命名，生成新基因名称，以及输入文件input.gff,input.len，和用来准备sample.blast.txt的input.pep.fa。</p>
+<p>脚本地址：<a href="https://github.com/SunPengChuan/wgdi-example/tree/main/genome/Aquilegia_coerulea/Aquilegia_coerulea">https://github.com/SunPengChuan/wgdi-example/tree/main/genome/Aquilegia_coerulea/Aquilegia_coerulea</a></p>
+<ol>
+<li>01.getgff.py</li>
 </ol>
-<img src="https://www.dropbox.com/s/hhx2dtryrum2gyo/grape.peach.depth.png?raw=1" width=100% title="同线性深度直方图" alt="同线性深度直方图" align=center/>
+<ul>
+<li>功能：从传统的gff3格式的注释文件sample.gff中提取中间文件temp.gff</li>
+<li>用法：<code>python 01.getgff.py sample.gff temp.gff</code></li>
+<li>temp.gff格式如下：</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	DR000002	884947	886421	+</span><br><span class="line">LG01	DR000003	886634	890799	-</span><br></pre></td></tr></table></figure>
 
-<p><strong><p align="center">Figure 2. 同线性深度直方图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi viki</a></p></p>
-<h2 id="2-5-同线性染色体图"><a href="#2-5-同线性染色体图" class="headerlink" title="2.5. 同线性染色体图"></a>2.5. 同线性染色体图</h2><h3 id="2-5-1-输入文件"><a href="#2-5-1-输入文件" class="headerlink" title="2.5.1. 输入文件"></a>2.5.1. 输入文件</h3><ol>
-<li>seqids文件</li>
-</ol>
 <ul>
-<li>指定展示的染色体ID，两行对应两个物种，每行的染色体之间逗号隔开，不能有空行。</li>
-<li>seqids文件染色体的顺序对应同线性染色体图的顺序，可以根据前面得到的同线性点图来调整顺序使两个基因组的同线性对应，使图更清晰。</li>
+<li>01.getgff.py脚本：</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">scaf001,scaf002,scaf003,scaf004,scaf005,scaf006,scaf007,scaf008,scaf009,scaf010,scaf011,scaf012,scaf013,scaf014</span><br><span class="line">scaffold1,scaffold2,scaffold3,scaffold4,scaffold5,scaffold6,scaffold7</span><br></pre></td></tr></table></figure>
+<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> sys</span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"></span><br><span class="line">data = pd.read_csv(sys.argv[<span class="number">1</span>], sep=<span class="string">&quot;\t&quot;</span>, header=<span class="literal">None</span>,skiprows=<span class="number">3</span>)</span><br><span class="line">data = data[data[<span class="number">2</span>] == <span class="string">&#x27;mRNA&#x27;</span>]</span><br><span class="line">data = data.loc[:, [<span class="number">0</span>, <span class="number">8</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">6</span>]]</span><br><span class="line">data[<span class="number">8</span>] = data[<span class="number">8</span>].<span class="built_in">str</span>.split(<span class="string">&#x27;:|=|;&#x27;</span>,expand=<span class="literal">True</span>)[<span class="number">1</span>]</span><br><span class="line">data[<span class="number">0</span>] = data[<span class="number">0</span>].<span class="built_in">str</span>.replace(<span class="string">&#x27;Chr_0?&#x27;</span>,<span class="string">&#x27;&#x27;</span>)</span><br><span class="line">data.to_csv(sys.argv[<span class="number">2</span>], sep=<span class="string">&quot;\t&quot;</span>, header=<span class="literal">None</span>, index=<span class="literal">False</span>)</span><br></pre></td></tr></table></figure>
 
 <ol start="2">
-<li>.simple文件</li>
+<li>02.gff_lens.py</li>
 </ol>
 <ul>
-<li>每行代表一个同线性区块</li>
-<li>一共六列，前四列是两个物种的同线性区块的上下限基因，第五列是评分，第六列是方向。</li>
-<li>用前面分析同线性点图的结果文件.anchors创建。</li>
-<li>运行<code>python -m jcvi.compara.synteny screen --minspan=30 --simple sampleA.sampleB.anchors sampleA.sampleB.anchors.new</code>会生成sampleA.sampleB.anchors.simple文件。</li>
+<li>功能：从temp.gff中生成两个输入文件：input.gff和input.len</li>
+<li>用法：<code>python 02.gff_lens.py temp.gff input.gff input.len</code></li>
+<li>这个脚本还对基因名称做了整理和重命名。先对temp.gff进行排序，再按照物种标识符(md)+染色体名称(LG01)+基因名称(g00001)进行重命名，input.gff的第二列是新名称，第七列是旧名称。</li>
+<li>修改物种标识符(md)可以通过修改脚本的md来实现。</li>
 </ul>
-<ol start="3">
-<li>layout文件</li>
-</ol>
-<ul>
-<li>设置绘制参数。前三列是设置图的位置坐标信息，整个图的x和y轴都是[0-1]范围。第四列是设置旋转角度，第五列是设置染色体颜色，第六列是展示的标签，第七列是对齐方式，第八列指定bed文件。</li>
-<li>注意, #edges下的每一行开头都不能有空格。用于指定simple文件。<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"># y, xstart, xend, rotation, color, label, va,  bed</span><br><span class="line"> .6,     .2,    .8,       0,      lightblue, sample_A, top, sampleA.bed</span><br><span class="line"> .4,     .2,    .8,       0,      lightpink, sample_B, top, sampleB.bed</span><br><span class="line"># edges</span><br><span class="line">e, 0, 1, sampleA.sampleB.anchors.simple</span><br></pre></td></tr></table></figure></li>
-</ul>
-<h3 id="2-5-2-运行"><a href="#2-5-2-运行" class="headerlink" title="2.5.2. 运行"></a>2.5.2. 运行</h3><p><code>python -m jcvi.graphics.karyotype seqids layout</code></p>
-<p>生成karyotype.pdf文件，为同线性双染色体图</p>
-<img src="https://www.dropbox.com/s/51k8jujyzage3oa/grape.peach.karyotype.png?raw=1" width=100% title="同线性双染色体图" alt="同线性双染色体图" align=center/>
-
-<p><strong><p align="center">Figure 3. 同线性双染色体图结果示例</strong><br>from <a href="https://github-wiki-see.page/m/tanghaibao/jcvi/wiki/MCscan-%28Python-version%29">MCscan jcvi wiki</a></p></p>
-<h3 id="2-5-3-tips"><a href="#2-5-3-tips" class="headerlink" title="2.5.3. tips"></a>2.5.3. tips</h3><ul>
-<li>运行虽然报错<code>ERROR    savefig failed. Reset usetex to False.</code>，应该是没能生成png图，但还是生成了pdf图文件。</li>
-<li>当label用的名称过长时，label和图容易重叠，没有好的办法解决，最好用短一点的label。</li>
-</ul>
-<h3 id="2-5-4-个性化"><a href="#2-5-4-个性化" class="headerlink" title="2.5.4. 个性化"></a>2.5.4. 个性化</h3><h4 id="2-5-4-1-highlight指定同线性区块"><a href="#2-5-4-1-highlight指定同线性区块" class="headerlink" title="2.5.4.1. highlight指定同线性区块"></a>2.5.4.1. highlight指定同线性区块</h4><p>如果要突出显示指定的同线性区块（一般用于展示染色体倍数关系），则在sampleA.sampleB.anchors.sample文件中的对应同线性区域行的前面加上<code>yellowgreen*</code>,表示这行的颜色用yellowgreen展示。</p>
-<p>然后再运行<code>python -m jcvi.graphics.karyotype seqids layout</code>就会生成突出显示的图。</p>
-<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
-<li><a href="https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)">https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)</a></li>
-<li><a href="https://xuzhougeng.top/archives/Comparative-genomics-using-JCVI-part-one">https://xuzhougeng.top/archives/Comparative-genomics-using-JCVI-part-one</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>synteny</category>
-      </categories>
-      <tags>
-        <tag>biosoft</tag>
-        <tag>synteny</tag>
-        <tag>colinearity</tag>
-        <tag>MCscan</tag>
-        <tag>jcvi</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>使用Rldeogram的ideogram函数画两物种的同线性图</title>
-    <url>/2022/08/03/bioinfo_synteny_plot_Rldeogram/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=20707476&auto=1&height=32"></iframe><music URL></div>
-
-<h1 id="1-使用Rldeogram的ideogram函数画两物种的共线性图"><a href="#1-使用Rldeogram的ideogram函数画两物种的共线性图" class="headerlink" title="1. 使用Rldeogram的ideogram函数画两物种的共线性图"></a>1. 使用Rldeogram的ideogram函数画两物种的共线性图</h1><h2 id="1-1-输入文件"><a href="#1-1-输入文件" class="headerlink" title="1.1. 输入文件"></a>1.1. 输入文件</h2><ol>
-<li>karyotype.txt</li>
-</ol>
-<ul>
-<li>Chr: 染色体号</li>
-<li>Start: 起始</li>
-<li>End: 终止</li>
-<li>fill: 染色体填充色</li>
-<li>species：物种名</li>
-<li>size: 物种名字体大小</li>
-<li>color: 物种名字体颜色</li>
-</ul>
-<p>文件示例：</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr Start      End   fill species size  color</span><br><span class="line">1   1 23037639  FF8C00   Grape   12 252525</span><br><span class="line">2   1 18779884  FF8C00   Grape   12 252525</span><br><span class="line">3   1 17934068  FF8C00   Grape   12 252525</span><br><span class="line">4   1 17349521  FF8C00   Grape   12 252525</span><br><span class="line">1   1 22042719  4682B4   Populus   12 252525</span><br><span class="line">2   1 19858802  4682B4   Populus   12 252525</span><br><span class="line">3   1 19278319  4682B4   Populus   12 252525</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>synteny.txt</li>
-</ol>
-<ul>
-<li>Species_1：物种1染色体号</li>
-<li>Start_1，End_1：物种1染色体区域位置</li>
-<li>Species_2：物种2染色体号</li>
-<li>Start_2，End_2：物种2染色体区域位置</li>
-</ul>
-<p>文件示例：</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Species_1  Start_1    End_1 Species_2 Start_2   End_2   fill</span><br><span class="line">1   12226377    12267836    1   5900307 5827251 cccccc</span><br><span class="line">1  5635667 5667377 2 4459512  4393226 cccccc</span><br><span class="line">1   7916366 7945659 3 8618518   8486865 cccccc</span><br><span class="line">2   8214553 8242202 1 5964233  6027199 cccccc</span><br><span class="line">3  2330522 2356593 1 6224069  6138821 cccccc</span><br><span class="line">3  10861038    10886821    2  8099058 8011502 cccccc</span><br><span class="line">4  9487312    9540261    3  7657579 7701112 cccccc</span><br></pre></td></tr></table></figure>
-
-<h2 id="1-2-运行"><a href="#1-2-运行" class="headerlink" title="1.2. 运行"></a>1.2. 运行</h2><figure class="highlight r"><table><tr><td class="code"><pre><span class="line">install.packages<span class="punctuation">(</span><span class="string">&#x27;RIdeogram&#x27;</span><span class="punctuation">)</span> <span class="comment">#安装RIdeogram</span></span><br><span class="line">library<span class="punctuation">(</span><span class="string">&#x27;RIdeogram&#x27;</span><span class="punctuation">)</span> <span class="comment">#载入RIdeogram</span></span><br><span class="line">ka <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;karyotype.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">,</span>header <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span>stringsAsFactors <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span> <span class="comment">#读取karyotype.txt文件</span></span><br><span class="line">sy <span class="operator">&lt;-</span> read.table<span class="punctuation">(</span><span class="string">&quot;synteny.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span><span class="string">&quot;\t&quot;</span><span class="punctuation">,</span>header <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span>stringsAsFactors <span class="operator">=</span> <span class="built_in">F</span><span class="punctuation">)</span> <span class="comment">#读取synteny.txt文件</span></span><br><span class="line">ideogram<span class="punctuation">(</span>karyotype <span class="operator">=</span> ka<span class="punctuation">,</span> synteny <span class="operator">=</span> sy<span class="punctuation">)</span> <span class="comment">#使用ideogram函数，生成chromosome.svg文件用于绘图</span></span><br><span class="line">convertSVG<span class="punctuation">(</span><span class="string">&quot;chromosome.svg&quot;</span><span class="punctuation">,</span> device <span class="operator">=</span> <span class="string">&quot;pdf&quot;</span><span class="punctuation">,</span>dpi<span class="operator">=</span><span class="number">1600</span><span class="punctuation">)</span> <span class="comment">#转化成chromosome.pdf文件，还可选择转化的格式：tiff，png，jpg，分辨率1600。</span></span><br></pre></td></tr></table></figure>
-
-<h2 id="1-3-结果"><a href="#1-3-结果" class="headerlink" title="1.3. 结果"></a>1.3. 结果</h2><p>结果如下图：</p>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/ideogram.png?raw=true" title="Rldeogram绘制的同线性图" width="80%" />
-
-<p><strong><p align="center">Figure 1. Rldeogram绘制的同线性图</p></strong></p>
-<h1 id="2-references"><a href="#2-references" class="headerlink" title="2. references"></a>2. references</h1><ol>
-<li><a href="https://www.jianshu.com/p/07ae1fe18071">https://www.jianshu.com/p/07ae1fe18071</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>synteny</category>
-        <category>plot</category>
-      </categories>
-      <tags>
-        <tag>R package</tag>
-        <tag>R</tag>
-        <tag>plot</tag>
-        <tag>synteny</tag>
-        <tag>Rldeogram</tag>
-        <tag>ideogram</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>分析基因组共线性、计算Ks和鉴定WGD —— WGDI</title>
-    <url>/2021/09/11/bioinfo_synteny_WGDI/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1697043&auto=1&height=32"></iframe></div>
-
-<h1 id="1-WGDI介绍"><a href="#1-WGDI介绍" class="headerlink" title="1. WGDI介绍"></a>1. WGDI介绍</h1><p>WGDI (Whole-Genome Duplication Integrated analysis)是python写的命令行工具，用于多倍化和跨物种基因组比对的综合分析，包括鉴定基因组内共线性区块，Ks计算，全基因组复制事件(WGD, whole genome duplication)的数量和时间的鉴定。</p>
-<p>WGDI主要的三个功能是：</p>
-<ul>
-<li>多倍体推断</li>
-<li>基因组同源性的层次推断</li>
-<li>祖先染色体核型分析</li>
-</ul>
-<h1 id="2-WGDI安装"><a href="#2-WGDI安装" class="headerlink" title="2. WGDI安装"></a>2. WGDI安装</h1><p>WGDI的依赖：PAML,MAFFT,MUSCLE,PAL2NAL,IQTREE。</p>
-<p>安装有三种方法:</p>
-<ol>
-<li>conda<br><code>conda create -c bioconda -c conda-forge -n wgdi wgdi</code> 建议新建一个环境</li>
-<li>pip<br><code>pip install wgdi</code></li>
-<li>github<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/SunPengChuan/wgdi.git</span><br><span class="line">cd wgdi</span><br><span class="line">python setup.py install</span><br></pre></td></tr></table></figure></li>
-</ol>
-<h1 id="3-WGDI进行共线性分析"><a href="#3-WGDI进行共线性分析" class="headerlink" title="3. WGDI进行共线性分析"></a>3. WGDI进行共线性分析</h1><h2 id="3-1-初始输入文件"><a href="#3-1-初始输入文件" class="headerlink" title="3.1. 初始输入文件"></a>3.1. 初始输入文件</h2><h3 id="3-1-1-输入文件"><a href="#3-1-1-输入文件" class="headerlink" title="3.1.1. 输入文件"></a>3.1.1. 输入文件</h3><p>WGDI需要三个输入文件，分别是基因的位置信息文件sample.gff，染色体长度信息文件sample.len和BLAST的输出文件sample.blast.txt，要求格式如下：</p>
-<ol>
-<li>input.gff</li>
-</ol>
-<ul>
-<li>一共七列，以tab分隔，分别为chr(chromosome number)，geneid(gene name)，start，end，direction(strand+&#x2F;-)，order(order of each chromosome,starting from 1, 每个chr内部从1开始的顺序），original(original id and not read,旧的id，不会读入)。</li>
-<li>和典型的GFF格式不一样</li>
-</ul>
-<ol start="2">
-<li>input.len</li>
-</ol>
-<ul>
-<li>一共三列，以tab分隔，染色体长度信息和染色体上的基因个数，chr(chromosome number), chr_length(染色体长度bp), chr_gene_number(注释到单个染色体的基因数量)。</li>
-<li>后面分析作图，比如点图，图中显示的染色体顺序是依据这个文件的顺序来决定的。所以可以通过修改这个文件的染色体顺序来修改图的显示。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	35874591	5368</span><br><span class="line">LG02	29224816	3939</span><br><span class="line">LG03	27760256	3620</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>sample.blast.txt</li>
-</ol>
-<ul>
-<li>蛋白质序列的自我比对，BLAST的-outfmt 6输出格式的文件sample.blast.txt</li>
-</ul>
-<h3 id="3-1-2-用开发者脚本准备input-gff和input-len"><a href="#3-1-2-用开发者脚本准备input-gff和input-len" class="headerlink" title="3.1.2. 用开发者脚本准备input.gff和input.len"></a>3.1.2. 用开发者脚本准备input.gff和input.len</h3><p>开发者准备的三个脚本来对原始的sample.gff进行处理，对基因名称进行排序和重命名，生成新基因名称，以及输入文件input.gff,input.len，和用来准备sample.blast.txt的input.pep.fa。</p>
-<p>脚本地址：<a href="https://github.com/SunPengChuan/wgdi-example/tree/main/genome/Aquilegia_coerulea/Aquilegia_coerulea">https://github.com/SunPengChuan/wgdi-example/tree/main/genome/Aquilegia_coerulea/Aquilegia_coerulea</a></p>
-<ol>
-<li>01.getgff.py</li>
-</ol>
-<ul>
-<li>功能：从传统的gff3格式的注释文件sample.gff中提取中间文件temp.gff</li>
-<li>用法：<code>python 01.getgff.py sample.gff temp.gff</code></li>
-<li>temp.gff格式如下：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	DR000002	884947	886421	+</span><br><span class="line">LG01	DR000003	886634	890799	-</span><br></pre></td></tr></table></figure>
-
-<ul>
-<li>01.getgff.py脚本：</li>
-</ul>
-<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> sys</span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"></span><br><span class="line">data = pd.read_csv(sys.argv[<span class="number">1</span>], sep=<span class="string">&quot;\t&quot;</span>, header=<span class="literal">None</span>,skiprows=<span class="number">3</span>)</span><br><span class="line">data = data[data[<span class="number">2</span>] == <span class="string">&#x27;mRNA&#x27;</span>]</span><br><span class="line">data = data.loc[:, [<span class="number">0</span>, <span class="number">8</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">6</span>]]</span><br><span class="line">data[<span class="number">8</span>] = data[<span class="number">8</span>].<span class="built_in">str</span>.split(<span class="string">&#x27;:|=|;&#x27;</span>,expand=<span class="literal">True</span>)[<span class="number">1</span>]</span><br><span class="line">data[<span class="number">0</span>] = data[<span class="number">0</span>].<span class="built_in">str</span>.replace(<span class="string">&#x27;Chr_0?&#x27;</span>,<span class="string">&#x27;&#x27;</span>)</span><br><span class="line">data.to_csv(sys.argv[<span class="number">2</span>], sep=<span class="string">&quot;\t&quot;</span>, header=<span class="literal">None</span>, index=<span class="literal">False</span>)</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>02.gff_lens.py</li>
-</ol>
-<ul>
-<li>功能：从temp.gff中生成两个输入文件：input.gff和input.len</li>
-<li>用法：<code>python 02.gff_lens.py temp.gff input.gff input.len</code></li>
-<li>这个脚本还对基因名称做了整理和重命名。先对temp.gff进行排序，再按照物种标识符(md)+染色体名称(LG01)+基因名称(g00001)进行重命名，input.gff的第二列是新名称，第七列是旧名称。</li>
-<li>修改物种标识符(md)可以通过修改脚本的md来实现。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	mdLG01g00001	23987	27163	+	1	DR032173</span><br><span class="line">LG01	mdLG01g00002	30894	32329	-	2	DR032172</span><br></pre></td></tr></table></figure>
-
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">LG01	mdLG01g00001	23987	27163	+	1	DR032173</span><br><span class="line">LG01	mdLG01g00002	30894	32329	-	2	DR032172</span><br></pre></td></tr></table></figure>
+
 <ul>
 <li>02.gff_lens.py脚本：</li>
 </ul>
@@ -8293,256 +8113,76 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </categories>
       <tags>
         <tag>biosoft</tag>
-        <tag>Ks</tag>
         <tag>WGD</tag>
+        <tag>Ks</tag>
         <tag>synteny</tag>
         <tag>colinearity</tag>
         <tag>WGDI</tag>
       </tags>
   </entry>
   <entry>
-    <title>转录因子（transcription factor，TF）基础及WGD后保留的TF分析</title>
-    <url>/2022/10/18/bioinfo_transcriptionFactor/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105140&auto=1&height=32"></iframe></div>
-
+    <title>结构变异分析软件：MUM&amp;Co</title>
+    <url>/2022/07/17/bioinfo_variation_SV_MUMandCo/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=18031133&auto=1&height=32"></iframe></div>
 
-<h1 id="1-转录因子（transcription-factor）"><a href="#1-转录因子（transcription-factor）" class="headerlink" title="1. 转录因子（transcription factor）"></a>1. 转录因子（transcription factor）</h1><p>转录因子（transcription factor，TF）是一种蛋白质，它通过与特定DNA序列结合来控制遗传信息从DNA到信使RNA的转录速率。</p>
-<p>TFs 的功能是调节——打开和关闭——基因，以确保它们在所需的细胞中在正确的时间和正确的数量表达。TF 组以协调的方式发挥作用，在整个生命过程中指导细胞分裂、细胞生长和细胞死亡；胚胎发育过程中的细胞迁移和组织；并且间歇性地响应来自细胞外的信号，例如激素。人类基因组中有多达 1600 个 TF 。转录因子是蛋白质组和调节组的成员。</p>
-<h1 id="2-植物转录因子数据库PlantTFDB"><a href="#2-植物转录因子数据库PlantTFDB" class="headerlink" title="2. 植物转录因子数据库PlantTFDB"></a>2. 植物转录因子数据库PlantTFDB</h1><p>植物转录因子数据库PlantTFDB是北京大学生物信息学中心研发的数据库和网站，目前包括165个植物物种的转录因子。</p>
-<p>目前数据库已更新到v5.0，在网站<a href="http://planttfdb.gao-lab.org/index.php%E5%8F%AF%E4%BB%A5%E6%9F%A5%E7%9C%8B%E3%80%81%E4%B8%8B%E8%BD%BD%E5%92%8C%E4%BD%BF%E7%94%A8%E6%A4%8D%E7%89%A9%E8%BD%AC%E5%BD%95%E5%9B%A0%E5%AD%90%E6%95%B0%E6%8D%AE%E5%BA%93%E3%80%82">http://planttfdb.gao-lab.org/index.php可以查看、下载和使用植物转录因子数据库。</a></p>
-<p>网站的功能包括：</p>
-<ol>
-<li>上传核酸或蛋白质的fasta序列，在线做转录因子的注释。</li>
-<li>上传核酸或蛋白质的fasta序列，在线与数据库做blastx或blastp比对。</li>
-<li>下载特定植物的TF列表，CDS或蛋白质序列。</li>
-<li>查询特定TF和TF家族的功能描述。</li>
-</ol>
-<h1 id="3-转录因子相关分析"><a href="#3-转录因子相关分析" class="headerlink" title="3. 转录因子相关分析"></a>3. 转录因子相关分析</h1><p>转录因子分析可以应用的场景很多，这里介绍全基因组复制事件（WGD）后转录因子保留的分析。</p>
-<h2 id="3-1-WGD后保留TF的分析"><a href="#3-1-WGD后保留TF的分析" class="headerlink" title="3.1. WGD后保留TF的分析"></a>3.1. WGD后保留TF的分析</h2><h3 id="3-1-1-思路"><a href="#3-1-1-思路" class="headerlink" title="3.1.1. 思路"></a>3.1.1. 思路</h3><p>除了直接看WGD后保留的基因中包含了什么种类和多少数量的TF外，还可以通过利用转录因子数据库PlantTFDB来做WGD后保留的每种TF的保留模式的进一步分析。</p>
+<h1 id="1-MUM-amp-Co简介"><a href="#1-MUM-amp-Co简介" class="headerlink" title="1. MUM&amp;Co简介"></a>1. MUM&amp;Co简介</h1><p>MUM&amp;Co是用于<strong>两个基因组间</strong>的<strong>结构变异（structural variant，SV）</strong>检测分析的软件，基于MUMmer(v4)的nucmer比对结果检测全基因组范围内的插入，缺失，串联重复，倒位和易位等结构变异。</p>
+<p>2020年发表在Bioinformatics。</p>
+<h1 id="2-MUM-amp-Co原理"><a href="#2-MUM-amp-Co原理" class="headerlink" title="2. MUM&amp;Co原理"></a>2. MUM&amp;Co原理</h1><p>MUMandCo调用MUMmer的nucmer模块做互换的全基因组比对（whole-genome alignmetn，WGA），delta-filter模块做全局（g-）和多对多（m-）的过滤，show-coords模块解析坐标。</p>
 <ol>
-<li>参考</li>
+<li>选出与参考基因组有最精确、非重叠的alignments的query contigs。其余的alignment都不考虑。</li>
+<li>利用这些alignments统计超过50bp的SV的类型和特征。</li>
+<li>全局（g-）比对 基于多个contig-chromosome pairings来检测易位（translocation）片段，基于alignment orientation检测大的倒位（inversions），基于alignment gaps检测可能的插入缺失（insertion and deletion），利用互换的比对，gaps会在reference和query基因组间同时被考虑。</li>
+<li>多对多（m-）比对用于检测潜在的倒位（inversions）和重复（duplications）。</li>
+<li>用全局（g-）和多对多（m-）比对的过滤去除假阳性。</li>
+<li>参数<code>-b</code>可以调用BLAST来检测插入和缺失是重复的（mobile）还是新的（novel）。</li>
+<li>生成保存了reference和query基因组的SV的类型、坐标等细节的tsv文件。</li>
 </ol>
-<ul>
-<li>paper: <a href="https://www.sciencedirect.com/science/article/pii/S1674205219303594">https://www.sciencedirect.com/science/article/pii/S1674205219303594</a> 的 Retention Analysis of Transcription Factors部分。</li>
-<li>在博客<strong>鉴定全基因复制事件(WGD)后保留的复制基因</strong> <a href="https://yanzhongsino.github.io/2022/10/18/bioinfo_WGD_geneRetention/">https://yanzhongsino.github.io/2022/10/18/bioinfo_WGD_geneRetention/</a> 的基础上完成WGD后保留TF的分析</li>
+<h1 id="3-MUM-amp-Co可以检测的内容"><a href="#3-MUM-amp-Co可以检测的内容" class="headerlink" title="3. MUM&amp;Co可以检测的内容"></a>3. MUM&amp;Co可以检测的内容</h1><ul>
+<li>插入（insertion）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
+<li>缺失（deletion）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
+<li>串联重复复制（tandem duplication）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
+<li>串联重复收缩（tandem contraction）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
+<li>倒位（inversion）：&gt;&#x3D;1kb</li>
+<li>易位（translocation）：&gt;&#x3D;10kb</li>
+</ul>
+<h1 id="4-MUM-amp-Co安装"><a href="#4-MUM-amp-Co安装" class="headerlink" title="4. MUM&amp;Co安装"></a>4. MUM&amp;Co安装</h1><ul>
+<li>MUMandCo是个bash脚本，下载后赋予执行权限即可使用。</li>
+<li>依赖的软件包括MUMmer(v4)和samtools，如果使用<code>-b</code>参数则还需要blast。</li>
 </ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/SAMtoBAM/MUMandCo.git</span><br><span class="line">chmod 711 ./MUMandCo/mumandco_v3.8.sh</span><br></pre></td></tr></table></figure>
+
+<h1 id="5-MUM-amp-Co使用"><a href="#5-MUM-amp-Co使用" class="headerlink" title="5. MUM&amp;Co使用"></a>5. MUM&amp;Co使用</h1><ol>
+<li>运行</li>
+</ol>
+<p><code>bash mumandco_v3.8.sh -r genome.fa -q query.fa -g 125500000 -t 24 -b -o out</code></p>
 <ol start="2">
-<li>基本思路</li>
+<li>参数</li>
 </ol>
 <ul>
-<li>从PlantTFDB数据库下载已有物种（比如拟南芥）的TF家族，用下载的TF家族注释orthogroups。对每个WGD事件，确认每个TF家族的保留的orthogroups的数量。</li>
-<li>有些TF家族可能会被分到几个orthogroups，为了消除一个TF家族的orthogroups的大小不均的影响，文章通过标准化计算一个保留参数R值（retention value），R值用来反映WGD事件后每个TF的保留模式。</li>
+<li>-r genome.fa：参考基因组。注意参考基因组和检测基因组的选择不同，结果会有一些差异。</li>
+<li>-q query.fa：被检测的基因组</li>
+<li>-g 125500000：参考基因组大小，单位是bp</li>
+<li>-t 24：线程24，默认1</li>
+<li>-b：添加blast选项用来确认插入&#x2F;缺失是mobile&#x2F;repetitive还是novel。我的理解是mobile&#x2F;repetitive代表别的地方有这个插入&#x2F;缺失，novel代表是一个新产生的插入&#x2F;缺失。</li>
+<li>-ml：alignments的最小长度；默认50bp</li>
+<li>-o out：输出文件的前缀；默认是mumandco</li>
 </ul>
 <ol start="3">
-<li>R值的计算公式：$$Rvalue&#x3D;(Rs⁄Ts)&#x2F;(Ra⁄Ta)&#x3D;Rs<em>Ta&#x2F;Ts</em>Ra$$，其中：</li>
+<li>时间</li>
 </ol>
 <ul>
-<li>Rs: Number of orthogroups with retention in specific TF</li>
-<li>Ts: Total number of orthogroups in specific TF</li>
-<li>Ra: Number of all TF orthogroups with retention</li>
-<li>Ta: Total number of TF orthogroups</li>
-<li>Rs&#x2F;Ts: 代表在WGD后特定TF家族保留的可能性</li>
-<li>Ra&#x2F;Ta: 代表在WGD后所有TF家族保留的可能性</li>
-<li>Rvalue: 用Rs&#x2F;Ts比上Ra&#x2F;Ta，代表相较TF家族平均水平，特定TF家族保留的可能性的高低。Rvalue越大，特定TF家族的保留率越高。</li>
+<li>两个都是约300Mbp的基因组的SV分析，用了24线程，运行总耗时5小时。</li>
 </ul>
-<h3 id="3-1-2-准备文件"><a href="#3-1-2-准备文件" class="headerlink" title="3.1.2. 准备文件"></a>3.1.2. 准备文件</h3><ol>
-<li>Orthogroups.txt</li>
+<h1 id="6-MUM-amp-Co结果"><a href="#6-MUM-amp-Co结果" class="headerlink" title="6. MUM&amp;Co结果"></a>6. MUM&amp;Co结果</h1><ol>
+<li>out.summary.txt</li>
 </ol>
 <ul>
-<li>Orthofinder的结果文件&#x2F;path&#x2F;to&#x2F;OrthoFinder&#x2F;Results_xx&#x2F;Orthogroups&#x2F;Orthogroups.txt</li>
-<li>Orthofinder运行时需要包含了下载TF的物种</li>
+<li>总结SV的类型和数量的文件</li>
 </ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">mumandco</span><br><span class="line">Total_SVs	8252</span><br><span class="line">Deletions	3337</span><br><span class="line">Insertions	4363</span><br><span class="line">Duplications	44</span><br><span class="line">Contractions	8</span><br><span class="line">Inversions	144</span><br><span class="line">Translocations	356</span><br></pre></td></tr></table></figure>
+
 <ol start="2">
-<li>dup_wgd.og</li>
-</ol>
-<ul>
-<li>dup_wgd.og包含了前期分析的基因复制的、涉及特定WGD保留的那些orthogroups的ID列表。</li>
-<li>可以从WGD后保留基因的分析的结果文件<code>N5_filter_OG_dup.tsv</code>中提取第二列来获取：<code>cat N5_filter_OG_dup.tsv|cut -f2 &gt;dup_wgd.og</code>。</li>
-</ul>
-<ol start="3">
-<li>下载Ath_TF_list.txt并转化成Ath_TF_list.og</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">cat Ath_TF_list.txt|sed &#x27;1d&#x27;|cut -f2 &gt;ath_2.tem # 提取第二列geneID</span><br><span class="line">cat Ath_TF_list.txt |sed &#x27;1d&#x27;|cut -f3|sort|uniq &gt;ath.tf # 提取第三列Family</span><br><span class="line">for i in $(cat ath_2.tem); do echo $i &gt;&gt;ath.og &amp;&amp; grep $i /path/to/OrthoFinder/Results_xx/Orthogroups/Orthogroups.txt|cut -d &quot;:&quot; -f1 &gt;&gt;ath.og ; done # 根据geneID提取orthogroups</span><br><span class="line">sed -i -e &#x27;1i\Gene_ID\torthogroups_ID&#x27; -e &quot;s/ /\t/g&quot; ath.og # 在ath.og文件首行前插入标题行，并把列间分隔的空格改成tab分隔。</span><br><span class="line">paste Ath_TF_list.txt ath.og &gt;Ath_TF_list.og # 横向拼接Ath_TF_list.txt和ath.og两个文件</span><br><span class="line">head Ath_TF_list.og &amp;&amp; tail Ath_TF_list.og # 检查一下首尾的第二列和第四列是不是一样，看有没有拼接错误</span><br></pre></td></tr></table></figure>
-
-<h3 id="3-1-3-统计R相关参数"><a href="#3-1-3-统计R相关参数" class="headerlink" title="3.1.3. 统计R相关参数"></a>3.1.3. 统计R相关参数</h3><ol>
-<li>这里的Ta、Ra、Ts、Rs可以用两种数量来代表，一种是统计TF_ID的数量，另一种是统计Orthogroups的数量。</li>
-</ol>
-<ul>
-<li>下面的是统计TF_ID的数量，如果想要统计Orthogroups的数量，则需要在每一个值统计命令<code>wc -l</code>前面加上<code>cut -f5|sort|uniq|</code>来提取Orthogroups并去重。</li>
-</ul>
-<ol start="2">
-<li>对每一个ath.tf里的Family，统计Ta,Ra,Rs和Ts值</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">for i in $(cat ath.tf);</span><br><span class="line">do</span><br><span class="line">    ta = $(($(cat Ath_TF_list.ogs|wc -l)-1)) # 统计Ta值（有标题行，结果需要减1）</span><br><span class="line">    ra = $(grep -f dup_wgd.og Ath_TF_list.ogs|wc -l) # 统计Ra值</span><br><span class="line">	rs=$(grep -f dup_wgd.og Ath_TF_list.ogs |awk -v awka=&quot;$i&quot; &#x27;$3 == awka &#123;print$0&#125;&#x27;|wc -l);</span><br><span class="line">	ts=$(awk -v awka=&quot;$i&quot; &#x27;$3 == awka &#123;print $0&#125;&#x27; Ath_TF_list.ogs |wc -l);</span><br><span class="line">	echo &quot;$&#123;i&#125; $&#123;rs&#125; $&#123;ts&#125; $&#123;ra&#125; $&#123;ta&#125;&quot; &gt;&gt; ath_r.tem</span><br><span class="line">done</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>有了Ta,Ra,Rs和Ts值，接下来就可以计算Rvalue&#x3D;(Rs⁄Ts)&#x2F;(Ra⁄Ta)了。</li>
-</ol>
-<ul>
-<li><code>cat ath_r.tem|sed &quot;s/ /\t/g&quot;|awk -F&quot;\t&quot; &#39;&#123;print $0,($1*$4)/($2*$3)&#125;&#39;|sed &#39;1i\TF\tRs\tTs\tRa\tTa\tRvalue&#39; &gt;ath_r.txt</code> 待检查是否有效</li>
-</ul>
-<h3 id="3-1-4-绘制热图"><a href="#3-1-4-绘制热图" class="headerlink" title="3.1.4. 绘制热图"></a>3.1.4. 绘制热图</h3><p>热图绘制可以参考博客<a href="https://yanzhongsino.github.io/2022/11/06/R_plot_heatmap">https://yanzhongsino.github.io/2022/11/06/R_plot_heatmap</a></p>
-<ul>
-<li>如果只有一次WGD的TF保留结果，可以直接根据Rvalue判断哪些TF家族保留率高。</li>
-<li>如果有多次WGD的TF保留结果，或者做了多个物种的TF数据库保留结果，可以绘制热图相互比较。</li>
-</ul>
-<p>用R包pheatmap绘制热图，简单快捷。(notes: 画热图这里的代码还需根据数据格式调整)</p>
-<figure class="highlight r"><table><tr><td class="code"><pre><span class="line">df<span class="operator">&lt;-</span>read.table<span class="punctuation">(</span><span class="string">&quot;tf_rvalue.txt&quot;</span><span class="punctuation">,</span>sep<span class="operator">=</span> <span class="string">&quot; &quot;</span><span class="punctuation">,</span> header <span class="operator">=</span> <span class="built_in">T</span><span class="punctuation">,</span>row.names <span class="operator">=</span> <span class="number">1</span><span class="punctuation">)</span></span><br><span class="line">df_row <span class="operator">&lt;-</span> hclust<span class="punctuation">(</span>dist<span class="punctuation">(</span>df<span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#对行聚类</span></span><br><span class="line">df <span class="operator">&lt;-</span> df<span class="punctuation">[</span>df_row<span class="operator">$</span>order<span class="punctuation">,</span><span class="punctuation">]</span> <span class="comment">#按行聚类结果排序</span></span><br><span class="line">df_column <span class="operator">&lt;-</span> hclust<span class="punctuation">(</span>dist<span class="punctuation">(</span>t<span class="punctuation">(</span>df<span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">)</span> <span class="comment">#对列聚类</span></span><br><span class="line">df <span class="operator">&lt;-</span> df<span class="punctuation">[</span><span class="punctuation">,</span>df_column<span class="operator">$</span>order<span class="punctuation">]</span> <span class="comment">#按列聚类结果排序</span></span><br><span class="line"></span><br><span class="line">BiocManager<span class="operator">::</span>install<span class="punctuation">(</span><span class="string">&quot;pheatmap&quot;</span><span class="punctuation">)</span></span><br><span class="line">library<span class="punctuation">(</span>pheatmap<span class="punctuation">)</span></span><br><span class="line">pheatmap<span class="punctuation">(</span>df<span class="punctuation">,</span>color <span class="operator">=</span> colorRampPalette<span class="punctuation">(</span><span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;lightgreen&quot;</span><span class="punctuation">,</span> <span class="string">&quot;yellow&quot;</span><span class="punctuation">,</span><span class="string">&quot;orange&quot;</span><span class="punctuation">,</span><span class="string">&quot;red&quot;</span><span class="punctuation">)</span><span class="punctuation">)</span><span class="punctuation">(</span><span class="number">20</span><span class="punctuation">)</span><span class="punctuation">,</span>legend_breaks <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="number">1</span><span class="operator">:</span><span class="number">4</span><span class="punctuation">)</span><span class="punctuation">,</span> legend_labels <span class="operator">=</span> <span class="built_in">c</span><span class="punctuation">(</span><span class="string">&quot;1.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;2.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;3.0&quot;</span><span class="punctuation">,</span><span class="string">&quot;4.0&quot;</span><span class="punctuation">)</span><span class="punctuation">,</span> border_color<span class="operator">=</span><span class="string">&quot;white&quot;</span><span class="punctuation">,</span>treeheight_row <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> treeheight_col <span class="operator">=</span> <span class="number">8</span><span class="punctuation">,</span> display_numbers <span class="operator">=</span> <span class="literal">TRUE</span><span class="punctuation">,</span> number_color <span class="operator">=</span> <span class="string">&quot;black&quot;</span><span class="punctuation">,</span>main <span class="operator">=</span> <span class="string">&quot;TF heatmap&quot;</span><span class="punctuation">,</span>cellwidth <span class="operator">=</span> <span class="number">50</span><span class="punctuation">,</span> cellheight <span class="operator">=</span> <span class="number">10</span><span class="punctuation">)</span></span><br><span class="line"><span class="comment"># 其中color = colorRampPalette(c(&quot;lightgreen&quot;, &quot;yellow&quot;,&quot;orange&quot;,&quot;red&quot;))(20) #设置颜色渐变，值从低到高依次是浅绿色-黄色-橙色-红色，共20个颜色。</span></span><br></pre></td></tr></table></figure>
-
-<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li>wiki:transcription factor: <a href="https://en.wikipedia.org/wiki/Transcription_factor">https://en.wikipedia.org/wiki/Transcription_factor</a></li>
-<li>PlantTFDB: <a href="http://planttfdb.gao-lab.org/index.php">http://planttfdb.gao-lab.org/index.php</a></li>
-<li>paper: <a href="https://www.sciencedirect.com/science/article/pii/S1674205219303594">https://www.sciencedirect.com/science/article/pii/S1674205219303594</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>transcription factor</category>
-      </categories>
-      <tags>
-        <tag>WGD</tag>
-        <tag>transcription factor</tag>
-        <tag>PlantTFDB</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>结构变异分析软件：Assemblytics</title>
-    <url>/2022/08/02/bioinfo_variation_SV_Assemblytics/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=20707476&auto=1&height=32"></iframe></div>
-
-<h1 id="1-Assemblytics简介"><a href="#1-Assemblytics简介" class="headerlink" title="1. Assemblytics简介"></a>1. Assemblytics简介</h1><ol>
-<li>Assemblytics在2016年发表在Bioinformatics上，用于检测检测基因组间的结构变异（Structural Variation，SV）。</li>
-<li>Assemblytics检测的结构变异主要包括：INDELs，tandem和repeat的expansion和contraction；不包含倒位inversion和易位translocation的检测。</li>
-<li>Assemblytics建议使用基因组组装的contigs而非scaffolds，避免Ns对统计结构变异产生影响（假阳性）。</li>
-</ol>
-<img src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bioinformatics/32/19/10.1093_bioinformatics_btw369/5/btw369f1p.gif?Expires=1662466735&Signature=XxOCD8I9su8WHNrORriUZmNxiCU219tKxNJr~iM-QEGM1pWydiON3YFnn~znOY~ibASOkh6T8rTksGBcPKFI-3ymeYLx2FDqR29phWp90a4of40ouDPqKsK5Crw5PVK5qNbbbV67n5hKpsaxUOtim-jDp5kyM8lZmPzQGm6qTRE5nclS6Lis-mQw96ZQFeguCbTS1k4Re7ULtzaMO1uLOEWG-401s~2JbBG07HI7cmt2UAP3zLV~n8CV8ISpi~W8SqvBaF7EjEqpPITGxSQQJCTP8EOg1erhp7Knf6jgTuwvnGShz4-cNNH2oRN3~2kw6aug3~B0qqPmhgFkW4JYGQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" width=100% title="Assemblytics" alt="Assemblytics" align=center/>
-
-<p><strong><p align="center">Figure 1. variant class and web results interface Assemblytics</strong><br>图片来源： <a href="https://academic.oup.com/bioinformatics/article/32/19/3021/2196631">Assemblytics paper</a></p></p>
-<h1 id="2-Assemblytics使用"><a href="#2-Assemblytics使用" class="headerlink" title="2. Assemblytics使用"></a>2. Assemblytics使用</h1><p>Assemblytics的使用很简单，网站也说得清楚明了。</p>
-<ol>
-<li>先用MUMmer把reference genome和query genome比对，得到共线性结果。</li>
-</ol>
-<p><code>nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT</code></p>
-<ul>
-<li>-l 100：最小匹配长度设置成100</li>
-<li>-c 500：最小匹配序列簇的匹配长度为500</li>
-<li>-prefix OUT：输出结果文件前缀为OUT</li>
-</ul>
-<ol start="2">
-<li>把delta结果文件压缩，压缩后的结果OUT.delta.gz上传到Assemblytics网站。</li>
-</ol>
-<p><code>gzip OUT.delta</code></p>
-<ol start="3">
-<li>填写参数，提交即可在线运行和得到结果。参数包括：</li>
-</ol>
-<ul>
-<li>Description：物种名或样品名</li>
-<li>Unique Sequence length required：需要的独特序列长度，代表一个决定用于call variants的序列是否独特的一个锚定（anchor），用于代替读取比对（read alignment）的映射质量的过滤，默认是10000。</li>
-<li>Maximum variant size：最大变异的大小，默认是10000。</li>
-<li>Minimum variant size：最小变异的大小，默认是50。</li>
-</ul>
-<h1 id="3-结果"><a href="#3-结果" class="headerlink" title="3. 结果"></a>3. 结果</h1><p>运行结果包括可交互的可视化图的界面和可下载的压缩包。</p>
-<p>主要结果在文件<strong>Assemblytics_structural_variants.summary</strong>中，文件内容示例：</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Insertion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        835          91429</span><br><span class="line">     500-10,000 bp:        158         468629</span><br><span class="line">             Total:        993         560058</span><br><span class="line"></span><br><span class="line">Deletion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        779          85083</span><br><span class="line">     500-10,000 bp:        140         289652</span><br><span class="line">             Total:        919         374735</span><br><span class="line"></span><br><span class="line">Tandem_expansion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:         46           8957</span><br><span class="line">     500-10,000 bp:        117         444964</span><br><span class="line">             Total:        163         453921</span><br><span class="line"></span><br><span class="line">Tandem_contraction</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:          2            379</span><br><span class="line">     500-10,000 bp:          0              0</span><br><span class="line">             Total:          2            379</span><br><span class="line"></span><br><span class="line">Repeat_expansion</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        373          80936</span><br><span class="line">     500-10,000 bp:        968        3666087</span><br><span class="line">             Total:       1341        3747023</span><br><span class="line"></span><br><span class="line">Repeat_contraction</span><br><span class="line">                         Count       Total bp</span><br><span class="line">         50-500 bp:        411          92021</span><br><span class="line">     500-10,000 bp:        893        3378919</span><br><span class="line">             Total:       1304        3470940</span><br><span class="line"></span><br><span class="line">Total number of all variants: 4,722</span><br><span class="line">Total bases affected by all variants: 8.61 Mbp</span><br><span class="line">Total number of structural variants: 4,722</span><br><span class="line">Total bases affected by structural variants: 8.61 Mbp</span><br></pre></td></tr></table></figure>
-
-<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li><a href="http://assemblytics.com/">http://assemblytics.com/</a></li>
-<li><a href="https://github.com/marianattestad/assemblytics">https://github.com/marianattestad/assemblytics</a></li>
-<li>paper: <a href="https://academic.oup.com/bioinformatics/article/32/19/3021/2196631">https://academic.oup.com/bioinformatics/article/32/19/3021/2196631</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bioinfo</category>
-        <category>variation</category>
-        <category>structural variation</category>
-      </categories>
-      <tags>
-        <tag>biosoft</tag>
-        <tag>structural variation</tag>
-        <tag>SV</tag>
-        <tag>insertion</tag>
-        <tag>deletion</tag>
-        <tag>Indel</tag>
-        <tag>tandem expansion</tag>
-        <tag>tandem contraction</tag>
-        <tag>repeat expansion</tag>
-        <tag>repeat contraction</tag>
-        <tag>Assemblytics</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>结构变异分析软件：MUM&amp;Co</title>
-    <url>/2022/07/17/bioinfo_variation_SV_MUMandCo/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=18031133&auto=1&height=32"></iframe></div>
-
-<h1 id="1-MUM-amp-Co简介"><a href="#1-MUM-amp-Co简介" class="headerlink" title="1. MUM&amp;Co简介"></a>1. MUM&amp;Co简介</h1><p>MUM&amp;Co是用于<strong>两个基因组间</strong>的<strong>结构变异（structural variant，SV）</strong>检测分析的软件，基于MUMmer(v4)的nucmer比对结果检测全基因组范围内的插入，缺失，串联重复，倒位和易位等结构变异。</p>
-<p>2020年发表在Bioinformatics。</p>
-<h1 id="2-MUM-amp-Co原理"><a href="#2-MUM-amp-Co原理" class="headerlink" title="2. MUM&amp;Co原理"></a>2. MUM&amp;Co原理</h1><p>MUMandCo调用MUMmer的nucmer模块做互换的全基因组比对（whole-genome alignmetn，WGA），delta-filter模块做全局（g-）和多对多（m-）的过滤，show-coords模块解析坐标。</p>
-<ol>
-<li>选出与参考基因组有最精确、非重叠的alignments的query contigs。其余的alignment都不考虑。</li>
-<li>利用这些alignments统计超过50bp的SV的类型和特征。</li>
-<li>全局（g-）比对 基于多个contig-chromosome pairings来检测易位（translocation）片段，基于alignment orientation检测大的倒位（inversions），基于alignment gaps检测可能的插入缺失（insertion and deletion），利用互换的比对，gaps会在reference和query基因组间同时被考虑。</li>
-<li>多对多（m-）比对用于检测潜在的倒位（inversions）和重复（duplications）。</li>
-<li>用全局（g-）和多对多（m-）比对的过滤去除假阳性。</li>
-<li>参数<code>-b</code>可以调用BLAST来检测插入和缺失是重复的（mobile）还是新的（novel）。</li>
-<li>生成保存了reference和query基因组的SV的类型、坐标等细节的tsv文件。</li>
-</ol>
-<h1 id="3-MUM-amp-Co可以检测的内容"><a href="#3-MUM-amp-Co可以检测的内容" class="headerlink" title="3. MUM&amp;Co可以检测的内容"></a>3. MUM&amp;Co可以检测的内容</h1><ul>
-<li>插入（insertion）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
-<li>缺失（deletion）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
-<li>串联重复复制（tandem duplication）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
-<li>串联重复收缩（tandem contraction）：&gt;&#x3D;50bp &amp; &lt;&#x3D;150kb</li>
-<li>倒位（inversion）：&gt;&#x3D;1kb</li>
-<li>易位（translocation）：&gt;&#x3D;10kb</li>
-</ul>
-<h1 id="4-MUM-amp-Co安装"><a href="#4-MUM-amp-Co安装" class="headerlink" title="4. MUM&amp;Co安装"></a>4. MUM&amp;Co安装</h1><ul>
-<li>MUMandCo是个bash脚本，下载后赋予执行权限即可使用。</li>
-<li>依赖的软件包括MUMmer(v4)和samtools，如果使用<code>-b</code>参数则还需要blast。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/SAMtoBAM/MUMandCo.git</span><br><span class="line">chmod 711 ./MUMandCo/mumandco_v3.8.sh</span><br></pre></td></tr></table></figure>
-
-<h1 id="5-MUM-amp-Co使用"><a href="#5-MUM-amp-Co使用" class="headerlink" title="5. MUM&amp;Co使用"></a>5. MUM&amp;Co使用</h1><ol>
-<li>运行</li>
-</ol>
-<p><code>bash mumandco_v3.8.sh -r genome.fa -q query.fa -g 125500000 -t 24 -b -o out</code></p>
-<ol start="2">
-<li>参数</li>
-</ol>
-<ul>
-<li>-r genome.fa：参考基因组。注意参考基因组和检测基因组的选择不同，结果会有一些差异。</li>
-<li>-q query.fa：被检测的基因组</li>
-<li>-g 125500000：参考基因组大小，单位是bp</li>
-<li>-t 24：线程24，默认1</li>
-<li>-b：添加blast选项用来确认插入&#x2F;缺失是mobile&#x2F;repetitive还是novel。我的理解是mobile&#x2F;repetitive代表别的地方有这个插入&#x2F;缺失，novel代表是一个新产生的插入&#x2F;缺失。</li>
-<li>-ml：alignments的最小长度；默认50bp</li>
-<li>-o out：输出文件的前缀；默认是mumandco</li>
-</ul>
-<ol start="3">
-<li>时间</li>
-</ol>
-<ul>
-<li>两个都是约300Mbp的基因组的SV分析，用了24线程，运行总耗时5小时。</li>
-</ul>
-<h1 id="6-MUM-amp-Co结果"><a href="#6-MUM-amp-Co结果" class="headerlink" title="6. MUM&amp;Co结果"></a>6. MUM&amp;Co结果</h1><ol>
-<li>out.summary.txt</li>
-</ol>
-<ul>
-<li>总结SV的类型和数量的文件</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">mumandco</span><br><span class="line">Total_SVs	8252</span><br><span class="line">Deletions	3337</span><br><span class="line">Insertions	4363</span><br><span class="line">Duplications	44</span><br><span class="line">Contractions	8</span><br><span class="line">Inversions	144</span><br><span class="line">Translocations	356</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>out.SVs_all.tsv</li>
+<li>out.SVs_all.tsv</li>
 </ol>
 <ul>
 <li>保存了所有检测到的SVs的每一个SV的类型、坐标、长度等详细信息。</li>
@@ -8704,160 +8344,55 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>把简历部署到网站上</title>
-    <url>/2022/07/31/blog_hexo+github_setting/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5284273&auto=1&height=32"></iframe><music URL></div>
+    <title>hexo建站，github.io发布，多终端同步</title>
+    <url>/2018/06/05/blog_hexo+github/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=330 height=86 src="//music.163.com/outchain/player?type=2&id=418550511&auto=1&height=66"></iframe></div>
 
-<p>在根据博客<a href="https://yanzhongsino.github.io/2018/06/05/blog_hexo.github/">hexo建站，github.io发布，多终端同步</a>配置了hexo网站（使用next主题）的基础上，记录了添加自定义网页（包括简历、about me、404等页面）的设置技巧。</p>
-<h1 id="1-添加自定义网页"><a href="#1-添加自定义网页" class="headerlink" title="1. 添加自定义网页"></a>1. 添加自定义网页</h1><p>添加自定义网页，自定义网页包括简历、404、About页面等，这里以添加<strong>简历</strong>为例。</p>
-<h2 id="1-1-制作简历"><a href="#1-1-制作简历" class="headerlink" title="1.1. 制作简历"></a>1.1. 制作简历</h2><ol>
-<li>现状</li>
-</ol>
-<ul>
-<li>网上的博客在线简历相关教程大多是开发了一个专门用于简历展示的博客主题，需要博客使用这个主题以实现制作简历和在线显示的目的。</li>
-<li>但我的博客用的是hexo的next主题，不想换。</li>
-<li>所以最后决定用模板的html文件自己修改制作简历html。</li>
-</ul>
-<ol start="2">
-<li>操作</li>
-</ol>
 <ul>
-<li>在网上找喜欢的简历模板，提取html文件。</li>
-<li>比如我的简历参考的是这个模板：<a href="https://github.com/mtics/hexo-mtics-resume">https://github.com/mtics/hexo-mtics-resume</a></li>
-<li>在模板的demo：<a href="https://mtics.netlify.app/%EF%BC%8C%E5%8F%B3%E9%94%AE**%E6%9F%A5%E7%9C%8B%E7%BD%91%E9%A1%B5%E6%BA%90%E4%BB%A3%E7%A0%81**%EF%BC%8C%E7%84%B6%E5%90%8E%E5%A4%8D%E5%88%B6%E5%92%8C%E4%BF%9D%E5%AD%98html%E4%BB%A3%E7%A0%81%E5%88%B0index.html%E3%80%82">https://mtics.netlify.app/，右键**查看网页源代码**，然后复制和保存html代码到index.html。</a></li>
-<li>修改index.html的信息为自己的简历内容，即可使用。</li>
-<li>同时为了达到同样的渲染效果，把模板<a href="https://github.com/mtics/hexo-mtics-resume/tree/master/source%E9%87%8C%E7%9A%84%E4%B8%89%E4%B8%AAcss%E6%96%87%E4%BB%B6%E4%B8%8B%E8%BD%BD%E4%B8%8B%E6%9D%A5%EF%BC%8Cimg/avatar.jpg%E6%96%87%E4%BB%B6%E6%94%B9%E6%88%90%E8%87%AA%E5%B7%B1%E7%9A%84%E5%A4%B4%E5%83%8F%E7%85%A7%E7%89%87%E3%80%82">https://github.com/mtics/hexo-mtics-resume/tree/master/source里的三个css文件下载下来，img/avatar.jpg文件改成自己的头像照片。</a></li>
+<li><strong>markdown</strong>是一种适用撰写文本的语法和文件格式，后缀是.md</li>
+<li><strong>hexo</strong>是把写的markdown文件生成blog网站的工具，hexo是不仅是博客引擎驱动，还是一个快速、简洁高效的博客框架，可生成静态网页。</li>
+<li><strong>git</strong>是目前世界上最先进的分布式版本控制系统，用于对开发程序或者其他需要长期追踪版本变更的项目&#x2F;文件的版本控制。</li>
+<li><strong>github</strong>提供网络站点进行blog的展示和备份</li>
 </ul>
-<h2 id="1-2-添加简历"><a href="#1-2-添加简历" class="headerlink" title="1.2. 添加简历"></a>1.2. 添加简历</h2><ol>
-<li>在hexo&#x2F;source目录下创建文件夹，例如resume。（如果添加about页面就创建about文件夹）</li>
-<li>然后把制作好的简历网页文件index.html放在resume文件夹下。</li>
-<li>简历引用了的css或js文件和图片文件，也放在resume文件夹下。</li>
+<p>github pages是github网站的一个功能，专门用于管理用户自行编写的静态网页，提供静态网页存储空间，许多人把它用于搭建独立博客。</p>
+<p>github pages需要相应的博客引擎驱动，主流的是<a href="https://jekyllrb.com/">jekyllrb</a>和<a href="https://hexo.io/">hexo</a>。</p>
+<p><strong>部署和发布blog有很多方法。这里介绍的是markdown撰写博客，hexo建立博客网站，通过git管理博客网站，发布博客网站到github平台和多终端同步的操作。</strong></p>
+<p>部署本地blog的写作环境（git+hexo），并用github.io网站发布blog</p>
+<ol>
+<li>git和github的配置<br>新终端搭建博客撰写环境都需要配置</li>
+<li>hexo的安装和配置<br>多终端同步和单终端有一点差别，建议一开始就配置多终端同步并备份到githbu网站，一步到位，避免博客在单终端丢失。</li>
+<li>撰写与发布blog</li>
+<li>github两个分支实现多终端同步</li>
+<li>日常blog撰写和备份操作</li>
 </ol>
-<h2 id="1-3-跳过渲染"><a href="#1-3-跳过渲染" class="headerlink" title="1.3. 跳过渲染"></a>1.3. 跳过渲染</h2><p>因为hexo应用的主题给每个网页都进行相同的渲染，所以需要对自定义网页设置跳过默认的渲染，有以下两种方法。</p>
+<h1 id="1-git和github的配置"><a href="#1-git和github的配置" class="headerlink" title="1. git和github的配置"></a>1. git和github的配置</h1><p><em>notes：username和useremail替换成自己的github用户名和邮箱。</em></p>
 <ol>
-<li>在index.html设置</li>
+<li><p>下载安装<a href="https://git-scm.com/">git</a></p>
+</li>
+<li><p>注册github网站</p>
+<p>  注册github网站，并创建一个名为username.github.io的仓库</p>
+<p>  这个特殊的名称直接关联了gitpages，gitpages服务器会自动把上传到这个仓库的文档进行部署发布，并可以通过分配的域名<a href="https://username.github.io/%E5%AE%9E%E7%8E%B0%E5%8D%9A%E5%AE%A2%E7%9A%84%E8%AE%BF%E9%97%AE%E3%80%82">https://username.github.io/实现博客的访问。</a></p>
+</li>
+<li><p>git配置和生成SSH key密钥对</p>
+<p>  git配置：打开git bash（任意位置右键选择git bash here)</p>
+<p>  运行<code>git config --global user.name username</code>和<code>git config --global user.email useremail</code></p>
+<p>  cd ~进入用户主目录，运行<code>ssh-keygen -t rsa -C useremail@example.com</code>，一路回车，完成后会在用户主目录（C&#x2F;Users&#x2F;you&#x2F;)下生成.ssh目录，里面有id_rsa和id_rsa.pub两个文件，即SSH key密钥对，id_rsa是私钥，需要保密，id_rsa.pub是公钥，可分享。</p>
+</li>
+<li><p>github添加终端的公钥</p>
+<p>  打开网页，登录github，打开settings-&gt;SSH and GPG keys，点击new SSH key，填上任意title，在key文本框粘贴公钥，即id_rsa.pub文件的内容，添加。</p>
+</li>
+<li><p>验证与github的添加</p>
+<p>  在git bash下运行<code>ssh -T git@github.com</code>。<br>  输出You’ve successfully suthenicated.则代表github账户成功授权你当前使用的终端，本机可连接上github了。</p>
+</li>
 </ol>
-<ul>
-<li>在网页文件index.html的开头添加下面代码设置跳过渲染的指令，这样通过<code>hexo g</code>生成网页时会跳过index.html文件，不受hexo主题渲染，而完全是一个独立的网页。</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">---</span><br><span class="line">layout: false</span><br><span class="line">---</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>在_config.yml文件中设置</li>
-</ol>
-<p>在hexo目录的_config.yml文件设置skip_render参数来设置不渲染的文件&#x2F;文件夹。</p>
-<ul>
-<li>屏蔽指定文件：<code>skip_render：./resume/index.html</code></li>
-<li>屏蔽指定文件夹的所有文件：<code>skip_render: ./resume/*</code></li>
-<li>屏蔽指定文件夹所有文件包括子文件夹和子文件夹下的文件：<code>skip_render: ./resume/**</code></li>
-<li>屏蔽多个路径：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">skip_render:</span><br><span class="line">    - ./resume/index.html</span><br><span class="line">    - ./resume/image.png</span><br></pre></td></tr></table></figure>
-
-<h2 id="1-4-设置主页显示"><a href="#1-4-设置主页显示" class="headerlink" title="1.4. 设置主页显示"></a>1.4. 设置主页显示</h2><p>在博客首页的菜单栏中增加简历项的设置。</p>
-<ol>
-<li>在主题下的设置文件中<code>./themes/next/_config.yml</code>文件中的menu项中添加简历行<code>resume: /resume/index.html || fa fa-file-user</code>，这样可以在博客首页的菜单栏中增加简历项。</li>
-<li>其中<code>resume</code>会显示在博客主页</li>
-<li><code>/resume/index.html</code>指定自定义网页文件</li>
-<li><code>fa fa-file-user</code>定义图标，可以在<a href="https://fontawesome.com/icons?from=io%E9%80%89%E6%8B%A9%E5%90%88%E9%80%82%E7%9A%84%E5%9B%BE%E6%A0%87%E4%BD%BF%E7%94%A8%E3%80%82">https://fontawesome.com/icons?from=io选择合适的图标使用。</a></li>
-</ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">menu:</span><br><span class="line">  home: / || fa fa-home</span><br><span class="line">  about: /about/ || fa fa-user</span><br><span class="line">  tags: /tags/ || fa fa-tags</span><br><span class="line">  categories: /categories/ || fa fa-th</span><br><span class="line">  archives: /archives/ || fa fa-archive</span><br><span class="line">  #schedule: /schedule/ || fa fa-calendar</span><br><span class="line">  #sitemap: /sitemap.xml || fa fa-sitemap</span><br><span class="line">  #commonweal: /404/ || fa fa-heartbeat</span><br><span class="line">  resume: /resume/index.html || fa fa-file-user</span><br></pre></td></tr></table></figure>
-
-<h2 id="1-5-简历成品"><a href="#1-5-简历成品" class="headerlink" title="1.5. 简历成品"></a>1.5. 简历成品</h2><p>最后的效果可以参考我的简历：<a href="https://yanzhongsino.github.io/resume/index.html">https://yanzhongsino.github.io/resume/index.html</a></p>
-<h1 id="2-添加about页面"><a href="#2-添加about页面" class="headerlink" title="2. 添加about页面"></a>2. 添加about页面</h1><p>与上面的添加简历页面类似：</p>
-<ol>
-<li>在hexo&#x2F;source目录下创建文件夹about</li>
-<li>在about文件夹下创建index.md文件写入about显示内容</li>
-<li>如果使用hexo的主题显示，则无需跳过渲染</li>
-<li>把主题下的设置文件中<code>./themes/next/_config.yml</code>文件中的menu项中的about信息行的注释符#删除，表示在博客主页显示about项进入页面</li>
-</ol>
-<h1 id="3-添加404页面"><a href="#3-添加404页面" class="headerlink" title="3. 添加404页面"></a>3. 添加404页面</h1><p>404页面是在域名下访问不存在的地址时出现的页面。</p>
-<ol>
-<li>在博客主题的source下（比如我的.&#x2F;themes&#x2F;next&#x2F;source&#x2F;）创建404.html文件</li>
-<li>在404.html文件中写入想要在404页面展示的内容</li>
-<li>推荐把腾讯公益404页面的代码写入404页面，为传播失踪儿童信息贡献一点微薄之力。</li>
-</ol>
-<ul>
-<li>下面的**homePageUrl&#x3D;”<a href="https://yanzhongsino.github.io&quot;**用于设置网站首页地址,需要改成自己的网站首页网址./">https://yanzhongsino.github.io&quot;**用于设置网站首页地址，需要改成自己的网站首页网址。</a></li>
-<li>**homePageName&#x3D;”返回主页（您可以进入首页继续搜索）”**设置引导词，可以替换成引导用户回到网站首页的引导语。</li>
-<li>腾讯公益404页面的js地址可能会更新，可以以腾讯官方代码：<a href="https://news.qq.com/404/%EF%BC%8C%E5%8F%91%E5%B8%83%E7%9A%84%E4%B8%BA%E5%87%86%E3%80%82">https://news.qq.com/404/，发布的为准。</a></li>
-<li>404.html示例：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;!DOCTYPE HTML&gt;</span><br><span class="line">&lt;html&gt;</span><br><span class="line">&lt;head&gt;</span><br><span class="line">  &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html;charset=utf-8;&quot;/&gt;</span><br><span class="line">  &lt;meta http-equiv=&quot;X-UA-Compatible&quot; content=&quot;IE=edge,chrome=1&quot; /&gt;</span><br><span class="line">  &lt;meta name=&quot;robots&quot; content=&quot;all&quot; /&gt;</span><br><span class="line">  &lt;meta name=&quot;robots&quot; content=&quot;index,follow&quot;/&gt;</span><br><span class="line">  &lt;link rel=&quot;stylesheet&quot; type=&quot;text/css&quot; href=&quot;https://qzone.qq.com/gy/404/style/404style.css&quot;&gt;</span><br><span class="line">&lt;/head&gt;</span><br><span class="line">&lt;body&gt;</span><br><span class="line">  &lt;script type=&quot;text/plain&quot; src=&quot;http://www.qq.com/404/search_children.js&quot;</span><br><span class="line">          charset=&quot;utf-8&quot; homePageUrl=&quot;https://yanzhongsino.github.io&quot;</span><br><span class="line">          homePageName=&quot;返回主页（您可以进入首页继续搜索）&quot;&gt;</span><br><span class="line">  &lt;/script&gt;</span><br><span class="line">  &lt;script src=&quot;https://qzone.qq.com/gy/404/data.js&quot; charset=&quot;utf-8&quot;&gt;&lt;/script&gt;</span><br><span class="line">  &lt;script src=&quot;https://qzone.qq.com/gy/404/page.js&quot; charset=&quot;utf-8&quot;&gt;&lt;/script&gt;</span><br><span class="line">&lt;/body&gt;</span><br><span class="line">&lt;/html&gt;</span><br></pre></td></tr></table></figure>
-
-<ol>
-<li>接着正常部署博客即可。</li>
-<li>测试404页面。在网页域名下输入一个不存在的地址（比如<a href="https://yanzhongsino.github.io/111%EF%BC%89%EF%BC%8C%E7%9C%8B404%E9%A1%B5%E9%9D%A2%E6%98%AF%E5%90%A6%E6%9B%B4%E6%96%B0%E6%88%90%E5%8A%9F%E3%80%82">https://yanzhongsino.github.io/111），看404页面是否更新成功。</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>blog</category>
-      </categories>
-      <tags>
-        <tag>blog</tag>
-        <tag>hexo</tag>
-        <tag>github</tag>
-        <tag>markdown</tag>
-        <tag>resume</tag>
-        <tag>readme</tag>
-        <tag>404 page</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>hexo建站，github.io发布，多终端同步</title>
-    <url>/2018/06/05/blog_hexo+github/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=330 height=86 src="//music.163.com/outchain/player?type=2&id=418550511&auto=1&height=66"></iframe></div>
-
-<ul>
-<li><strong>markdown</strong>是一种适用撰写文本的语法和文件格式，后缀是.md</li>
-<li><strong>hexo</strong>是把写的markdown文件生成blog网站的工具，hexo是不仅是博客引擎驱动，还是一个快速、简洁高效的博客框架，可生成静态网页。</li>
-<li><strong>git</strong>是目前世界上最先进的分布式版本控制系统，用于对开发程序或者其他需要长期追踪版本变更的项目&#x2F;文件的版本控制。</li>
-<li><strong>github</strong>提供网络站点进行blog的展示和备份</li>
-</ul>
-<p>github pages是github网站的一个功能，专门用于管理用户自行编写的静态网页，提供静态网页存储空间，许多人把它用于搭建独立博客。</p>
-<p>github pages需要相应的博客引擎驱动，主流的是<a href="https://jekyllrb.com/">jekyllrb</a>和<a href="https://hexo.io/">hexo</a>。</p>
-<p><strong>部署和发布blog有很多方法。这里介绍的是markdown撰写博客，hexo建立博客网站，通过git管理博客网站，发布博客网站到github平台和多终端同步的操作。</strong></p>
-<p>部署本地blog的写作环境（git+hexo），并用github.io网站发布blog</p>
-<ol>
-<li>git和github的配置<br>新终端搭建博客撰写环境都需要配置</li>
-<li>hexo的安装和配置<br>多终端同步和单终端有一点差别，建议一开始就配置多终端同步并备份到githbu网站，一步到位，避免博客在单终端丢失。</li>
-<li>撰写与发布blog</li>
-<li>github两个分支实现多终端同步</li>
-<li>日常blog撰写和备份操作</li>
-</ol>
-<h1 id="1-git和github的配置"><a href="#1-git和github的配置" class="headerlink" title="1. git和github的配置"></a>1. git和github的配置</h1><p><em>notes：username和useremail替换成自己的github用户名和邮箱。</em></p>
-<ol>
-<li><p>下载安装<a href="https://git-scm.com/">git</a></p>
-</li>
-<li><p>注册github网站</p>
-<p>  注册github网站，并创建一个名为username.github.io的仓库</p>
-<p>  这个特殊的名称直接关联了gitpages，gitpages服务器会自动把上传到这个仓库的文档进行部署发布，并可以通过分配的域名<a href="https://username.github.io/%E5%AE%9E%E7%8E%B0%E5%8D%9A%E5%AE%A2%E7%9A%84%E8%AE%BF%E9%97%AE%E3%80%82">https://username.github.io/实现博客的访问。</a></p>
-</li>
-<li><p>git配置和生成SSH key密钥对</p>
-<p>  git配置：打开git bash（任意位置右键选择git bash here)</p>
-<p>  运行<code>git config --global user.name username</code>和<code>git config --global user.email useremail</code></p>
-<p>  cd ~进入用户主目录，运行<code>ssh-keygen -t rsa -C useremail@example.com</code>，一路回车，完成后会在用户主目录（C&#x2F;Users&#x2F;you&#x2F;)下生成.ssh目录，里面有id_rsa和id_rsa.pub两个文件，即SSH key密钥对，id_rsa是私钥，需要保密，id_rsa.pub是公钥，可分享。</p>
-</li>
-<li><p>github添加终端的公钥</p>
-<p>  打开网页，登录github，打开settings-&gt;SSH and GPG keys，点击new SSH key，填上任意title，在key文本框粘贴公钥，即id_rsa.pub文件的内容，添加。</p>
-</li>
-<li><p>验证与github的添加</p>
-<p>  在git bash下运行<code>ssh -T git@github.com</code>。<br>  输出You’ve successfully suthenicated.则代表github账户成功授权你当前使用的终端，本机可连接上github了。</p>
-</li>
-</ol>
-<h1 id="2-hexo的安装和配置"><a href="#2-hexo的安装和配置" class="headerlink" title="2. hexo的安装和配置"></a>2. hexo的安装和配置</h1><ol>
-<li><p>下载安装<a href="https://nodejs.org/zh-cn/">node.js</a></p>
-<p> 使用git bash或者cmd终端检查node的安装，<code>node -v</code>和<code>npm -v</code>出现版本号即node和npm 安装成功</p>
-</li>
-<li><p>安装hexo</p>
-<p><code>npm install hexo-cli -g</code>命令安装hexo-cli（hexo的cli命令行模块）</p>
-</li>
-<li><p>初始化目录<code>hexo init</code><br> 选定位置通过git bash进入，创建hexo文件夹并进入hexo，hexo init命令初始化hexo文件夹。<br> 即生成一系列建站需要的文件：</p>
+<h1 id="2-hexo的安装和配置"><a href="#2-hexo的安装和配置" class="headerlink" title="2. hexo的安装和配置"></a>2. hexo的安装和配置</h1><ol>
+<li><p>下载安装<a href="https://nodejs.org/zh-cn/">node.js</a></p>
+<p> 使用git bash或者cmd终端检查node的安装，<code>node -v</code>和<code>npm -v</code>出现版本号即node和npm 安装成功</p>
+</li>
+<li><p>安装hexo</p>
+<p><code>npm install hexo-cli -g</code>命令安装hexo-cli（hexo的cli命令行模块）</p>
+</li>
+<li><p>初始化目录<code>hexo init</code><br> 选定位置通过git bash进入，创建hexo文件夹并进入hexo，hexo init命令初始化hexo文件夹。<br> 即生成一系列建站需要的文件：</p>
 <ul>
 <li>_config.yml：站点配置文件</li>
 <li>package.json：说明使用哪些包，npm install参考这个文件安装</li>
@@ -9081,6 +8616,111 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>deploy website</tag>
       </tags>
   </entry>
+  <entry>
+    <title>把简历部署到网站上</title>
+    <url>/2022/07/31/blog_hexo+github_setting/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=5284273&auto=1&height=32"></iframe><music URL></div>
+
+<p>在根据博客<a href="https://yanzhongsino.github.io/2018/06/05/blog_hexo.github/">hexo建站，github.io发布，多终端同步</a>配置了hexo网站（使用next主题）的基础上，记录了添加自定义网页（包括简历、about me、404等页面）的设置技巧。</p>
+<h1 id="1-添加自定义网页"><a href="#1-添加自定义网页" class="headerlink" title="1. 添加自定义网页"></a>1. 添加自定义网页</h1><p>添加自定义网页，自定义网页包括简历、404、About页面等，这里以添加<strong>简历</strong>为例。</p>
+<h2 id="1-1-制作简历"><a href="#1-1-制作简历" class="headerlink" title="1.1. 制作简历"></a>1.1. 制作简历</h2><ol>
+<li>现状</li>
+</ol>
+<ul>
+<li>网上的博客在线简历相关教程大多是开发了一个专门用于简历展示的博客主题，需要博客使用这个主题以实现制作简历和在线显示的目的。</li>
+<li>但我的博客用的是hexo的next主题，不想换。</li>
+<li>所以最后决定用模板的html文件自己修改制作简历html。</li>
+</ul>
+<ol start="2">
+<li>操作</li>
+</ol>
+<ul>
+<li>在网上找喜欢的简历模板，提取html文件。</li>
+<li>比如我的简历参考的是这个模板：<a href="https://github.com/mtics/hexo-mtics-resume">https://github.com/mtics/hexo-mtics-resume</a></li>
+<li>在模板的demo：<a href="https://mtics.netlify.app/%EF%BC%8C%E5%8F%B3%E9%94%AE**%E6%9F%A5%E7%9C%8B%E7%BD%91%E9%A1%B5%E6%BA%90%E4%BB%A3%E7%A0%81**%EF%BC%8C%E7%84%B6%E5%90%8E%E5%A4%8D%E5%88%B6%E5%92%8C%E4%BF%9D%E5%AD%98html%E4%BB%A3%E7%A0%81%E5%88%B0index.html%E3%80%82">https://mtics.netlify.app/，右键**查看网页源代码**，然后复制和保存html代码到index.html。</a></li>
+<li>修改index.html的信息为自己的简历内容，即可使用。</li>
+<li>同时为了达到同样的渲染效果，把模板<a href="https://github.com/mtics/hexo-mtics-resume/tree/master/source%E9%87%8C%E7%9A%84%E4%B8%89%E4%B8%AAcss%E6%96%87%E4%BB%B6%E4%B8%8B%E8%BD%BD%E4%B8%8B%E6%9D%A5%EF%BC%8Cimg/avatar.jpg%E6%96%87%E4%BB%B6%E6%94%B9%E6%88%90%E8%87%AA%E5%B7%B1%E7%9A%84%E5%A4%B4%E5%83%8F%E7%85%A7%E7%89%87%E3%80%82">https://github.com/mtics/hexo-mtics-resume/tree/master/source里的三个css文件下载下来，img/avatar.jpg文件改成自己的头像照片。</a></li>
+</ul>
+<h2 id="1-2-添加简历"><a href="#1-2-添加简历" class="headerlink" title="1.2. 添加简历"></a>1.2. 添加简历</h2><ol>
+<li>在hexo&#x2F;source目录下创建文件夹，例如resume。（如果添加about页面就创建about文件夹）</li>
+<li>然后把制作好的简历网页文件index.html放在resume文件夹下。</li>
+<li>简历引用了的css或js文件和图片文件，也放在resume文件夹下。</li>
+</ol>
+<h2 id="1-3-跳过渲染"><a href="#1-3-跳过渲染" class="headerlink" title="1.3. 跳过渲染"></a>1.3. 跳过渲染</h2><p>因为hexo应用的主题给每个网页都进行相同的渲染，所以需要对自定义网页设置跳过默认的渲染，有以下两种方法。</p>
+<ol>
+<li>在index.html设置</li>
+</ol>
+<ul>
+<li>在网页文件index.html的开头添加下面代码设置跳过渲染的指令，这样通过<code>hexo g</code>生成网页时会跳过index.html文件，不受hexo主题渲染，而完全是一个独立的网页。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">---</span><br><span class="line">layout: false</span><br><span class="line">---</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>在_config.yml文件中设置</li>
+</ol>
+<p>在hexo目录的_config.yml文件设置skip_render参数来设置不渲染的文件&#x2F;文件夹。</p>
+<ul>
+<li>屏蔽指定文件：<code>skip_render：./resume/index.html</code></li>
+<li>屏蔽指定文件夹的所有文件：<code>skip_render: ./resume/*</code></li>
+<li>屏蔽指定文件夹所有文件包括子文件夹和子文件夹下的文件：<code>skip_render: ./resume/**</code></li>
+<li>屏蔽多个路径：</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">skip_render:</span><br><span class="line">    - ./resume/index.html</span><br><span class="line">    - ./resume/image.png</span><br></pre></td></tr></table></figure>
+
+<h2 id="1-4-设置主页显示"><a href="#1-4-设置主页显示" class="headerlink" title="1.4. 设置主页显示"></a>1.4. 设置主页显示</h2><p>在博客首页的菜单栏中增加简历项的设置。</p>
+<ol>
+<li>在主题下的设置文件中<code>./themes/next/_config.yml</code>文件中的menu项中添加简历行<code>resume: /resume/index.html || fa fa-file-user</code>，这样可以在博客首页的菜单栏中增加简历项。</li>
+<li>其中<code>resume</code>会显示在博客主页</li>
+<li><code>/resume/index.html</code>指定自定义网页文件</li>
+<li><code>fa fa-file-user</code>定义图标，可以在<a href="https://fontawesome.com/icons?from=io%E9%80%89%E6%8B%A9%E5%90%88%E9%80%82%E7%9A%84%E5%9B%BE%E6%A0%87%E4%BD%BF%E7%94%A8%E3%80%82">https://fontawesome.com/icons?from=io选择合适的图标使用。</a></li>
+</ol>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">menu:</span><br><span class="line">  home: / || fa fa-home</span><br><span class="line">  about: /about/ || fa fa-user</span><br><span class="line">  tags: /tags/ || fa fa-tags</span><br><span class="line">  categories: /categories/ || fa fa-th</span><br><span class="line">  archives: /archives/ || fa fa-archive</span><br><span class="line">  #schedule: /schedule/ || fa fa-calendar</span><br><span class="line">  #sitemap: /sitemap.xml || fa fa-sitemap</span><br><span class="line">  #commonweal: /404/ || fa fa-heartbeat</span><br><span class="line">  resume: /resume/index.html || fa fa-file-user</span><br></pre></td></tr></table></figure>
+
+<h2 id="1-5-简历成品"><a href="#1-5-简历成品" class="headerlink" title="1.5. 简历成品"></a>1.5. 简历成品</h2><p>最后的效果可以参考我的简历：<a href="https://yanzhongsino.github.io/resume/index.html">https://yanzhongsino.github.io/resume/index.html</a></p>
+<h1 id="2-添加about页面"><a href="#2-添加about页面" class="headerlink" title="2. 添加about页面"></a>2. 添加about页面</h1><p>与上面的添加简历页面类似：</p>
+<ol>
+<li>在hexo&#x2F;source目录下创建文件夹about</li>
+<li>在about文件夹下创建index.md文件写入about显示内容</li>
+<li>如果使用hexo的主题显示，则无需跳过渲染</li>
+<li>把主题下的设置文件中<code>./themes/next/_config.yml</code>文件中的menu项中的about信息行的注释符#删除，表示在博客主页显示about项进入页面</li>
+</ol>
+<h1 id="3-添加404页面"><a href="#3-添加404页面" class="headerlink" title="3. 添加404页面"></a>3. 添加404页面</h1><p>404页面是在域名下访问不存在的地址时出现的页面。</p>
+<ol>
+<li>在博客主题的source下（比如我的.&#x2F;themes&#x2F;next&#x2F;source&#x2F;）创建404.html文件</li>
+<li>在404.html文件中写入想要在404页面展示的内容</li>
+<li>推荐把腾讯公益404页面的代码写入404页面，为传播失踪儿童信息贡献一点微薄之力。</li>
+</ol>
+<ul>
+<li>下面的**homePageUrl&#x3D;”<a href="https://yanzhongsino.github.io&quot;**用于设置网站首页地址,需要改成自己的网站首页网址./">https://yanzhongsino.github.io&quot;**用于设置网站首页地址，需要改成自己的网站首页网址。</a></li>
+<li>**homePageName&#x3D;”返回主页（您可以进入首页继续搜索）”**设置引导词，可以替换成引导用户回到网站首页的引导语。</li>
+<li>腾讯公益404页面的js地址可能会更新，可以以腾讯官方代码：<a href="https://news.qq.com/404/%EF%BC%8C%E5%8F%91%E5%B8%83%E7%9A%84%E4%B8%BA%E5%87%86%E3%80%82">https://news.qq.com/404/，发布的为准。</a></li>
+<li>404.html示例：</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&lt;!DOCTYPE HTML&gt;</span><br><span class="line">&lt;html&gt;</span><br><span class="line">&lt;head&gt;</span><br><span class="line">  &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html;charset=utf-8;&quot;/&gt;</span><br><span class="line">  &lt;meta http-equiv=&quot;X-UA-Compatible&quot; content=&quot;IE=edge,chrome=1&quot; /&gt;</span><br><span class="line">  &lt;meta name=&quot;robots&quot; content=&quot;all&quot; /&gt;</span><br><span class="line">  &lt;meta name=&quot;robots&quot; content=&quot;index,follow&quot;/&gt;</span><br><span class="line">  &lt;link rel=&quot;stylesheet&quot; type=&quot;text/css&quot; href=&quot;https://qzone.qq.com/gy/404/style/404style.css&quot;&gt;</span><br><span class="line">&lt;/head&gt;</span><br><span class="line">&lt;body&gt;</span><br><span class="line">  &lt;script type=&quot;text/plain&quot; src=&quot;http://www.qq.com/404/search_children.js&quot;</span><br><span class="line">          charset=&quot;utf-8&quot; homePageUrl=&quot;https://yanzhongsino.github.io&quot;</span><br><span class="line">          homePageName=&quot;返回主页（您可以进入首页继续搜索）&quot;&gt;</span><br><span class="line">  &lt;/script&gt;</span><br><span class="line">  &lt;script src=&quot;https://qzone.qq.com/gy/404/data.js&quot; charset=&quot;utf-8&quot;&gt;&lt;/script&gt;</span><br><span class="line">  &lt;script src=&quot;https://qzone.qq.com/gy/404/page.js&quot; charset=&quot;utf-8&quot;&gt;&lt;/script&gt;</span><br><span class="line">&lt;/body&gt;</span><br><span class="line">&lt;/html&gt;</span><br></pre></td></tr></table></figure>
+
+<ol>
+<li>接着正常部署博客即可。</li>
+<li>测试404页面。在网页域名下输入一个不存在的地址（比如<a href="https://yanzhongsino.github.io/111%EF%BC%89%EF%BC%8C%E7%9C%8B404%E9%A1%B5%E9%9D%A2%E6%98%AF%E5%90%A6%E6%9B%B4%E6%96%B0%E6%88%90%E5%8A%9F%E3%80%82">https://yanzhongsino.github.io/111），看404页面是否更新成功。</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>blog</category>
+      </categories>
+      <tags>
+        <tag>blog</tag>
+        <tag>hexo</tag>
+        <tag>github</tag>
+        <tag>markdown</tag>
+        <tag>resume</tag>
+        <tag>readme</tag>
+        <tag>404 page</tag>
+      </tags>
+  </entry>
   <entry>
     <title>hexo博客下打开和设置RSS订阅</title>
     <url>/2023/12/21/blog_hexo_setting_RSS/</url>
@@ -9570,121 +9210,25 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>祖先状态重建：用PAML的baseML重建祖先序列</title>
-    <url>/2023/11/03/evolution_ancestral.state.reconstruction_paml.baseml/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=195874&auto=1&height=32"></iframe></div>
-
+    <title>用HyPhy分析自然选择：（二）用RELAX模型检测指定分支的选择是加强或放松</title>
+    <url>/2023/10/20/evolution_selection_HyPhy_RELAX/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1934103823&auto=1&height=32"></iframe></div>
 
-<h1 id="1-重建祖先序列"><a href="#1-重建祖先序列" class="headerlink" title="1. 重建祖先序列"></a>1. 重建祖先序列</h1><ul>
-<li>根据现存物种的状态（性状&#x2F;序列），以及系统发育关系，可以推断祖先节点物种的状态（性状&#x2F;序列），即祖先状态重建。</li>
-<li>这篇博客介绍使用PAML的baseML程序根据系统发育关系从现存物种的序列重建祖先序列的用法。</li>
+<h1 id="1-HyPhy的简介"><a href="#1-HyPhy的简介" class="headerlink" title="1. HyPhy的简介"></a>1. HyPhy的简介</h1><p>HyPhy的介绍可以参考博客<strong>用HyPhy分析自然选择：（一）介绍HyPhy</strong>：<a href="https://yanzhongsino.github.io/2023/10/20/evolution_selection_HyPhy_intro">https://yanzhongsino.github.io/2023/10/20/evolution_selection_HyPhy_intro</a></p>
+<ul>
+<li>HyPhy的全称是Hypothesis Testing using Phylogenies，是一款近些年受到广泛使用和关注的一款用于选择压力分析的软件。</li>
+<li>相较经典的选择分析软件PAML的codeML，HyPhy操作更简单一点，并且支持多线程运行，并使用了更新的模型，模型数量也更多。</li>
 </ul>
-<h1 id="2-用PAML的baseML程序重建祖先序列"><a href="#2-用PAML的baseML程序重建祖先序列" class="headerlink" title="2. 用PAML的baseML程序重建祖先序列"></a>2. 用PAML的baseML程序重建祖先序列</h1><h2 id="2-1-准备输入文件"><a href="#2-1-准备输入文件" class="headerlink" title="2.1. 准备输入文件"></a>2.1. 准备输入文件</h2><p>输入的序列比对文件和树文件的格式可以参考<a href="https://github.com/abacus-gene/paml-tutorial/tree/main/positive-selection/00_data">https://github.com/abacus-gene/paml-tutorial/tree/main/positive-selection/00_data</a> 中的 Mx_aln.phy Mx_root.tree，Mx_unroot.tre 文件。</p>
-<ol>
-<li>序列比对文件 (cds_aln.phy)</li>
-</ol>
-<ul>
-<li>phylip格式</li>
-<li>比对好的编码序列 (cds) 文件，碱基数量是3的倍数（codon模式比对）</li>
-<li>建议删除gaps和难以align的区域，但要按密码子删除（删除和保留都是3的倍数个碱基）</li>
-</ul>
-<ol start="2">
-<li>树文件 (tree.newick)</li>
-</ol>
-<ul>
-<li>newick格式，并在首行前加上一行，包括两个数字，一个是物种&#x2F;样本数量，一个是树的数量（一般是1），空格隔开。</li>
-<li>只需要拓扑结构，删除枝长、支持率等信息。</li>
-</ul>
-<ol start="3">
-<li>配置文件 (baseml.ctl)</li>
-</ol>
-<ul>
-<li>baseml.ctl 的例子在安装的程序包paml&#x2F;examples&#x2F;下有，可以复制到分析目录下，修改后使用</li>
-<li>基础的配置参数如下，后续分析可基于这套参数修改：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">seqfile = aln.phy  * sequence data file name</span><br><span class="line">treefile = tree.newick * tree structure file name</span><br><span class="line">outfile = out.txt * main result file</span><br><span class="line">noisy = 3     * 0,1,2,3: how much rubbish on the screen 控制屏幕输出的信息量</span><br><span class="line">verbose = 0     * 1: detailed output, 0: concise output * 控制输出文件中的信息量</span><br><span class="line">runmode = 0     * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI</span><br><span class="line">model = 6     * 0:JC69, 1:K80, 2:F81, 3:F84, 4:HKY85, 5:T92, 6:TN93, 7:REV, 8:UNREST, 9:REVu; 10:UNRESTu 指定核酸替代模型。模型0,1,…,8分别表示模型JC69， K80， F81， F84， HKY85， T92， TN93， REV (也称为 GTR)， 和 UNREST。注释参看Yang（1994 JME 39:105-111）。两个模型可以分别运行。model = 9是REV模型的特例，而model = 10是无限制模型的特例。当model = 9或10时，同一行里包含的主要是指定模型的额外信息。中括号中的数字是自由参数比值的数字。例如，REV应该是5而UNREST应该是11。接在这些数字后面的是圆括号中的数字。输出文件中的比值参数将按照这里的顺序摆列。括号中没有提到的比值为1。当model = 9时，你只能指定TC或CT，但不能同时指定。当model = 10时，TC和CT是不同的。</span><br><span class="line">Mgene = 0      * 0:rates, 1:separate; 2:diff pi, 3:diff kapa, 4:all diff</span><br><span class="line">ndata = 1      * number of data sets * 基因或比对的数量</span><br><span class="line">clock = 0      * 0:no clock, 1:clock; 2:local clock; 3:CombinedAnalysis</span><br><span class="line">fix_kappa = 0      * 0: estimate kappa; 1: fix kappa at value below</span><br><span class="line">kappa = 2.5      * initial or fixed kappa</span><br><span class="line">fix_alpha = 1      * 0: estimate alpha; 1: fix alpha at value below</span><br><span class="line">alpha = 0.      * initial or fixed alpha, 0:infinity (constant rate)</span><br><span class="line">Malpha = 0      * 1: different alpha¡¯s for genes, 0: one alpha</span><br><span class="line">ncatG = 5      * # of categories in the dG, AdG, or nparK models of rates</span><br><span class="line">fix_rho = 1      * 0: estimate rho; 1: fix rho at value below</span><br><span class="line">rho = 0.      * initial or fixed rho, 0:no correlation</span><br><span class="line">nparK = 0      * rate-class models. 1:rK, 2:rK&amp;fK, 3:rK&amp;MK(1/K), 4:rK&amp;MK</span><br><span class="line">nhomo = 0      * 0 &amp; 1: homogeneous, 2: kappa for branches, 3: N1, 4: N2。 nhomo仅针对baseml，并涉及同一替代模型中的频率参数。nhomo = 1适合一个均匀模型，但是通过最大似然迭代估算频率参数（πT，πC和πA；πG在频率总和为1中不是一个自由参数。）。这适用于F81，F84，HKY85，T92（这种情况下πGC是一个参数），TN93，或REV模型。通常（nhomo = 0）这些通过观察到的频率的平均值来估算。在两种情况下（nhomo = 0 和 1），你需要为碱基频率计算三个（或对T92计算一个）自由参数。 nhomo = 2 适用K80，F84和HKY85模型对树中的每个分枝估算一个转换/颠换比率（k）。 选项 nhomo = 3，4，和5仅用于F84，HKY85 或 T92。</span><br><span class="line">getSE = 0      * 0: don¡¯t want them, 1: want S.E.s of estimates</span><br><span class="line">RateAncestor = 1      * (0,1,2): rates (alpha&gt;0) or ancestral states，默认是0，不重构祖先状态，1是重构祖先序列。</span><br><span class="line">Small_Diff = 1e-6</span><br><span class="line">cleandata = 0      * remove sites with ambiguity data (1:yes, 0:no)?  歧义数据和比对间隙的位点要保留(0)或删除(1)，建议自行删除歧义和gaps数据，并在这里选保留(0)。</span><br><span class="line">* icode = 0      * (RateAncestor=1 for coding genes, ¡°GC¡± in data)</span><br><span class="line">* fix_blength = 0      * 0: ignore, -1: random, 1: initial, 2: fixed。设置程序处理输入树文件中枝长数据的方法：0，忽略输入树文件中的枝长信息；-1，使用一个随机起始进行进行计算；1，以输入的枝长信息作为初始值进行ML迭代分析，此时需要注意输入的枝长信息是碱基替换率，而不是碱基替换数；2，不需要使用ML迭代计算枝长，直接使用输入的枝长信息，需要注意，若枝长信息和序列信息不吻合可能导致程序崩溃；3，让ML计算出的枝长和输入的枝长呈正比。</span><br><span class="line">method = 0      * 0: simultaneous; 1: one branch at a time</span><br></pre></td></tr></table></figure>
-
-<h2 id="2-2-执行"><a href="#2-2-执行" class="headerlink" title="2.2. 执行"></a>2.2. 执行</h2><ul>
-<li>执行命令<code>baseml baseml.ctl</code></li>
-</ul>
-<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><p>推断的祖先序列在结果文件rst中，与提供的比对好的现存物种序列长度一致，可免比对，直接提取使用。</p>
-<h3 id="2-3-1-out-txt"><a href="#2-3-1-out-txt" class="headerlink" title="2.3.1. out.txt"></a>2.3.1. out.txt</h3><ul>
-<li>主要结果文件，默认名称是mlc。</li>
-<li>内容包括每条序列的A&#x2F;T&#x2F;C&#x2F;G的频率，一致性位点数量，成对遗传距离矩阵，Pairwise deletion matrix，带枝长的树，树节点和枝的编号，以及kappa值。<h3 id="2-3-2-rst：包含三块内容"><a href="#2-3-2-rst：包含三块内容" class="headerlink" title="2.3.2. rst：包含三块内容"></a>2.3.2. rst：包含三块内容</h3>Marginal reconstruction和Joint reconstruction是两种祖先序列重构的算法。如果对所有分类单元的合集都感兴趣，用joint；如果对特定分类单元的祖先感兴趣，用marginal。</li>
-</ul>
-<ol>
-<li>运行的参数以及树的信息</li>
-</ol>
-<ul>
-<li>给系统发育树的每个node进行编号的信息，以及每个branch是哪两个node连接而成；</li>
-</ul>
-<ol start="2">
-<li>Marginal reconstruction of ancestral sequences的结果</li>
-</ol>
-<ul>
-<li>Prob of best state at each node, listed by site. 分位点记录的每个node的序列和频率，以及重建的祖先序列的概率；</li>
-<li>Summary of changes along branches. 分branch记录突变的位点；</li>
-<li>List of extant and reconstructed sequences（现存node和祖先node的序列）；</li>
-<li>Overall accuracy of the 91 ancestral sequences</li>
-<li>Counts of changes at sites. 分位点记录突变的计数；</li>
-</ul>
-<ol start="3">
-<li>Joint reconstruction of ancestral sequences的结果</li>
-</ol>
-<ul>
-<li>Reconstruction (prob.), listed by pattern</li>
-<li>List of extant and reconstructed sequences（现存node和祖先node的序列）；<h3 id="2-3-3-其他文件"><a href="#2-3-3-其他文件" class="headerlink" title="2.3.3. 其他文件"></a>2.3.3. 其他文件</h3>另外还有一些其他结果文件，暂时还未用到。</li>
-</ul>
-<ol>
-<li>rst1</li>
-<li>lnf</li>
-<li>rub</li>
-</ol>
-<h1 id="3-reference"><a href="#3-reference" class="headerlink" title="3. reference"></a>3. reference</h1><ol>
-<li>PAML User Guide：<a href="https://github.com/abacus-gene/paml/blob/master/doc/pamlDOC.pdf">https://github.com/abacus-gene/paml/blob/master/doc/pamlDOC.pdf</a></li>
-<li>PAML 中文用户手册：<a href="https://blog.sciencenet.cn/blog-3433349-1241310.html">https://blog.sciencenet.cn/blog-3433349-1241310.html</a></li>
-<li>PAML开发者网站：<a href="http://abacus.gene.ucl.ac.uk/software/#phylogenetic-analysis-by-maximum-likelihood-paml">http://abacus.gene.ucl.ac.uk/software/#phylogenetic-analysis-by-maximum-likelihood-paml</a></li>
-<li>paml软件进行祖先酶序列重建极简教程视频：<a href="https://www.bilibili.com/video/BV1AX4y1K7H4/">https://www.bilibili.com/video/BV1AX4y1K7H4/</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>bio</category>
-        <category>evolution</category>
-        <category>ancestral state reconstruction</category>
-      </categories>
-      <tags>
-        <tag>biosoft</tag>
-        <tag>bioinfo</tag>
-        <tag>PAML</tag>
-        <tag>baseML</tag>
-        <tag>祖先状态重建</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用HyPhy分析自然选择：（二）用RELAX模型检测指定分支的选择是加强或放松</title>
-    <url>/2023/10/20/evolution_selection_HyPhy_RELAX/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1934103823&auto=1&height=32"></iframe></div>
-
-<h1 id="1-HyPhy的简介"><a href="#1-HyPhy的简介" class="headerlink" title="1. HyPhy的简介"></a>1. HyPhy的简介</h1><p>HyPhy的介绍可以参考博客<strong>用HyPhy分析自然选择：（一）介绍HyPhy</strong>：<a href="https://yanzhongsino.github.io/2023/10/20/evolution_selection_HyPhy_intro">https://yanzhongsino.github.io/2023/10/20/evolution_selection_HyPhy_intro</a></p>
-<ul>
-<li>HyPhy的全称是Hypothesis Testing using Phylogenies，是一款近些年受到广泛使用和关注的一款用于选择压力分析的软件。</li>
-<li>相较经典的选择分析软件PAML的codeML，HyPhy操作更简单一点，并且支持多线程运行，并使用了更新的模型，模型数量也更多。</li>
-</ul>
-<h1 id="2-HyPhy的RELAX模型"><a href="#2-HyPhy的RELAX模型" class="headerlink" title="2. HyPhy的RELAX模型"></a>2. HyPhy的RELAX模型</h1><ul>
-<li>HyPhy的RELAX模型是用来检测系统发育树上的指定分支是否经历了负选择的放松或加强的模型。</li>
-<li>RELAX检测选择压力与一组指定的 “测试 （TEST）”分支的放松（负选择变得不那么严格）或加强（负选择变得更强）。这种方法不适用于检测正选择。</li>
-<li>RELAX是一种假设检验框架，它检测自然选择的强度是否沿着一组指定的测试分支被放松或加强。因此，RELAX不是明确检测正选择的合适方法。相反，RELAX在识别特定基因上自然选择严格程度的趋势和&#x2F;或变化方面最有用。</li>
-<li>RELAX需要一组指定的 “测试 “分支与第二组 “参考 “分支进行比较（注意，不必分配所有的分支，但测试集和参考集各需要一个分支）。RELAX首先对整个系统发育过程拟合一个具有三个ω类的密码子模型（空模型）。然后，RELAX通过引入作为选择强度参数的参数k（其中k≥0）作为推断ω值的指数：ωk来测试放松&#x2F;强化选择。具体来说，RELAX固定推断的ω值（都是ωk&lt;1,2,3&gt;），并对测试分支推断出一个将比率修改为ωk&lt;1,2,3&gt;的k值（替代模型）。然后，RELAX进行似然比检验，比较替代模型和空模型。</li>
-</ul>
-<h1 id="3-HyPhy的RELAX模型检测选择放松或加强"><a href="#3-HyPhy的RELAX模型检测选择放松或加强" class="headerlink" title="3. HyPhy的RELAX模型检测选择放松或加强"></a>3. HyPhy的RELAX模型检测选择放松或加强</h1><h2 id="3-1-安装HyPhy"><a href="#3-1-安装HyPhy" class="headerlink" title="3.1. 安装HyPhy"></a>3.1. 安装HyPhy</h2><p><code>conda install -c bioconda hyphy</code></p>
-<h2 id="3-2-输入文件"><a href="#3-2-输入文件" class="headerlink" title="3.2. 输入文件"></a>3.2. 输入文件</h2><ol>
-<li>samples.aln：fasta或phylip格式的序列比对文件</li>
-<li>tree.nw：newick格式的树文件</li>
+<h1 id="2-HyPhy的RELAX模型"><a href="#2-HyPhy的RELAX模型" class="headerlink" title="2. HyPhy的RELAX模型"></a>2. HyPhy的RELAX模型</h1><ul>
+<li>HyPhy的RELAX模型是用来检测系统发育树上的指定分支是否经历了负选择的放松或加强的模型。</li>
+<li>RELAX检测选择压力与一组指定的 “测试 （TEST）”分支的放松（负选择变得不那么严格）或加强（负选择变得更强）。这种方法不适用于检测正选择。</li>
+<li>RELAX是一种假设检验框架，它检测自然选择的强度是否沿着一组指定的测试分支被放松或加强。因此，RELAX不是明确检测正选择的合适方法。相反，RELAX在识别特定基因上自然选择严格程度的趋势和&#x2F;或变化方面最有用。</li>
+<li>RELAX需要一组指定的 “测试 “分支与第二组 “参考 “分支进行比较（注意，不必分配所有的分支，但测试集和参考集各需要一个分支）。RELAX首先对整个系统发育过程拟合一个具有三个ω类的密码子模型（空模型）。然后，RELAX通过引入作为选择强度参数的参数k（其中k≥0）作为推断ω值的指数：ωk来测试放松&#x2F;强化选择。具体来说，RELAX固定推断的ω值（都是ωk&lt;1,2,3&gt;），并对测试分支推断出一个将比率修改为ωk&lt;1,2,3&gt;的k值（替代模型）。然后，RELAX进行似然比检验，比较替代模型和空模型。</li>
+</ul>
+<h1 id="3-HyPhy的RELAX模型检测选择放松或加强"><a href="#3-HyPhy的RELAX模型检测选择放松或加强" class="headerlink" title="3. HyPhy的RELAX模型检测选择放松或加强"></a>3. HyPhy的RELAX模型检测选择放松或加强</h1><h2 id="3-1-安装HyPhy"><a href="#3-1-安装HyPhy" class="headerlink" title="3.1. 安装HyPhy"></a>3.1. 安装HyPhy</h2><p><code>conda install -c bioconda hyphy</code></p>
+<h2 id="3-2-输入文件"><a href="#3-2-输入文件" class="headerlink" title="3.2. 输入文件"></a>3.2. 输入文件</h2><ol>
+<li>samples.aln：fasta或phylip格式的序列比对文件</li>
+<li>tree.nw：newick格式的树文件</li>
 </ol>
 <ul>
 <li>RELAX模块的分析需要指定前景支&#x2F;测试支（TEST），其余支是参考支(reference，默认，无需标记)。</li>
@@ -9917,7 +9461,6 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       <tags>
         <tag>biosoft</tag>
         <tag>bioinfo</tag>
-        <tag>PAML</tag>
         <tag>HyPhy</tag>
         <tag>RELAX</tag>
         <tag>selection</tag>
@@ -9928,6 +9471,7 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>FADE</tag>
         <tag>aBSREL</tag>
         <tag>BUSTED</tag>
+        <tag>PAML</tag>
         <tag>codeML</tag>
       </tags>
   </entry>
@@ -11157,6 +10701,102 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>rar</tag>
       </tags>
   </entry>
+  <entry>
+    <title>祖先状态重建：用PAML的baseML重建祖先序列</title>
+    <url>/2023/11/03/evolution_ancestral.state.reconstruction_paml.baseml/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=195874&auto=1&height=32"></iframe></div>
+
+
+<h1 id="1-重建祖先序列"><a href="#1-重建祖先序列" class="headerlink" title="1. 重建祖先序列"></a>1. 重建祖先序列</h1><ul>
+<li>根据现存物种的状态（性状&#x2F;序列），以及系统发育关系，可以推断祖先节点物种的状态（性状&#x2F;序列），即祖先状态重建。</li>
+<li>这篇博客介绍使用PAML的baseML程序根据系统发育关系从现存物种的序列重建祖先序列的用法。</li>
+</ul>
+<h1 id="2-用PAML的baseML程序重建祖先序列"><a href="#2-用PAML的baseML程序重建祖先序列" class="headerlink" title="2. 用PAML的baseML程序重建祖先序列"></a>2. 用PAML的baseML程序重建祖先序列</h1><h2 id="2-1-准备输入文件"><a href="#2-1-准备输入文件" class="headerlink" title="2.1. 准备输入文件"></a>2.1. 准备输入文件</h2><p>输入的序列比对文件和树文件的格式可以参考<a href="https://github.com/abacus-gene/paml-tutorial/tree/main/positive-selection/00_data">https://github.com/abacus-gene/paml-tutorial/tree/main/positive-selection/00_data</a> 中的 Mx_aln.phy Mx_root.tree，Mx_unroot.tre 文件。</p>
+<ol>
+<li>序列比对文件 (cds_aln.phy)</li>
+</ol>
+<ul>
+<li>phylip格式</li>
+<li>比对好的编码序列 (cds) 文件，碱基数量是3的倍数（codon模式比对）</li>
+<li>建议删除gaps和难以align的区域，但要按密码子删除（删除和保留都是3的倍数个碱基）</li>
+</ul>
+<ol start="2">
+<li>树文件 (tree.newick)</li>
+</ol>
+<ul>
+<li>newick格式，并在首行前加上一行，包括两个数字，一个是物种&#x2F;样本数量，一个是树的数量（一般是1），空格隔开。</li>
+<li>只需要拓扑结构，删除枝长、支持率等信息。</li>
+</ul>
+<ol start="3">
+<li>配置文件 (baseml.ctl)</li>
+</ol>
+<ul>
+<li>baseml.ctl 的例子在安装的程序包paml&#x2F;examples&#x2F;下有，可以复制到分析目录下，修改后使用</li>
+<li>基础的配置参数如下，后续分析可基于这套参数修改：</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">seqfile = aln.phy  * sequence data file name</span><br><span class="line">treefile = tree.newick * tree structure file name</span><br><span class="line">outfile = out.txt * main result file</span><br><span class="line">noisy = 3     * 0,1,2,3: how much rubbish on the screen 控制屏幕输出的信息量</span><br><span class="line">verbose = 0     * 1: detailed output, 0: concise output * 控制输出文件中的信息量</span><br><span class="line">runmode = 0     * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI</span><br><span class="line">model = 6     * 0:JC69, 1:K80, 2:F81, 3:F84, 4:HKY85, 5:T92, 6:TN93, 7:REV, 8:UNREST, 9:REVu; 10:UNRESTu 指定核酸替代模型。模型0,1,…,8分别表示模型JC69， K80， F81， F84， HKY85， T92， TN93， REV (也称为 GTR)， 和 UNREST。注释参看Yang（1994 JME 39:105-111）。两个模型可以分别运行。model = 9是REV模型的特例，而model = 10是无限制模型的特例。当model = 9或10时，同一行里包含的主要是指定模型的额外信息。中括号中的数字是自由参数比值的数字。例如，REV应该是5而UNREST应该是11。接在这些数字后面的是圆括号中的数字。输出文件中的比值参数将按照这里的顺序摆列。括号中没有提到的比值为1。当model = 9时，你只能指定TC或CT，但不能同时指定。当model = 10时，TC和CT是不同的。</span><br><span class="line">Mgene = 0      * 0:rates, 1:separate; 2:diff pi, 3:diff kapa, 4:all diff</span><br><span class="line">ndata = 1      * number of data sets * 基因或比对的数量</span><br><span class="line">clock = 0      * 0:no clock, 1:clock; 2:local clock; 3:CombinedAnalysis</span><br><span class="line">fix_kappa = 0      * 0: estimate kappa; 1: fix kappa at value below</span><br><span class="line">kappa = 2.5      * initial or fixed kappa</span><br><span class="line">fix_alpha = 1      * 0: estimate alpha; 1: fix alpha at value below</span><br><span class="line">alpha = 0.      * initial or fixed alpha, 0:infinity (constant rate)</span><br><span class="line">Malpha = 0      * 1: different alpha¡¯s for genes, 0: one alpha</span><br><span class="line">ncatG = 5      * # of categories in the dG, AdG, or nparK models of rates</span><br><span class="line">fix_rho = 1      * 0: estimate rho; 1: fix rho at value below</span><br><span class="line">rho = 0.      * initial or fixed rho, 0:no correlation</span><br><span class="line">nparK = 0      * rate-class models. 1:rK, 2:rK&amp;fK, 3:rK&amp;MK(1/K), 4:rK&amp;MK</span><br><span class="line">nhomo = 0      * 0 &amp; 1: homogeneous, 2: kappa for branches, 3: N1, 4: N2。 nhomo仅针对baseml，并涉及同一替代模型中的频率参数。nhomo = 1适合一个均匀模型，但是通过最大似然迭代估算频率参数（πT，πC和πA；πG在频率总和为1中不是一个自由参数。）。这适用于F81，F84，HKY85，T92（这种情况下πGC是一个参数），TN93，或REV模型。通常（nhomo = 0）这些通过观察到的频率的平均值来估算。在两种情况下（nhomo = 0 和 1），你需要为碱基频率计算三个（或对T92计算一个）自由参数。 nhomo = 2 适用K80，F84和HKY85模型对树中的每个分枝估算一个转换/颠换比率（k）。 选项 nhomo = 3，4，和5仅用于F84，HKY85 或 T92。</span><br><span class="line">getSE = 0      * 0: don¡¯t want them, 1: want S.E.s of estimates</span><br><span class="line">RateAncestor = 1      * (0,1,2): rates (alpha&gt;0) or ancestral states，默认是0，不重构祖先状态，1是重构祖先序列。</span><br><span class="line">Small_Diff = 1e-6</span><br><span class="line">cleandata = 0      * remove sites with ambiguity data (1:yes, 0:no)?  歧义数据和比对间隙的位点要保留(0)或删除(1)，建议自行删除歧义和gaps数据，并在这里选保留(0)。</span><br><span class="line">* icode = 0      * (RateAncestor=1 for coding genes, ¡°GC¡± in data)</span><br><span class="line">* fix_blength = 0      * 0: ignore, -1: random, 1: initial, 2: fixed。设置程序处理输入树文件中枝长数据的方法：0，忽略输入树文件中的枝长信息；-1，使用一个随机起始进行进行计算；1，以输入的枝长信息作为初始值进行ML迭代分析，此时需要注意输入的枝长信息是碱基替换率，而不是碱基替换数；2，不需要使用ML迭代计算枝长，直接使用输入的枝长信息，需要注意，若枝长信息和序列信息不吻合可能导致程序崩溃；3，让ML计算出的枝长和输入的枝长呈正比。</span><br><span class="line">method = 0      * 0: simultaneous; 1: one branch at a time</span><br></pre></td></tr></table></figure>
+
+<h2 id="2-2-执行"><a href="#2-2-执行" class="headerlink" title="2.2. 执行"></a>2.2. 执行</h2><ul>
+<li>执行命令<code>baseml baseml.ctl</code></li>
+</ul>
+<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><p>推断的祖先序列在结果文件rst中，与提供的比对好的现存物种序列长度一致，可免比对，直接提取使用。</p>
+<h3 id="2-3-1-out-txt"><a href="#2-3-1-out-txt" class="headerlink" title="2.3.1. out.txt"></a>2.3.1. out.txt</h3><ul>
+<li>主要结果文件，默认名称是mlc。</li>
+<li>内容包括每条序列的A&#x2F;T&#x2F;C&#x2F;G的频率，一致性位点数量，成对遗传距离矩阵，Pairwise deletion matrix，带枝长的树，树节点和枝的编号，以及kappa值。<h3 id="2-3-2-rst：包含三块内容"><a href="#2-3-2-rst：包含三块内容" class="headerlink" title="2.3.2. rst：包含三块内容"></a>2.3.2. rst：包含三块内容</h3>Marginal reconstruction和Joint reconstruction是两种祖先序列重构的算法。如果对所有分类单元的合集都感兴趣，用joint；如果对特定分类单元的祖先感兴趣，用marginal。</li>
+</ul>
+<ol>
+<li>运行的参数以及树的信息</li>
+</ol>
+<ul>
+<li>给系统发育树的每个node进行编号的信息，以及每个branch是哪两个node连接而成；</li>
+</ul>
+<ol start="2">
+<li>Marginal reconstruction of ancestral sequences的结果</li>
+</ol>
+<ul>
+<li>Prob of best state at each node, listed by site. 分位点记录的每个node的序列和频率，以及重建的祖先序列的概率；</li>
+<li>Summary of changes along branches. 分branch记录突变的位点；</li>
+<li>List of extant and reconstructed sequences（现存node和祖先node的序列）；</li>
+<li>Overall accuracy of the 91 ancestral sequences</li>
+<li>Counts of changes at sites. 分位点记录突变的计数；</li>
+</ul>
+<ol start="3">
+<li>Joint reconstruction of ancestral sequences的结果</li>
+</ol>
+<ul>
+<li>Reconstruction (prob.), listed by pattern</li>
+<li>List of extant and reconstructed sequences（现存node和祖先node的序列）；<h3 id="2-3-3-其他文件"><a href="#2-3-3-其他文件" class="headerlink" title="2.3.3. 其他文件"></a>2.3.3. 其他文件</h3>另外还有一些其他结果文件，暂时还未用到。</li>
+</ul>
+<ol>
+<li>rst1</li>
+<li>lnf</li>
+<li>rub</li>
+</ol>
+<h1 id="3-reference"><a href="#3-reference" class="headerlink" title="3. reference"></a>3. reference</h1><ol>
+<li>PAML User Guide：<a href="https://github.com/abacus-gene/paml/blob/master/doc/pamlDOC.pdf">https://github.com/abacus-gene/paml/blob/master/doc/pamlDOC.pdf</a></li>
+<li>PAML 中文用户手册：<a href="https://blog.sciencenet.cn/blog-3433349-1241310.html">https://blog.sciencenet.cn/blog-3433349-1241310.html</a></li>
+<li>PAML开发者网站：<a href="http://abacus.gene.ucl.ac.uk/software/#phylogenetic-analysis-by-maximum-likelihood-paml">http://abacus.gene.ucl.ac.uk/software/#phylogenetic-analysis-by-maximum-likelihood-paml</a></li>
+<li>paml软件进行祖先酶序列重建极简教程视频：<a href="https://www.bilibili.com/video/BV1AX4y1K7H4/">https://www.bilibili.com/video/BV1AX4y1K7H4/</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bio</category>
+        <category>evolution</category>
+        <category>ancestral state reconstruction</category>
+      </categories>
+      <tags>
+        <tag>biosoft</tag>
+        <tag>bioinfo</tag>
+        <tag>PAML</tag>
+        <tag>baseML</tag>
+        <tag>祖先状态重建</tag>
+      </tags>
+  </entry>
   <entry>
     <title>服务器的硬件和开机流程的简介，以及BIOS和磁盘阵列控制器的设置</title>
     <url>/2023/04/19/linux_operation.and.maintenance_RAID.Controller.Card/</url>
@@ -11330,6 +10970,54 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>USB3.0 Extender</tag>
       </tags>
   </entry>
+  <entry>
+    <title>Linux系统服务器清理缓存</title>
+    <url>/2024/01/09/linux_operation.and.maintenance_cache.release/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2193514&auto=1&height=32"></iframe></div>
+
+<h1 id="1-释放缓存的步骤"><a href="#1-释放缓存的步骤" class="headerlink" title="1. 释放缓存的步骤"></a>1. 释放缓存的步骤</h1><p>服务器运行久了可以释放内存缓存，来增加运行速度，释放缓存需要root权限。</p>
+<ol>
+<li>查看内存和缓存使用情况</li>
+</ol>
+<ul>
+<li><code>free -h</code></li>
+<li>buff&#x2F;cache一栏是缓存占用量</li>
+</ul>
+<ol start="2">
+<li>同步</li>
+</ol>
+<ul>
+<li><code>sync</code></li>
+<li>释放内存前先使用sync命令做同步，以确保文件系统的完整性。</li>
+<li>这一步将所有未写的系统缓冲区写到磁盘中，包含已修改的 i-node、已延迟的块 I&#x2F;O 和读写映射文件。否则在释放缓存的过程中，可能会丢失未保存的文件。</li>
+</ul>
+<ol start="3">
+<li>释放缓存</li>
+</ol>
+<ul>
+<li><code>echo 1 &gt; /proc/sys/vm/drop_caches</code></li>
+<li>释放缓存，释放后可free -h再次查看缓存释放的效果。可以看到buff&#x2F;cache一栏显示的缓存占用量大大减少。</li>
+<li>可以使用<code>echo 1 &gt; /proc/sys/vm/drop_caches</code>和<code>echo 2 &gt; /proc/sys/vm/drop_caches</code>和<code>echo 3 &gt; /proc/sys/vm/drop_caches</code>多次释放缓存，使用第一次效果最好。</li>
+</ul>
+<h1 id="2-释放缓存示例"><a href="#2-释放缓存示例" class="headerlink" title="2. 释放缓存示例"></a>2. 释放缓存示例</h1><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        191G        388K        111G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 1 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        297G        396K        6.2G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 2 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        298G        396K        5.3G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 3 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        299G        396K        4.0G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br></pre></td></tr></table></figure>
+
+
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>linux</category>
+        <category>operation and maintenance</category>
+        <category>cache</category>
+      </categories>
+      <tags>
+        <tag>server</tag>
+        <tag>cache</tag>
+      </tags>
+  </entry>
   <entry>
     <title>Linux系统的服务器开机自动挂载硬盘的设置</title>
     <url>/2023/04/24/linux_operation.and.maintenance_auto.mount/</url>
@@ -11385,59 +11073,11 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>Linux系统服务器清理缓存</title>
-    <url>/2024/01/09/linux_operation.and.maintenance_cache.release/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2193514&auto=1&height=32"></iframe></div>
+    <title>Linux系统服务器清理系统日志</title>
+    <url>/2024/05/26/linux_operation.and.maintenance_log.release/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2092525192&auto=1&height=32"></iframe></div>
 
-<h1 id="1-释放缓存的步骤"><a href="#1-释放缓存的步骤" class="headerlink" title="1. 释放缓存的步骤"></a>1. 释放缓存的步骤</h1><p>服务器运行久了可以释放内存缓存，来增加运行速度，释放缓存需要root权限。</p>
-<ol>
-<li>查看内存和缓存使用情况</li>
-</ol>
-<ul>
-<li><code>free -h</code></li>
-<li>buff&#x2F;cache一栏是缓存占用量</li>
-</ul>
-<ol start="2">
-<li>同步</li>
-</ol>
-<ul>
-<li><code>sync</code></li>
-<li>释放内存前先使用sync命令做同步，以确保文件系统的完整性。</li>
-<li>这一步将所有未写的系统缓冲区写到磁盘中，包含已修改的 i-node、已延迟的块 I&#x2F;O 和读写映射文件。否则在释放缓存的过程中，可能会丢失未保存的文件。</li>
-</ul>
-<ol start="3">
-<li>释放缓存</li>
-</ol>
-<ul>
-<li><code>echo 1 &gt; /proc/sys/vm/drop_caches</code></li>
-<li>释放缓存，释放后可free -h再次查看缓存释放的效果。可以看到buff&#x2F;cache一栏显示的缓存占用量大大减少。</li>
-<li>可以使用<code>echo 1 &gt; /proc/sys/vm/drop_caches</code>和<code>echo 2 &gt; /proc/sys/vm/drop_caches</code>和<code>echo 3 &gt; /proc/sys/vm/drop_caches</code>多次释放缓存，使用第一次效果最好。</li>
-</ul>
-<h1 id="2-释放缓存示例"><a href="#2-释放缓存示例" class="headerlink" title="2. 释放缓存示例"></a>2. 释放缓存示例</h1><figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        191G        388K        111G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 1 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        297G        396K        6.2G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 2 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        298G        396K        5.3G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br><span class="line">root# sync</span><br><span class="line">root# echo 3 &gt; /proc/sys/vm/drop_caches</span><br><span class="line">root# free -h</span><br><span class="line">              total        used        free      shared  buff/cache   available</span><br><span class="line">Mem:           314G         11G        299G        396K        4.0G        301G</span><br><span class="line">Swap:           61G         60G         38M</span><br></pre></td></tr></table></figure>
-
-
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>linux</category>
-        <category>operation and maintenance</category>
-        <category>cache</category>
-      </categories>
-      <tags>
-        <tag>server</tag>
-        <tag>cache</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>Linux系统服务器清理系统日志</title>
-    <url>/2024/05/26/linux_operation.and.maintenance_log.release/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2092525192&auto=1&height=32"></iframe></div>
-
-<h1 id="1-查看系统日志记录情况【需要root权限】"><a href="#1-查看系统日志记录情况【需要root权限】" class="headerlink" title="1. 查看系统日志记录情况【需要root权限】"></a>1. 查看系统日志记录情况【需要root权限】</h1><p>服务器运行久了系统日志会满，有时开机会提示【SEL IS FULL】。这个时候就需要清理系统日志。<br>系统日志存放在目录<code>/var/log/journal/</code>下面。</p>
+<h1 id="1-查看系统日志记录情况【需要root权限】"><a href="#1-查看系统日志记录情况【需要root权限】" class="headerlink" title="1. 查看系统日志记录情况【需要root权限】"></a>1. 查看系统日志记录情况【需要root权限】</h1><p>服务器运行久了系统日志会满，有时开机会提示【SEL IS FULL】。这个时候就需要清理系统日志。<br>系统日志存放在目录<code>/var/log/journal/</code>下面。</p>
 <ol>
 <li><code>ll /var/log/journal/</code>查看系统日志文件</li>
 <li><code>du -t 100M /var</code>查看<code>/var</code>目录下面所有文件，包括系统日志和缓存等。 </li>
@@ -12265,6 +11905,207 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>awk</tag>
       </tags>
   </entry>
+  <entry>
+    <title>基因组注释（四）：非编码RNA的注释-用Infernal软件对Rfam 12进行RNA注释</title>
+    <url>/2022/04/22/omics_genome.annotation_ncRNA/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283100&auto=1&height=32"></iframe></div>
+
+<h1 id="1-ncRNA"><a href="#1-ncRNA" class="headerlink" title="1. ncRNA"></a>1. ncRNA</h1><p>非编码RNA(Non-coding RNA, ncRNA) 包括rRNA，tRNA，snRNA，snoRNA 和microRNA 等不编码蛋白质的RNA，它们转录后直接在RNA 水平上就能行使各自的生物学功能，并不需要翻译成蛋白质。</p>
+<h1 id="2-注释软件"><a href="#2-注释软件" class="headerlink" title="2. 注释软件"></a>2. 注释软件</h1><ul>
+<li>非编码RNA种类繁多，且结构特征各不相同，所以开发出了许多注释特定某一类RNA的软件，比如tRNAScan-SE预测tRNA，rnammer预测rRNA，snoScan 搜索带C&#x2F;D盒的snoRNAs，SnoGps 搜索带H&#x2F;ACA盒的snoRNAs，mirScan搜索microRNA等。</li>
+<li>Sanger实验室开发了Infernal软件，建立了1600多个RNA家族，并对每个家族建立了一致性二级结构和协方差模型，形成了Rfam数据库。采用Rfam数据库中的每个RNA的协方差模型，结合Infernal软件可以预测出已有RNA家族的新成员，只是特异性比较差。</li>
+</ul>
+<p>如果不是专门研究ncRNA，可以用Infernal注释所有ncRNA。如果需要更精细的注释，则可以选择特定软件注释特定RNA。</p>
+<p>这篇博客是介绍用Infernal程序与Rfam数据库一起用来注释与数据库中已知ncRNA同源的序列（这里用来注释完整的基因组）。注释结果包括tRNA，rRNA，snRNA，snoRNA和miRNA等。</p>
+<h1 id="3-Infernal"><a href="#3-Infernal" class="headerlink" title="3. Infernal"></a>3. Infernal</h1><p><a href="http://eddylab.org/infernal/">Infernal</a>全称是”INFERence of RNA ALignment”，是一个用来检索DNA序列数据库中RNA序列和结构相似性的软件，通过协方差模型covariance models (CMs)来实现。</p>
+<h2 id="3-1-安装Infernal"><a href="#3-1-安装Infernal" class="headerlink" title="3.1. 安装Infernal"></a>3.1. 安装Infernal</h2><p><code>conda install -c bioconda infernal</code><br>现在安装的是v1.1.4</p>
+<p>安装后可使用的命令包括：</p>
+<ul>
+<li>cmpress：对cm文件进行压缩并建立索引。</li>
+<li>cmscan：用提交的序列在cm数据库中进行检索。</li>
+<li>cmalign：将RNA序列同协方差模型进行比对，并输出为stockholm格式。</li>
+<li>cmbuild：通过多序列比对结果建立一个协方差模型，并保存在新文件中。</li>
+<li>cmcalibrate：对协方差模型(CM)进行校准，在使用cmsearch和cmscan前CM模型需要先经过这个程序的处理。</li>
+<li>cmconvert：用于格式转换，是将infernal 1.0以后的CM转化为当前版本需要的CM。但对1.0之前的版本无效。</li>
+<li>cmemit：一个采样程序，从CM中进行采样并输出。</li>
+<li>cmfetch：从一个大的CM文件中获取一个或多个CM。</li>
+<li>cmsearch：用提交的CM模型在序列数据库中进行检索。</li>
+<li>cmstat：对CM文件中的CM模型进行统计汇总。</li>
+</ul>
+<h1 id="4-Rfam"><a href="#4-Rfam" class="headerlink" title="4. Rfam"></a>4. Rfam</h1><p><a href="http://rfam.xfam.org/">Rfam</a>是RNA family数据库，包括ncRNA序列和ncRNA的二级结构，每个family用多序列比对和协方差模型covariance model (CM)来表示。</p>
+<ol>
+<li>下载Rfam数据库</li>
+</ol>
+<ul>
+<li><code>wget http://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz</code><br>在<a href="http://rfam.xfam.org/">Rfam</a>网站下载Rfam最新版本的CM数据库（目前是Rfam 14.7）</li>
+<li><code>gunzip Rfam.cm.gz</code><br>解压数据库</li>
+</ul>
+<ol start="2">
+<li>下载clanin</li>
+</ol>
+<ul>
+<li><code>wget http://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin</code><br>在<a href="http://rfam.xfam.org/">Rfam</a>网站下载Rfam数据库配套的clanin文件。</li>
+</ul>
+<h1 id="5-注释ncRNA"><a href="#5-注释ncRNA" class="headerlink" title="5. 注释ncRNA"></a>5. 注释ncRNA</h1><h2 id="5-1-建库"><a href="#5-1-建库" class="headerlink" title="5.1. 建库"></a>5.1. 建库</h2><ul>
+<li><code>cmpress Rfam.cm</code><br>使用cmpress压缩并建立索引，生成Rfam.cm.i1f, Rfam.cm.i1i, Rfam.cm.i1m, Rfam.cm.i1p。</li>
+</ul>
+<h2 id="5-2-注释"><a href="#5-2-注释" class="headerlink" title="5.2. 注释"></a>5.2. 注释</h2><ol>
+<li>序列索引<br>推荐的参数：</li>
+</ol>
+<p><code>nohup cmscan -Z 512 --cut_ga --rfam --nohmmonly --fmt 2 --tblout sample.tblout -o sample.result --clanin Rfam.clanin Rfam.cm genome.fa &amp;</code></p>
+<ul>
+<li>-Z：根据基因组大小来定，基因组大小的2倍，Mb单位，选一个整数。比如256Mb的基因组，-Z 512。</li>
+<li><code>--cut_ga --rfam --nohmmonly --fmt 2</code>：推荐使用</li>
+<li>–tblout sample.tblout：指定table格式输出文件</li>
+<li>-o sample.result：指定比对结果输出文件</li>
+<li>–clanin Rfam.clanin：指定clanin文件</li>
+<li>Rfam.cm genome.fa：指定数据库Rfam.cm和基因组genome.fa</li>
+</ul>
+<p>note：-o sample.result要放在Rfam.cm genome.fa前面，否则报错。</p>
+<p>此步骤<strong>耗时</strong>参考：250Mb基因组，默认线程，耗时2.5h。</p>
+<ol start="2">
+<li>结果文件</li>
+</ol>
+<ul>
+<li>sample.result：比对结果</li>
+<li>sample.tblout：table格式结果</li>
+</ul>
+<h2 id="5-3-整理结果"><a href="#5-3-整理结果" class="headerlink" title="5.3. 整理结果"></a>5.3. 整理结果</h2><h3 id="5-3-1-将注释结果整理成gff3文件"><a href="#5-3-1-将注释结果整理成gff3文件" class="headerlink" title="5.3.1. 将注释结果整理成gff3文件"></a>5.3.1. 将注释结果整理成gff3文件</h3><p>gff3文件可用于提交注释到数据库。</p>
+<p>用perl脚本<a href="https://github.com/yanzhongsino/bioscripts/blob/main/saved_scripts/infernal-tblout2gff.pl">infernal-tblout2gff.pl</a>实现，脚本来自<a href="https://www.cnblogs.com/jessepeng/p/15392809.html%E3%80%82">https://www.cnblogs.com/jessepeng/p/15392809.html。</a></p>
+<p><code>perl infernal-tblout2gff.pl --cmscan --fmt2 sample.tblout &gt;sample.infernal.ncRNA.gff3</code></p>
+<h3 id="5-3-2-统计各类ncRNA总数"><a href="#5-3-2-统计各类ncRNA总数" class="headerlink" title="5.3.2. 统计各类ncRNA总数"></a>5.3.2. 统计各类ncRNA总数</h3><ol>
+<li><p>整理注释结果文件sample.tblout<br>提取必需的列，非重叠区域或重叠区域得分高的区域<br><code>awk &#39;BEGIN&#123;OFS=&quot;\t&quot;;&#125;&#123;if(FNR==1) print &quot;target_name\taccession\tquery_name\tquery_start\tquery_end\tstrand\tscore\tEvalue&quot;; if(FNR&gt;2 &amp;&amp; $20!=&quot;=&quot; &amp;&amp; $0!~/^#/) print $2,$3,$4,$10,$11,$12,$17,$18; &#125;&#39; sample.tblout &gt;sample.tblout.xls</code></p>
+</li>
+<li><p>下载rfam注释</p>
+</li>
+</ol>
+<ul>
+<li>在<a href="https://rfam.xfam.org/">rfam官网</a>，选择【SEARCH】-【Entry type】</li>
+<li>然后选中所有的Entry types（包括Gene，Intron，Cis-regulatory element），点击【Submit】，会列出所有RNA family的注释信息。</li>
+<li>手动选择所有注释信息，复制，粘贴到新建的空白文本文件rfam.txt并保存。</li>
+<li>把rfam.txt传输到服务器，最好用<code>dos2unix rfam.txt</code>转换文件格式为unix版本。</li>
+<li>拆分第三列<code>cat rfam.txt | awk &#39;BEGIN &#123;FS=OFS=&quot;\t&quot;&#125;&#123;split($3,x,&quot;;&quot;);class=x[2];print $1,$2,$3,$4,class&#125;&#39; &gt; rfam_anno.txt</code></li>
+</ul>
+<p>rfam注释文件rfam_anno.txt包含了所有rfam的类型type和功能描述description信息。</p>
+<ol start="3">
+<li>统计ncRNA注释结果<br><code>awk &#39;BEGIN&#123;OFS=FS=&quot;\t&quot;&#125;ARGIND==1&#123;a[$2]=$5;&#125;ARGIND==2&#123;type=a[$1]; if(type==&quot;&quot;) type=&quot;Others&quot;; count[type]+=1;&#125;END&#123;for(type in count) print type, count[type];&#125;&#39; rfam_anno.txt sample.tblout.xls &gt;sample.ncRNA.statistic</code></li>
+</ol>
+<p>sample.ncRNA.statistic输出示例：</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"> riboswitch	1</span><br><span class="line"> ribozyme	1</span><br><span class="line"> tRNA	699</span><br><span class="line">Others	41</span><br><span class="line"> miRNA	188</span><br><span class="line"> antisense	4</span><br><span class="line"> rRNA	233</span><br><span class="line"> snRNA	698</span><br></pre></td></tr></table></figure>
+
+<ol start="4">
+<li>统计细分分类</li>
+</ol>
+<ul>
+<li>也可以根据细分分类分别统计，细分分类参考<a href="https://rfam.xfam.org/">rfam官网</a>，【SEARCH】-【Entry type】。</li>
+<li>可参考的统计值包括每个细分ncRNA的数量(copy)，平均长度(average length)，总长(total length)，总长占基因组的比例（Percent of the genome）等</li>
+<li>统计后整理成发表文章用的表格。</li>
+</ul>
+<p>比如：snRNA包括snoRNA和splicing，snoRNA包括CD-box，HACA-box和scaRNA。下面用统计CD-box这个细分分类的ncRNA举例。</p>
+<ul>
+<li>提取CD-box的Accession(RF00000格式)：<code>grep &quot;CD-box&quot; rfam_anno.txt |cut -f1 &gt;cdbox.tem</code></li>
+<li>提取注释到的CD-box信息：<code>grep -f cdbox.tem mc.tblout.xls &gt;cdbox.txt</code></li>
+<li>cdbox.txt的行数就是CD-box的数量；利用第四五列的位置信息，即可统计平均长度(average length)，总长(total length)，<strong>注意正反链</strong>。</li>
+</ul>
+<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
+<li><a href="https://www.cnblogs.com/jessepeng/p/15392809.html">https://www.cnblogs.com/jessepeng/p/15392809.html</a></li>
+<li><a href="http://www.360doc.com/content/18/1119/05/52645714_795799901.shtml">http://www.360doc.com/content/18/1119/05/52645714_795799901.shtml</a></li>
+<li><a href="https://genehub.wordpress.com/2019/08/08/%E6%A4%8D%E7%89%A9%E5%9F%BA%E5%9B%A0%E7%BB%84ncrna%E9%A2%84%E6%B5%8B%EF%BC%88trna%E3%80%81rrna%E3%80%81snrna%E3%80%81mirna%EF%BC%89/">https://genehub.wordpress.com/2019/08/08/%E6%A4%8D%E7%89%A9%E5%9F%BA%E5%9B%A0%E7%BB%84ncrna%E9%A2%84%E6%B5%8B%EF%BC%88trna%E3%80%81rrna%E3%80%81snrna%E3%80%81mirna%EF%BC%89/</a></li>
+<li><a href="http://embracethesky.cn/2018/07/08/%e4%bd%bf%e7%94%a8infernal%e5%af%b9rfam-12%e8%bf%9b%e8%a1%8crna%e6%b3%a8%e9%87%8a/#more-99">http://embracethesky.cn/2018/07/08/%e4%bd%bf%e7%94%a8infernal%e5%af%b9rfam-12%e8%bf%9b%e8%a1%8crna%e6%b3%a8%e9%87%8a/#more-99</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>omics</category>
+        <category>genome</category>
+        <category>annotation</category>
+      </categories>
+      <tags>
+        <tag>genome</tag>
+        <tag>genome annotation</tag>
+        <tag>ncRNA</tag>
+        <tag>Infernal</tag>
+        <tag>Rfam</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>用Hifiasm组装基因组：（一）简介Hifiasm软件和HiFi数据</title>
+    <url>/2024/06/19/omics_genome.assembly_TGS_Hifiasm_01.intro/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2154795903&auto=1&height=32"></iframe></div>
+
+<h1 id="1-HiFi数据"><a href="#1-HiFi数据" class="headerlink" title="1. HiFi数据"></a>1. HiFi数据</h1><p>HiFi reads（High Fidelity reads）是2019年由PacBio公司推出的基于环化一致性序列（Circular Consensus Sequencing，CCS）模式产生的既兼顾长读长（10-20kb的长度）又具有高精度（&gt;99%准确率）的测序结果。非常适合用于基因组组装。</p>
+<ol>
+<li>HiFi数据预处理</li>
+</ol>
+<ul>
+<li>可以用<code>bam2fasta</code>直接把下机数据ccs.bam或者hifi.bam转换成fasta格式文件用于后续分析。</li>
+<li><code>bam2fasta sample.ccs.bam -c 9 -o sample.ccs</code>命令会生成sample.ccs.fasta.gz文件，<code>-c 9</code>代表压缩程度为9。</li>
+</ul>
+<ol start="2">
+<li>用HiFi数据组装基因组的软件选择</li>
+</ol>
+<ul>
+<li>2024年发表在Genome Research上的一篇<a href="https://genome.cshlp.org/content/34/2/326">文章</a> 对11种针对HiFi测序技术的组装工具的评估结果显示，<strong>hifiasm</strong>和<strong>hifiasm-meta</strong>分别是组装真核基因组和宏基因组的最佳工具。</li>
+<li>文章显示，在真核生物基因组组装中，hifiasm在不同方法比较的组装基因组均具有更高的连续性、完整性和准确性；HiCanu、Verkko与LJA次之，但Verkko与LJA具有组装的contig较短等缺陷；NextDenovo仅对单倍体基因组具有更好的性能。</li>
+<li>宏基因组组装评估中，hifiasm-meta以及metaflye的组装错误最少，但是在面对复杂宏基因组时hifiasm-meta的完整性及连续性明显优于metaflye，但同时也会保留部分冗余的序列。</li>
+</ul>
+<p>目前来说，<strong>Hifiasm软件</strong>是用HiFi数据组装基因组的不二选择。</p>
+<h1 id="2-Hifiasm软件"><a href="#2-Hifiasm软件" class="headerlink" title="2. Hifiasm软件"></a>2. Hifiasm软件</h1><ol>
+<li>简介</li>
+</ol>
+<ul>
+<li>Hifiasm是一个利用PacBio HiFi数据进行从头组装基因组，获得单倍体基因组的组装工具。</li>
+<li>由哈佛大学李恒团队在2021年2月份开发，首次发表在Nature Methods上。2022年在Nature biotechnology上发表论文，在Hifiasm中引入了Hi-C Integrated assembly 模式。</li>
+<li>Hifiasm被设计用于PacBio HiFi数据组装基因组，使用在分型组装图（pahsed assembly graph）中表示单倍体信息的算法。</li>
+</ul>
+<ol start="2">
+<li>特点和优势</li>
+</ol>
+<ul>
+<li>运行速度很快。半天时间可以组装一个人类基因组。</li>
+<li>可以接受一种数据类群的多个输入文件（如多个HiFi数据文件），并且合并作为一个文件输入和多个文件输入的结果不同，建议就保持多个文件输入。</li>
+<li>倾向于尽量组装更长的contigs。</li>
+<li>能够更好地解决片段重复（segmental duplications）</li>
+<li>可以利用Hi-C数据或&#x2F;和亲本二代Illumina测序数据获得解析良好的单倍型组装。</li>
+<li>也可以利用Oxford Nanopore数据获得端粒到端粒的组装。</li>
+</ul>
+<ol start="3">
+<li>Hifiasm简化了组装流程</li>
+</ol>
+<ul>
+<li>内置了清除haplotigs之间的重复（duplications）的程序，无需第三方工具（如purge_dups）。</li>
+<li>组装完成后无需polish工具（如pilon，racon）进行polish。</li>
+</ul>
+<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
+<li>Hifiasm manual：<a href="https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/">https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/</a></li>
+<li>Hifiasm介绍：<a href="https://www.bilibili.com/read/cv18775152/">https://www.bilibili.com/read/cv18775152/</a></li>
+<li>hifiasm组装（多个cell的HiFi输入文件）的不同结果：<a href="https://mdnice.com/writing/25f5a8fe3bfe4474ae1bdcab44604da9">https://mdnice.com/writing/25f5a8fe3bfe4474ae1bdcab44604da9</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>omics</category>
+        <category>genome</category>
+        <category>genome assembly</category>
+      </categories>
+      <tags>
+        <tag>Hi-C</tag>
+        <tag>genome assembly</tag>
+        <tag>third generation sequencing</tag>
+        <tag>TGS</tag>
+        <tag>Hifiasm</tag>
+        <tag>HiFi</tag>
+        <tag>PacBio</tag>
+      </tags>
+  </entry>
   <entry>
     <title>基因组注释（三）：基因功能注释</title>
     <url>/2021/05/17/omics_genome.annotation_function/</url>
@@ -12637,210 +12478,9 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>基因组注释（四）：非编码RNA的注释-用Infernal软件对Rfam 12进行RNA注释</title>
-    <url>/2022/04/22/omics_genome.annotation_ncRNA/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283100&auto=1&height=32"></iframe></div>
-
-<h1 id="1-ncRNA"><a href="#1-ncRNA" class="headerlink" title="1. ncRNA"></a>1. ncRNA</h1><p>非编码RNA(Non-coding RNA, ncRNA) 包括rRNA，tRNA，snRNA，snoRNA 和microRNA 等不编码蛋白质的RNA，它们转录后直接在RNA 水平上就能行使各自的生物学功能，并不需要翻译成蛋白质。</p>
-<h1 id="2-注释软件"><a href="#2-注释软件" class="headerlink" title="2. 注释软件"></a>2. 注释软件</h1><ul>
-<li>非编码RNA种类繁多，且结构特征各不相同，所以开发出了许多注释特定某一类RNA的软件，比如tRNAScan-SE预测tRNA，rnammer预测rRNA，snoScan 搜索带C&#x2F;D盒的snoRNAs，SnoGps 搜索带H&#x2F;ACA盒的snoRNAs，mirScan搜索microRNA等。</li>
-<li>Sanger实验室开发了Infernal软件，建立了1600多个RNA家族，并对每个家族建立了一致性二级结构和协方差模型，形成了Rfam数据库。采用Rfam数据库中的每个RNA的协方差模型，结合Infernal软件可以预测出已有RNA家族的新成员，只是特异性比较差。</li>
-</ul>
-<p>如果不是专门研究ncRNA，可以用Infernal注释所有ncRNA。如果需要更精细的注释，则可以选择特定软件注释特定RNA。</p>
-<p>这篇博客是介绍用Infernal程序与Rfam数据库一起用来注释与数据库中已知ncRNA同源的序列（这里用来注释完整的基因组）。注释结果包括tRNA，rRNA，snRNA，snoRNA和miRNA等。</p>
-<h1 id="3-Infernal"><a href="#3-Infernal" class="headerlink" title="3. Infernal"></a>3. Infernal</h1><p><a href="http://eddylab.org/infernal/">Infernal</a>全称是”INFERence of RNA ALignment”，是一个用来检索DNA序列数据库中RNA序列和结构相似性的软件，通过协方差模型covariance models (CMs)来实现。</p>
-<h2 id="3-1-安装Infernal"><a href="#3-1-安装Infernal" class="headerlink" title="3.1. 安装Infernal"></a>3.1. 安装Infernal</h2><p><code>conda install -c bioconda infernal</code><br>现在安装的是v1.1.4</p>
-<p>安装后可使用的命令包括：</p>
-<ul>
-<li>cmpress：对cm文件进行压缩并建立索引。</li>
-<li>cmscan：用提交的序列在cm数据库中进行检索。</li>
-<li>cmalign：将RNA序列同协方差模型进行比对，并输出为stockholm格式。</li>
-<li>cmbuild：通过多序列比对结果建立一个协方差模型，并保存在新文件中。</li>
-<li>cmcalibrate：对协方差模型(CM)进行校准，在使用cmsearch和cmscan前CM模型需要先经过这个程序的处理。</li>
-<li>cmconvert：用于格式转换，是将infernal 1.0以后的CM转化为当前版本需要的CM。但对1.0之前的版本无效。</li>
-<li>cmemit：一个采样程序，从CM中进行采样并输出。</li>
-<li>cmfetch：从一个大的CM文件中获取一个或多个CM。</li>
-<li>cmsearch：用提交的CM模型在序列数据库中进行检索。</li>
-<li>cmstat：对CM文件中的CM模型进行统计汇总。</li>
-</ul>
-<h1 id="4-Rfam"><a href="#4-Rfam" class="headerlink" title="4. Rfam"></a>4. Rfam</h1><p><a href="http://rfam.xfam.org/">Rfam</a>是RNA family数据库，包括ncRNA序列和ncRNA的二级结构，每个family用多序列比对和协方差模型covariance model (CM)来表示。</p>
-<ol>
-<li>下载Rfam数据库</li>
-</ol>
-<ul>
-<li><code>wget http://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz</code><br>在<a href="http://rfam.xfam.org/">Rfam</a>网站下载Rfam最新版本的CM数据库（目前是Rfam 14.7）</li>
-<li><code>gunzip Rfam.cm.gz</code><br>解压数据库</li>
-</ul>
-<ol start="2">
-<li>下载clanin</li>
-</ol>
-<ul>
-<li><code>wget http://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin</code><br>在<a href="http://rfam.xfam.org/">Rfam</a>网站下载Rfam数据库配套的clanin文件。</li>
-</ul>
-<h1 id="5-注释ncRNA"><a href="#5-注释ncRNA" class="headerlink" title="5. 注释ncRNA"></a>5. 注释ncRNA</h1><h2 id="5-1-建库"><a href="#5-1-建库" class="headerlink" title="5.1. 建库"></a>5.1. 建库</h2><ul>
-<li><code>cmpress Rfam.cm</code><br>使用cmpress压缩并建立索引，生成Rfam.cm.i1f, Rfam.cm.i1i, Rfam.cm.i1m, Rfam.cm.i1p。</li>
-</ul>
-<h2 id="5-2-注释"><a href="#5-2-注释" class="headerlink" title="5.2. 注释"></a>5.2. 注释</h2><ol>
-<li>序列索引<br>推荐的参数：</li>
-</ol>
-<p><code>nohup cmscan -Z 512 --cut_ga --rfam --nohmmonly --fmt 2 --tblout sample.tblout -o sample.result --clanin Rfam.clanin Rfam.cm genome.fa &amp;</code></p>
-<ul>
-<li>-Z：根据基因组大小来定，基因组大小的2倍，Mb单位，选一个整数。比如256Mb的基因组，-Z 512。</li>
-<li><code>--cut_ga --rfam --nohmmonly --fmt 2</code>：推荐使用</li>
-<li>–tblout sample.tblout：指定table格式输出文件</li>
-<li>-o sample.result：指定比对结果输出文件</li>
-<li>–clanin Rfam.clanin：指定clanin文件</li>
-<li>Rfam.cm genome.fa：指定数据库Rfam.cm和基因组genome.fa</li>
-</ul>
-<p>note：-o sample.result要放在Rfam.cm genome.fa前面，否则报错。</p>
-<p>此步骤<strong>耗时</strong>参考：250Mb基因组，默认线程，耗时2.5h。</p>
-<ol start="2">
-<li>结果文件</li>
-</ol>
-<ul>
-<li>sample.result：比对结果</li>
-<li>sample.tblout：table格式结果</li>
-</ul>
-<h2 id="5-3-整理结果"><a href="#5-3-整理结果" class="headerlink" title="5.3. 整理结果"></a>5.3. 整理结果</h2><h3 id="5-3-1-将注释结果整理成gff3文件"><a href="#5-3-1-将注释结果整理成gff3文件" class="headerlink" title="5.3.1. 将注释结果整理成gff3文件"></a>5.3.1. 将注释结果整理成gff3文件</h3><p>gff3文件可用于提交注释到数据库。</p>
-<p>用perl脚本<a href="https://github.com/yanzhongsino/bioscripts/blob/main/saved_scripts/infernal-tblout2gff.pl">infernal-tblout2gff.pl</a>实现，脚本来自<a href="https://www.cnblogs.com/jessepeng/p/15392809.html%E3%80%82">https://www.cnblogs.com/jessepeng/p/15392809.html。</a></p>
-<p><code>perl infernal-tblout2gff.pl --cmscan --fmt2 sample.tblout &gt;sample.infernal.ncRNA.gff3</code></p>
-<h3 id="5-3-2-统计各类ncRNA总数"><a href="#5-3-2-统计各类ncRNA总数" class="headerlink" title="5.3.2. 统计各类ncRNA总数"></a>5.3.2. 统计各类ncRNA总数</h3><ol>
-<li><p>整理注释结果文件sample.tblout<br>提取必需的列，非重叠区域或重叠区域得分高的区域<br><code>awk &#39;BEGIN&#123;OFS=&quot;\t&quot;;&#125;&#123;if(FNR==1) print &quot;target_name\taccession\tquery_name\tquery_start\tquery_end\tstrand\tscore\tEvalue&quot;; if(FNR&gt;2 &amp;&amp; $20!=&quot;=&quot; &amp;&amp; $0!~/^#/) print $2,$3,$4,$10,$11,$12,$17,$18; &#125;&#39; sample.tblout &gt;sample.tblout.xls</code></p>
-</li>
-<li><p>下载rfam注释</p>
-</li>
-</ol>
-<ul>
-<li>在<a href="https://rfam.xfam.org/">rfam官网</a>，选择【SEARCH】-【Entry type】</li>
-<li>然后选中所有的Entry types（包括Gene，Intron，Cis-regulatory element），点击【Submit】，会列出所有RNA family的注释信息。</li>
-<li>手动选择所有注释信息，复制，粘贴到新建的空白文本文件rfam.txt并保存。</li>
-<li>把rfam.txt传输到服务器，最好用<code>dos2unix rfam.txt</code>转换文件格式为unix版本。</li>
-<li>拆分第三列<code>cat rfam.txt | awk &#39;BEGIN &#123;FS=OFS=&quot;\t&quot;&#125;&#123;split($3,x,&quot;;&quot;);class=x[2];print $1,$2,$3,$4,class&#125;&#39; &gt; rfam_anno.txt</code></li>
-</ul>
-<p>rfam注释文件rfam_anno.txt包含了所有rfam的类型type和功能描述description信息。</p>
-<ol start="3">
-<li>统计ncRNA注释结果<br><code>awk &#39;BEGIN&#123;OFS=FS=&quot;\t&quot;&#125;ARGIND==1&#123;a[$2]=$5;&#125;ARGIND==2&#123;type=a[$1]; if(type==&quot;&quot;) type=&quot;Others&quot;; count[type]+=1;&#125;END&#123;for(type in count) print type, count[type];&#125;&#39; rfam_anno.txt sample.tblout.xls &gt;sample.ncRNA.statistic</code></li>
-</ol>
-<p>sample.ncRNA.statistic输出示例：</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line"> riboswitch	1</span><br><span class="line"> ribozyme	1</span><br><span class="line"> tRNA	699</span><br><span class="line">Others	41</span><br><span class="line"> miRNA	188</span><br><span class="line"> antisense	4</span><br><span class="line"> rRNA	233</span><br><span class="line"> snRNA	698</span><br></pre></td></tr></table></figure>
-
-<ol start="4">
-<li>统计细分分类</li>
-</ol>
-<ul>
-<li>也可以根据细分分类分别统计，细分分类参考<a href="https://rfam.xfam.org/">rfam官网</a>，【SEARCH】-【Entry type】。</li>
-<li>可参考的统计值包括每个细分ncRNA的数量(copy)，平均长度(average length)，总长(total length)，总长占基因组的比例（Percent of the genome）等</li>
-<li>统计后整理成发表文章用的表格。</li>
-</ul>
-<p>比如：snRNA包括snoRNA和splicing，snoRNA包括CD-box，HACA-box和scaRNA。下面用统计CD-box这个细分分类的ncRNA举例。</p>
-<ul>
-<li>提取CD-box的Accession(RF00000格式)：<code>grep &quot;CD-box&quot; rfam_anno.txt |cut -f1 &gt;cdbox.tem</code></li>
-<li>提取注释到的CD-box信息：<code>grep -f cdbox.tem mc.tblout.xls &gt;cdbox.txt</code></li>
-<li>cdbox.txt的行数就是CD-box的数量；利用第四五列的位置信息，即可统计平均长度(average length)，总长(total length)，<strong>注意正反链</strong>。</li>
-</ul>
-<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
-<li><a href="https://www.cnblogs.com/jessepeng/p/15392809.html">https://www.cnblogs.com/jessepeng/p/15392809.html</a></li>
-<li><a href="http://www.360doc.com/content/18/1119/05/52645714_795799901.shtml">http://www.360doc.com/content/18/1119/05/52645714_795799901.shtml</a></li>
-<li><a href="https://genehub.wordpress.com/2019/08/08/%E6%A4%8D%E7%89%A9%E5%9F%BA%E5%9B%A0%E7%BB%84ncrna%E9%A2%84%E6%B5%8B%EF%BC%88trna%E3%80%81rrna%E3%80%81snrna%E3%80%81mirna%EF%BC%89/">https://genehub.wordpress.com/2019/08/08/%E6%A4%8D%E7%89%A9%E5%9F%BA%E5%9B%A0%E7%BB%84ncrna%E9%A2%84%E6%B5%8B%EF%BC%88trna%E3%80%81rrna%E3%80%81snrna%E3%80%81mirna%EF%BC%89/</a></li>
-<li><a href="http://embracethesky.cn/2018/07/08/%e4%bd%bf%e7%94%a8infernal%e5%af%b9rfam-12%e8%bf%9b%e8%a1%8crna%e6%b3%a8%e9%87%8a/#more-99">http://embracethesky.cn/2018/07/08/%e4%bd%bf%e7%94%a8infernal%e5%af%b9rfam-12%e8%bf%9b%e8%a1%8crna%e6%b3%a8%e9%87%8a/#more-99</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>omics</category>
-        <category>genome</category>
-        <category>annotation</category>
-      </categories>
-      <tags>
-        <tag>genome</tag>
-        <tag>genome annotation</tag>
-        <tag>ncRNA</tag>
-        <tag>Infernal</tag>
-        <tag>Rfam</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用Hifiasm组装基因组：（一）简介Hifiasm软件和HiFi数据</title>
-    <url>/2024/06/19/omics_genome.assembly_TGS_Hifiasm_01.intro/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2154795903&auto=1&height=32"></iframe></div>
-
-<h1 id="1-HiFi数据"><a href="#1-HiFi数据" class="headerlink" title="1. HiFi数据"></a>1. HiFi数据</h1><p>HiFi reads（High Fidelity reads）是2019年由PacBio公司推出的基于环化一致性序列（Circular Consensus Sequencing，CCS）模式产生的既兼顾长读长（10-20kb的长度）又具有高精度（&gt;99%准确率）的测序结果。非常适合用于基因组组装。</p>
-<ol>
-<li>HiFi数据预处理</li>
-</ol>
-<ul>
-<li>可以用<code>bam2fasta</code>直接把下机数据ccs.bam或者hifi.bam转换成fasta格式文件用于后续分析。</li>
-<li><code>bam2fasta sample.ccs.bam -c 9 -o sample.ccs</code>命令会生成sample.ccs.fasta.gz文件，<code>-c 9</code>代表压缩程度为9。</li>
-</ul>
-<ol start="2">
-<li>用HiFi数据组装基因组的软件选择</li>
-</ol>
-<ul>
-<li>2024年发表在Genome Research上的一篇<a href="https://genome.cshlp.org/content/34/2/326">文章</a> 对11种针对HiFi测序技术的组装工具的评估结果显示，<strong>hifiasm</strong>和<strong>hifiasm-meta</strong>分别是组装真核基因组和宏基因组的最佳工具。</li>
-<li>文章显示，在真核生物基因组组装中，hifiasm在不同方法比较的组装基因组均具有更高的连续性、完整性和准确性；HiCanu、Verkko与LJA次之，但Verkko与LJA具有组装的contig较短等缺陷；NextDenovo仅对单倍体基因组具有更好的性能。</li>
-<li>宏基因组组装评估中，hifiasm-meta以及metaflye的组装错误最少，但是在面对复杂宏基因组时hifiasm-meta的完整性及连续性明显优于metaflye，但同时也会保留部分冗余的序列。</li>
-</ul>
-<p>目前来说，<strong>Hifiasm软件</strong>是用HiFi数据组装基因组的不二选择。</p>
-<h1 id="2-Hifiasm软件"><a href="#2-Hifiasm软件" class="headerlink" title="2. Hifiasm软件"></a>2. Hifiasm软件</h1><ol>
-<li>简介</li>
-</ol>
-<ul>
-<li>Hifiasm是一个利用PacBio HiFi数据进行从头组装基因组，获得单倍体基因组的组装工具。</li>
-<li>由哈佛大学李恒团队在2021年2月份开发，首次发表在Nature Methods上。2022年在Nature biotechnology上发表论文，在Hifiasm中引入了Hi-C Integrated assembly 模式。</li>
-<li>Hifiasm被设计用于PacBio HiFi数据组装基因组，使用在分型组装图（pahsed assembly graph）中表示单倍体信息的算法。</li>
-</ul>
-<ol start="2">
-<li>特点和优势</li>
-</ol>
-<ul>
-<li>运行速度很快。半天时间可以组装一个人类基因组。</li>
-<li>可以接受一种数据类群的多个输入文件（如多个HiFi数据文件），并且合并作为一个文件输入和多个文件输入的结果不同，建议就保持多个文件输入。</li>
-<li>倾向于尽量组装更长的contigs。</li>
-<li>能够更好地解决片段重复（segmental duplications）</li>
-<li>可以利用Hi-C数据或&#x2F;和亲本二代Illumina测序数据获得解析良好的单倍型组装。</li>
-<li>也可以利用Oxford Nanopore数据获得端粒到端粒的组装。</li>
-</ul>
-<ol start="3">
-<li>Hifiasm简化了组装流程</li>
-</ol>
-<ul>
-<li>内置了清除haplotigs之间的重复（duplications）的程序，无需第三方工具（如purge_dups）。</li>
-<li>组装完成后无需polish工具（如pilon，racon）进行polish。</li>
-</ul>
-<h1 id="3-references"><a href="#3-references" class="headerlink" title="3. references"></a>3. references</h1><ol>
-<li>Hifiasm manual：<a href="https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/">https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/</a></li>
-<li>Hifiasm介绍：<a href="https://www.bilibili.com/read/cv18775152/">https://www.bilibili.com/read/cv18775152/</a></li>
-<li>hifiasm组装（多个cell的HiFi输入文件）的不同结果：<a href="https://mdnice.com/writing/25f5a8fe3bfe4474ae1bdcab44604da9">https://mdnice.com/writing/25f5a8fe3bfe4474ae1bdcab44604da9</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>omics</category>
-        <category>genome</category>
-        <category>genome assembly</category>
-      </categories>
-      <tags>
-        <tag>Hi-C</tag>
-        <tag>genome assembly</tag>
-        <tag>third generation sequencing</tag>
-        <tag>TGS</tag>
-        <tag>Hifiasm</tag>
-        <tag>HiFi</tag>
-        <tag>PacBio</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>基因组注释（一）：重复序列注释</title>
-    <url>/2021/08/02/omics_genome.annotation_repeat/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=365639&auto=1&height=32"></iframe></div>
+    <title>基因组注释（一）：重复序列注释</title>
+    <url>/2021/08/02/omics_genome.annotation_repeat/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=365639&auto=1&height=32"></iframe></div>
 
 <p><strong>防坑指南</strong></p>
 <ol>
@@ -13206,263 +12846,29 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用Hifiasm组装基因组：（二）Hifiasm软件的算法</title>
-    <url>/2024/06/20/omics_genome.assembly_TGS_Hifiasm_02.algorithm/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2154110020&auto=1&height=32"></iframe></div>
+    <title>用Hifiasm组装基因组：（四）用Hifiasm软件组装基因组的操作</title>
+    <url>/2024/06/22/omics_genome.assembly_TGS_Hifiasm_04.operate/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2153862323&auto=1&height=32"></iframe></div>
 
-<h1 id="1-Hifiasm软件的基本算法"><a href="#1-Hifiasm软件的基本算法" class="headerlink" title="1. Hifiasm软件的基本算法"></a>1. Hifiasm软件的基本算法</h1><p>Hifiasm软件由哈佛大学李恒团队在2021年2月份发表在Nature Methods上。</p>
-<p>Hifiasm组装主要分为三步：</p>
-<ol>
-<li>校正测序错误</li>
-<li>构建分型字符串图（phased string graph）</li>
-<li>单倍体分型组装</li>
+<h1 id="1-Hifiasm基本用法"><a href="#1-Hifiasm基本用法" class="headerlink" title="1. Hifiasm基本用法"></a>1. Hifiasm基本用法</h1><h2 id="1-1-安装Hifiasm"><a href="#1-1-安装Hifiasm" class="headerlink" title="1.1. 安装Hifiasm"></a>1.1. 安装Hifiasm</h2><ol>
+<li>git安装</li>
 </ol>
-<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41592-020-01056-5/MediaObjects/41592_2020_1056_Fig1_HTML.png?as=webp" width=90% title="Outline of the hifiasm algorithm" alt="Outline of the hifiasm algorithm" align=center/>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/chhylp123/hifiasm</span><br><span class="line">cd hifisam &amp;&amp; make</span><br></pre></td></tr></table></figure>
 
-<p><strong><p align="center">Figure 1. Hifiasm算法概况</strong> 图片来源：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></p></p>
-<h2 id="1-1-校正测序错误"><a href="#1-1-校正测序错误" class="headerlink" title="1.1. 校正测序错误"></a>1.1. 校正测序错误</h2><ol>
-<li>尽管Hifi reads使用CCS测序模式已经进行了一轮校正，准确性已经比CLR测序模式高很多，但仍然会有部分测序(&lt;1%)错误。</li>
-<li>Hifiasm进行所有序列的相互比对(all-versus-all)来校正可能的测序错误。</li>
-<li>在比对中基于reads间的overlap关系来校正错误。如果在比对的同一个位置出现两种碱基类型（不考虑gaps），且每个碱基类型至少有3条reads支持，那么这个位置会被当作杂合位点（SNP）被保留。在这一步，Hifiasm可以对杂合SNP进行定相&#x2F;分型（phasing）。</li>
-<li>如果达不到上述条件的两碱基比对，两种碱基中较少的一种被视作测序错误，将被校正（默认三轮校正）。值得注意的是，Hifiasm只使用相同单倍型的数据进行纠错，从而避免过度校正，保留来自不同单倍型的杂合变异信息。</li>
-</ol>
-<h2 id="1-2-构建分型字符串图（phased-string-graph）"><a href="#1-2-构建分型字符串图（phased-string-graph）" class="headerlink" title="1.2. 构建分型字符串图（phased string graph）"></a>1.2. 构建分型字符串图（phased string graph）</h2><ol>
-<li>基于第一步校正后的reads和reads之间的重叠关系（overlap），构建分型字符串图（phased string graph）。</li>
-<li>以调整朝向的reads作为顶点(vertex)，一致的overlap重叠区域作为边(edge)，通过气泡 （bubble）的形式形成多条路径来表示杂合位点信息，因而可以保留下来基因组上全部的单倍型信息，以便后续对于单倍型的处理。</li>
-</ol>
-<h2 id="1-3-单倍体分型组装"><a href="#1-3-单倍体分型组装" class="headerlink" title="1.3. 单倍体分型组装"></a>1.3. 单倍体分型组装</h2><p><strong>HiFi-only assembly 模式</strong>：如果没有其他数据，如亲本数据，Hi-C数据，使用此模式进行单倍体分型组装。</p>
-<ol>
-<li>hifiasm 会随机选择每个气泡的一边构建primary assembly（与 Falcon-Unzip 和 HiCanu 类似的主装配）。</li>
-<li>对于杂合基因组，由于存在一个以上的纯合haplotype，因此primary assembly可能还会包含haplotigs。</li>
-<li>HiCanu 依靠第三方工具（如 purge_dups20）来去除多余的haplotigs。Hifiasm 集成实现了 purge_dups 算法的一个变体，简化了组装流程。</li>
-</ol>
-<h1 id="2-Hifiasm-trio-算法：Trio-binning模式"><a href="#2-Hifiasm-trio-算法：Trio-binning模式" class="headerlink" title="2. Hifiasm(trio)算法：Trio-binning模式"></a>2. Hifiasm(trio)算法：Trio-binning模式</h1><p>如果样本的亲本也进行了测序，则可以使用Trio-binning模式。Trio-binning模式主要在单倍体分型组装步骤上与上面不同。</p>
-<p>Hifiasm的trio-binning模式基于2018年发表的TrioCanu软件的trio-binning策略，并进行改进。</p>
-<h2 id="2-1-早期软件TrioCanu"><a href="#2-1-早期软件TrioCanu" class="headerlink" title="2.1. 早期软件TrioCanu"></a>2.1. 早期软件TrioCanu</h2><ol>
-<li>2018年开发的<strong>TrioCanu软件的trio-binning策略</strong>先将三代reads区分为来自父本、母本以及部分无法区分的reads，对区分后的reads分别组装获得了子代的两套单倍体序列。</li>
-<li>TrioCanu软件的trio-binning策略的一个主要缺陷是，一部分杂合子reads无法明确地划分为亲本单倍型：如果双亲在某个位点上都是杂合，那么这个位点无法给reads提供有效的kmer信息，并且reads不能被唯一地被分配给一个亲本单倍型；如果父本在一个位点是杂合的，而母本是纯合的，则reads也无法唯一地被分配给母本单倍型。在标准的trio-binning策略中，无法区分的杂合reads在两个亲本数据集中都会使用。因此，两个亲本的等位基因可能存在于一个单倍型组合中，从而导致错误的重复。另外还可能存在将reads错误划分到其中一个亲本的情况。</li>
-<li>总言之，TrioCanu软件的标准trio-binning策略无法清楚地分离两个亲本单倍型。</li>
+<ol start="2">
+<li>conda安装</li>
 </ol>
-<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fnbt.4277/MediaObjects/41587_2018_Article_BFnbt4277_Fig1_HTML.jpg?as=webp" width=90% title="Outline of trio binning and haplotype assembly" alt="Outline of trio binning and haplotype assembly" align=center/>
-
-<p><strong><p align="center">Figure 2. TrioCanu软件的trio-binning策略</strong> 图片来源：<a href="https://www.nature.com/articles/nbt.4277">https://www.nature.com/articles/nbt.4277</a></p></p>
-<h2 id="2-2-Hifiasm的trio-binning模式"><a href="#2-2-Hifiasm的trio-binning模式" class="headerlink" title="2.2. Hifiasm的trio-binning模式"></a>2.2. Hifiasm的trio-binning模式</h2><ol>
-<li>与TrioCanu软件的trio-binning策略不同，Hifiasm使用了graph-binning的策略对此进行了改进。</li>
+<ul>
+<li><code>conda install -c bioconda hifiasm</code></li>
+</ul>
+<h2 id="1-2-Hifiasm的基础命令"><a href="#1-2-Hifiasm的基础命令" class="headerlink" title="1.2. Hifiasm的基础命令"></a>1.2. Hifiasm的基础命令</h2><ol>
+<li>命令</li>
 </ol>
 <ul>
-<li>Hifiasm 不预先划分reads，而是利用亲本特有的 k-mer trio binning 对字符串图（string graph）的reads进行标记。</li>
-<li>因此在一个代表一对杂合等位基因的长bubble中，即使只有一小部分reads被正确标记，hifiasm也可以正确地将其分型。</li>
-<li>标记之后，hifiasm 会有效地剔除母本的unitigs来生成父本的序列，反之亦然。</li>
+<li><code>nohup hifiasm -o sample_prefix -t 32 --h1 sample_HiC_1.fq.gz --h2 sample_HiC_2.fq.gz Hifi.fastq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
 </ul>
 <ol start="2">
-<li>Hifiasm的trio-binning模式的优势</li>
-</ol>
-<ul>
-<li>通过这种方式，可以避免因为reads分型错误而引入的错误位点和组装断裂，避免错误地将双亲等位基因放在一个单倍型组装中，从而获得更完整和更准确的单倍体组装结果。</li>
-<li>这种基于三元组分选的图（graph-based trio binning）可能会经过三元组（trio）中所有三个样本的杂合区域，对错误分型的reads的鲁棒性更高。</li>
-</ul>
-<ol start="3">
-<li>Hifiasm的trio-binning模式修复分型错误</li>
-</ol>
-<ul>
-<li>基于 Hi-C 或 Strand-seq 的分型可以明确地对大多数杂合reads进行分型，自然不会出现错误的重复。</li>
-<li>不过，它们也有标准trio-binning策略的共同问题：分配给错误亲本单倍型的reads可能会破坏contigs（Figure 3）。</li>
-<li>通过考虑 HiFi reads 分型和组装图（assembly graph）的结构，Hifiasm 可以识别并修复这类分型错误。</li>
-</ul>
-<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41592-020-01056-5/MediaObjects/41592_2020_1056_Fig2_HTML.png?as=webp" width=90% title="Effect of false read binning" alt="Effect of false read binning" align=center/>
-
-<p><strong><p align="center">Figure 3. 错误的reads分型在Hifiasm的trio-binning模式中会被修正</strong> 图片来源：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></p></p>
-<h1 id="3-新算法Hifiasm-Hi-C-：Hi-C-Integrated-assembly-模式"><a href="#3-新算法Hifiasm-Hi-C-：Hi-C-Integrated-assembly-模式" class="headerlink" title="3. 新算法Hifiasm(Hi-C)：Hi-C Integrated assembly 模式"></a>3. 新算法Hifiasm(Hi-C)：Hi-C Integrated assembly 模式</h1><p>李恒团队2022年在Nature biotechnology上发表论文Haplotype-resolved assembly of diploid genomes without parental data（<a href="https://www.nature.com/articles/s41587-022-01261-x%EF%BC%89%EF%BC%8C%E5%9C%A8Hifiasm%E4%B8%AD%E5%BC%95%E5%85%A5%E4%BA%86%E6%96%B0%E7%AE%97%E6%B3%95Hifiasm(Hi-C)%EF%BC%8C%E5%8F%AF%E4%BB%A5%E4%BD%BF%E7%94%A8Hi-C">https://www.nature.com/articles/s41587-022-01261-x），在Hifiasm中引入了新算法Hifiasm(Hi-C)，可以使用Hi-C</a> Integrated assembly 模式进行单倍体分型组装。</p>
-<h2 id="3-1-Hi-C-Integrated-assembly模式"><a href="#3-1-Hi-C-Integrated-assembly模式" class="headerlink" title="3.1. Hi-C Integrated assembly模式"></a>3.1. Hi-C Integrated assembly模式</h2><ol>
-<li>Hi-C Integrated assembly模式针对PacBio HiFi (High-Fidelity) 长读长测序技术和Hi-C (High-Throughput Chromatin Confirmation Capture) 测序技术进行了全新的设计。</li>
-<li>在无亲本数据的情况下，利用至少30倍覆盖度的HiFi数据和至少30倍覆盖度的Hi-C数据也可以获得二倍体生物的单倍型解决的组装结果。</li>
-<li>它建立在分型 hifiasm 组装图 （assembly graphs）的基础上，但在序列分类（sequence partition）方面与已发表的 hifiasm (trio) 算法不同。</li>
-<li>在 hifiasm graph中，每个节点（node）都是由相位正确的 HiFi reads组装而成的unitig，每条边（edge）代表两个unitigs之间的重叠。</li>
-<li>Hifiasm（trio）算法在亲本 k-mers 的unitigs中标记reads，但 Hifiasm（Hi-C）用 Hi-C reads对相对较短的unitigs进行分类。</li>
-</ol>
-<h2 id="3-2-Hifiasm-Hi-C-算法"><a href="#3-2-Hifiasm-Hi-C-算法" class="headerlink" title="3.2. Hifiasm(Hi-C)算法"></a>3.2. Hifiasm(Hi-C)算法</h2><ol>
-<li>Hifiasm（Hi-C）算法，先检索unitigs中的 31-mers，并将 Hi-C reads 映射到这些unitigs中，而不进行详细的碱基比对。如果来自 Hi-C 片段的一对reads与两个unitigs上的两个远距离杂合子相匹配，就会在unitigs之间添加一个单倍型特异性 “link”，从而提供远距离的相位信息。</li>
-<li>然后，把unitigs分类到两个类别中，使每个类别中的unitigs几乎没有冗余，并且每个类别共享许多 Hi-C “link”。</li>
-<li>这样就将单元双分类问题简化为图最大切割（Max-Cut）问题，并通过随机算法(stochastic algorithm)找到接近最优的解决方案。</li>
-<li>还考虑了组装图 （assembly graphs）的拓扑结构，以减少局部最优的概率。</li>
-<li>最后，使用与 Hifiasm（trio）算法相同的图分选策略(graph binning strategy)，生成最终的 Hifiasm（Hi-C）组装结果。</li>
-</ol>
-<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41587-022-01261-x/MediaObjects/41587_2022_1261_Fig1_HTML.png?as=webp" width=90% title="Haplotype-resolved assembly using Hi-C data" alt="Haplotype-resolved assembly using Hi-C data" align=center/>
-
-<p><strong><p align="center">Figure 4. Hifiasm（Hi-C）算法</strong> 图片来源：<a href="https://www.nature.com/articles/s41587-022-01261-x">https://www.nature.com/articles/s41587-022-01261-x</a></p></p>
-<h2 id="3-3-Hifiasm（Hi-C）算法与其他工具相比的优势"><a href="#3-3-Hifiasm（Hi-C）算法与其他工具相比的优势" class="headerlink" title="3.3. Hifiasm（Hi-C）算法与其他工具相比的优势"></a>3.3. Hifiasm（Hi-C）算法与其他工具相比的优势</h2><p>与现有基于Hi-C组装单倍体基因组的方法不同，Hifiasm（Hi-C）算法直接在 HiFi 组装图上运行，并将 Hi-C read mapping、分型（phasing）和组装紧密集成到一个单一的可执行程序中，而不依赖外部工具。它更易于使用，运行速度更快。</p>
-<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li>Hifiasm paper：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></li>
-<li>Hifiasm （Hi-C Integrated assembly 模式）paper：<a href="https://www.nature.com/articles/s41587-022-01261-x">https://www.nature.com/articles/s41587-022-01261-x</a></li>
-<li>Hifiasm介绍：<a href="https://www.bilibili.com/read/cv18775152/">https://www.bilibili.com/read/cv18775152/</a></li>
-<li><a href="https://www.jianshu.com/p/9dc0b5c5af81">https://www.jianshu.com/p/9dc0b5c5af81</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
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-      <categories>
-        <category>omics</category>
-        <category>genome</category>
-        <category>genome assembly</category>
-      </categories>
-      <tags>
-        <tag>Hi-C</tag>
-        <tag>genome assembly</tag>
-        <tag>third generation sequencing</tag>
-        <tag>TGS</tag>
-        <tag>Hifiasm</tag>
-        <tag>HiFi</tag>
-        <tag>PacBio</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用Hifiasm组装基因组：（三）Hifiasm软件组装基因组的多种模式</title>
-    <url>/2024/06/21/omics_genome.assembly_TGS_Hifiasm_03.modes/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2153506013&auto=1&height=32"></iframe></div>
-
-
-<h1 id="1-Hifiasm组装基因组的模式"><a href="#1-Hifiasm组装基因组的模式" class="headerlink" title="1. Hifiasm组装基因组的模式"></a>1. Hifiasm组装基因组的模式</h1><p>根据可用的数据不同，Hifiasm组装基因组在HiFi数据基础上，还有几种模式可以增加组装的完整度和准确度。</p>
-<ol>
-<li>HiFi-only assembly 模式（只有HiFi数据）</li>
-<li>Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）</li>
-<li>Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）</li>
-<li>端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）</li>
-</ol>
-<h1 id="2-HiFi-only-assembly-模式（只有HiFi数据）"><a href="#2-HiFi-only-assembly-模式（只有HiFi数据）" class="headerlink" title="2. HiFi-only assembly 模式（只有HiFi数据）"></a>2. HiFi-only assembly 模式（只有HiFi数据）</h1><h2 id="2-1-经典模式"><a href="#2-1-经典模式" class="headerlink" title="2.1. 经典模式"></a>2.1. 经典模式</h2><ol>
-<li>命令</li>
-</ol>
-<ul>
-<li><code>nohup hifiasm -o sample_prefix -t 32 Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
-</ul>
-<ol start="2">
-<li>参数</li>
-</ol>
-<ul>
-<li>HiFi reads可以是fq或fa格式（fq的质量值会被忽略），可以是gz压缩格式。</li>
-<li>-o指定输出文件前缀；-t指定线程。</li>
-<li>用命令<code>2&gt;&amp;1 &gt;hifiasm.log</code>保存日志和报错内容到hifiasm.log文件。</li>
-</ul>
-<h2 id="2-2-两种组装方式"><a href="#2-2-两种组装方式" class="headerlink" title="2.2. 两种组装方式"></a>2.2. 两种组装方式</h2><ol>
-<li>单倍体分型组装（two partially phased assembly）</li>
-</ol>
-<ul>
-<li>默认是以此方式组装。</li>
-<li>单倍体分型组装生成一对文件（asm.bp.hap1.p_ctg.gfa和asm.bp.hap2.p_ctg.gfa），代表二倍体的两个单倍型。同时也会生成primary contigs文件asm.bp.p_ctg.gfa。</li>
-</ul>
-<ol start="2">
-<li>primary&#x2F;alternate组装</li>
-</ol>
-<ul>
-<li>加一个参数<code>--primary</code>则指定primary&#x2F;alternate组装方式。</li>
-<li>命令：<code>nohup hifiasm -o sample_prefix -t 32 --primary Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
-<li>分别生成primary contigs和alternate contigs文件asm.p_ctg.gfa和asm._ctg.gfa。</li>
-</ul>
-<h1 id="3-Trio-binning模式（HiFi数据-父母本二代Illumina测序数据）"><a href="#3-Trio-binning模式（HiFi数据-父母本二代Illumina测序数据）" class="headerlink" title="3. Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）"></a>3. Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）</h1><ol>
-<li>介绍</li>
-</ol>
-<ul>
-<li>当父母本的二代Illumina reads可用时，也可以通过trio binning生成一对解析的单倍型的组装。</li>
-<li>Hifiasm中用到的trio binning 技术是指利用父本、母本和子代的遗传信息对子代的单倍型划分的方法。该方法的有效性随着杂合度的增加而提高，极大地提升了等位基因组的组装质量。</li>
-</ul>
-<ol start="2">
-<li>命令</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">trio-binning模式需要额外安装yak，两种安装方式任选一种</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash"><span class="built_in">source</span> code</span></span><br><span class="line">git clone https://github.com/lh3/yak</span><br><span class="line">cd yak &amp;&amp; make</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">bioncda</span></span><br><span class="line">conda install -c bioconda yak</span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">运行组装</span></span><br><span class="line">yak count -b37 -t16 -o pat.yak &lt;(cat paternal_1.fq.gz paternal_2.fq.gz) &lt;(cat paternal_1.fq.gz paternal_2.fq.gz)</span><br><span class="line">yak count -b37 -t16 -o mat.yak &lt;(cat maternal_1.fq.gz maternal_2.fq.gz) &lt;(cat maternal_1.fq.gz maternal_2.fq.gz)</span><br><span class="line">hifiasm -o sample_prefix -t 32 -1 pat.yak -2 mat.yak Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>参数解释</li>
-</ol>
-<ul>
-<li>命令中Illumina双端测序的父本paternal数据和母本maternal数据同时使用</li>
-</ul>
-<h1 id="4-Hi-C-Integrated-assembly-模式（HiFi数据-Hi-C数据）"><a href="#4-Hi-C-Integrated-assembly-模式（HiFi数据-Hi-C数据）" class="headerlink" title="4. Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）"></a>4. Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）</h1><ol>
-<li>介绍</li>
-</ol>
-<ul>
-<li>当Hi-C数据可用时，可以生成一对解析的单倍型的组装。</li>
-<li>李恒团队2022年在Nature biotechnology上发表论文Haplotype-resolved assembly of diploid genomes without parental data（<a href="https://www.nature.com/articles/s41587-022-01261-x%EF%BC%89%EF%BC%8C%E5%9C%A8Hifiasm%E4%B8%AD%E5%BC%95%E5%85%A5%E4%BA%86Hi-C">https://www.nature.com/articles/s41587-022-01261-x），在Hifiasm中引入了Hi-C</a> Integrated assembly 模式。</li>
-<li>Hi-C Integrated assembly模式针对PacBio HiFi (High-Fidelity) 长读长测序技术和Hi-C (High-Throughput Chromatin Confirmation Capture) 测序技术进行了全新的设计。</li>
-<li>该算法结合了HiFi数据中精确的局部单倍型信息和Hi-C数据中的长距离互作用信息以达到全局定相 (phasing)，从而获得不依赖亲本信息的染色体级别的单倍型组装结果。为了进一步提高组装质量，作者充分利用了组装图中的结构信息，以及其前期研究中的Graph-binning等策略。</li>
-<li>这个模式组装后的基因组还未挂载在染色体上，仍然需要Juicer+3ddna+juicebox等软件进行染色体挂载。</li>
-<li>这个模式的数据最易获得，所以也很常用。</li>
-</ul>
-<ol start="2">
-<li>命令</li>
-</ol>
-<ul>
-<li><code>nohup hifiasm -o sample_prefix -t 32  --h1 HiC_1.fq.gz --h2 sample_HiC_2.fq.gz Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
-</ul>
-<ol start="3">
-<li>参数</li>
-</ol>
-<ul>
-<li>用–h1和–h2指定Hi-C数据。</li>
-</ul>
-<h1 id="5-端对端组装：HiFi-ONT模式（HiFi数据-ONT超长reads数据）"><a href="#5-端对端组装：HiFi-ONT模式（HiFi数据-ONT超长reads数据）" class="headerlink" title="5. 端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）"></a>5. 端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）</h1><p>当ONT数据可用时，可以集成超长ONT数据生成端粒到端粒的组装</p>
-<ol>
-<li>命令</li>
-</ol>
-<ul>
-<li><code>nohup hifiasm -o sample_prefix -t 32 --ul ONT.fq.gz Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
-</ul>
-<ol start="2">
-<li>参数</li>
-</ol>
-<ul>
-<li>用–ul指定ONT数据。</li>
-</ul>
-<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
-<li>Hifiasm manual：<a href="https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/">https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>omics</category>
-        <category>genome</category>
-        <category>genome assembly</category>
-      </categories>
-      <tags>
-        <tag>Hi-C</tag>
-        <tag>genome assembly</tag>
-        <tag>third generation sequencing</tag>
-        <tag>TGS</tag>
-        <tag>Hifiasm</tag>
-        <tag>HiFi</tag>
-        <tag>PacBio</tag>
-        <tag>ONT</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用Hifiasm组装基因组：（四）用Hifiasm软件组装基因组的操作</title>
-    <url>/2024/06/22/omics_genome.assembly_TGS_Hifiasm_04.operate/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2153862323&auto=1&height=32"></iframe></div>
-
-<h1 id="1-Hifiasm基本用法"><a href="#1-Hifiasm基本用法" class="headerlink" title="1. Hifiasm基本用法"></a>1. Hifiasm基本用法</h1><h2 id="1-1-安装Hifiasm"><a href="#1-1-安装Hifiasm" class="headerlink" title="1.1. 安装Hifiasm"></a>1.1. 安装Hifiasm</h2><ol>
-<li>git安装</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/chhylp123/hifiasm</span><br><span class="line">cd hifisam &amp;&amp; make</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>conda安装</li>
-</ol>
-<ul>
-<li><code>conda install -c bioconda hifiasm</code></li>
-</ul>
-<h2 id="1-2-Hifiasm的基础命令"><a href="#1-2-Hifiasm的基础命令" class="headerlink" title="1.2. Hifiasm的基础命令"></a>1.2. Hifiasm的基础命令</h2><ol>
-<li>命令</li>
-</ol>
-<ul>
-<li><code>nohup hifiasm -o sample_prefix -t 32 --h1 sample_HiC_1.fq.gz --h2 sample_HiC_2.fq.gz Hifi.fastq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
-</ul>
-<ol start="2">
-<li>参数</li>
+<li>参数</li>
 </ol>
 <ul>
 <li>-o指定输出文件前缀；-t指定线程；–h1和–h2指定Hi-C数据；后接HiFi数据。</li>
@@ -13701,33 +13107,146 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>基因组组装和注释结果的整理</title>
-    <url>/2021/08/10/omics_genome.management/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1833352&auto=1&height=32"></iframe></div>
-
+    <title>用Hifiasm组装基因组：（二）Hifiasm软件的算法</title>
+    <url>/2024/06/20/omics_genome.assembly_TGS_Hifiasm_02.algorithm/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2154110020&auto=1&height=32"></iframe></div>
 
-<h1 id="1-基因组整理【组装之后，注释之前】"><a href="#1-基因组整理【组装之后，注释之前】" class="headerlink" title="1. 基因组整理【组装之后，注释之前】"></a>1. 基因组整理【组装之后，注释之前】</h1><h2 id="1-1-基因组碱基整理"><a href="#1-1-基因组碱基整理" class="headerlink" title="1.1. 基因组碱基整理"></a>1.1. 基因组碱基整理</h2><p>基因组碱基-u修改成大写字母形式，同时建议使用-w 0把序列输出限定为一行序列。</p>
-<p><code>seqkit seq -u -w 0 genome.old.fa&gt;genome.new.fa</code></p>
-<h2 id="1-2-基因组碱基小写带来的问题："><a href="#1-2-基因组碱基小写带来的问题：" class="headerlink" title="1.2. 基因组碱基小写带来的问题："></a>1.2. 基因组碱基小写带来的问题：</h2><ol>
-<li>edta运行TIR预测时，报错并中断运行。</li>
+<h1 id="1-Hifiasm软件的基本算法"><a href="#1-Hifiasm软件的基本算法" class="headerlink" title="1. Hifiasm软件的基本算法"></a>1. Hifiasm软件的基本算法</h1><p>Hifiasm软件由哈佛大学李恒团队在2021年2月份发表在Nature Methods上。</p>
+<p>Hifiasm组装主要分为三步：</p>
+<ol>
+<li>校正测序错误</li>
+<li>构建分型字符串图（phased string graph）</li>
+<li>单倍体分型组装</li>
 </ol>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">EDTA/bin/TIR-Learner2.5/Module2/RunGRF.py&quot;, line 79, in &lt;module&gt;</span><br><span class="line">    if (len(str(records[0].seq))&gt;int(length)+500):</span><br><span class="line">IndexError: list index out of range</span><br><span class="line">cp: cannot stat &#x27;TIR-Learner/*-p&#x27;: No such file or directory</span><br></pre></td></tr></table></figure>
+<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41592-020-01056-5/MediaObjects/41592_2020_1056_Fig1_HTML.png?as=webp" width=90% title="Outline of the hifiasm algorithm" alt="Outline of the hifiasm algorithm" align=center/>
 
-<ol start="2">
-<li>geta生成基因注释结果减少。<br>前期运行不受影响，会在geta生成的&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;中合并前面步骤预测结果时把许多基因预测结果识别为partial而筛除掉本为完整的基因，可看到&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;genome.gff3的gene数量是正常的，而out.tmp&#x2F;6.combineGeneModels&#x2F;genome.completed.gff3明显降低，&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;genome.partial.gff3明显升高。out.tmp&#x2F;6.combineGeneModels&#x2F;genome.filter.gff3是最后的基因注释结果，即out.gff3也相应减少。</li>
-<li>maker的某些运行步骤也会出错。</li>
+<p><strong><p align="center">Figure 1. Hifiasm算法概况</strong> 图片来源：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></p></p>
+<h2 id="1-1-校正测序错误"><a href="#1-1-校正测序错误" class="headerlink" title="1.1. 校正测序错误"></a>1.1. 校正测序错误</h2><ol>
+<li>尽管Hifi reads使用CCS测序模式已经进行了一轮校正，准确性已经比CLR测序模式高很多，但仍然会有部分测序(&lt;1%)错误。</li>
+<li>Hifiasm进行所有序列的相互比对(all-versus-all)来校正可能的测序错误。</li>
+<li>在比对中基于reads间的overlap关系来校正错误。如果在比对的同一个位置出现两种碱基类型（不考虑gaps），且每个碱基类型至少有3条reads支持，那么这个位置会被当作杂合位点（SNP）被保留。在这一步，Hifiasm可以对杂合SNP进行定相&#x2F;分型（phasing）。</li>
+<li>如果达不到上述条件的两碱基比对，两种碱基中较少的一种被视作测序错误，将被校正（默认三轮校正）。值得注意的是，Hifiasm只使用相同单倍型的数据进行纠错，从而避免过度校正，保留来自不同单倍型的杂合变异信息。</li>
 </ol>
-<h2 id="1-3-基因组序列ID修改"><a href="#1-3-基因组序列ID修改" class="headerlink" title="1.3. 基因组序列ID修改"></a>1.3. 基因组序列ID修改</h2><h3 id="1-3-1-排序【optional】–根据需要选择"><a href="#1-3-1-排序【optional】–根据需要选择" class="headerlink" title="1.3.1. 排序【optional】–根据需要选择"></a>1.3.1. 排序【optional】–根据需要选择</h3><p>按长度对基因组contigs进行从长到短的排序</p>
-<p><code>seqkit sort -l -r -w 0 genome.old.fa &gt;genome.sort.fa</code> </p>
-<h3 id="1-3-2-重命名【推荐】"><a href="#1-3-2-重命名【推荐】" class="headerlink" title="1.3.2. 重命名【推荐】"></a>1.3.2. 重命名【推荐】</h3><p>最好重命名成长度一致的数字，避免后期分析遇到contig1和contig11同时被匹配的问题。</p>
+<h2 id="1-2-构建分型字符串图（phased-string-graph）"><a href="#1-2-构建分型字符串图（phased-string-graph）" class="headerlink" title="1.2. 构建分型字符串图（phased string graph）"></a>1.2. 构建分型字符串图（phased string graph）</h2><ol>
+<li>基于第一步校正后的reads和reads之间的重叠关系（overlap），构建分型字符串图（phased string graph）。</li>
+<li>以调整朝向的reads作为顶点(vertex)，一致的overlap重叠区域作为边(edge)，通过气泡 （bubble）的形式形成多条路径来表示杂合位点信息，因而可以保留下来基因组上全部的单倍型信息，以便后续对于单倍型的处理。</li>
+</ol>
+<h2 id="1-3-单倍体分型组装"><a href="#1-3-单倍体分型组装" class="headerlink" title="1.3. 单倍体分型组装"></a>1.3. 单倍体分型组装</h2><p><strong>HiFi-only assembly 模式</strong>：如果没有其他数据，如亲本数据，Hi-C数据，使用此模式进行单倍体分型组装。</p>
 <ol>
-<li>solution A【速度最快，推荐】<br>序列IDs按顺序命名为1-n的数字，–nr-width可以设置数字的长度，长度为5时第一条序列名称为00001。</li>
+<li>hifiasm 会随机选择每个气泡的一边构建primary assembly（与 Falcon-Unzip 和 HiCanu 类似的主装配）。</li>
+<li>对于杂合基因组，由于存在一个以上的纯合haplotype，因此primary assembly可能还会包含haplotigs。</li>
+<li>HiCanu 依靠第三方工具（如 purge_dups20）来去除多余的haplotigs。Hifiasm 集成实现了 purge_dups 算法的一个变体，简化了组装流程。</li>
 </ol>
-<p><code>seqkit replace -p &#39;.*&#39; -r &#123;nr&#125; --nr-width 5 genome.sort.fa</code> </p>
-<ol start="2">
-<li>solution B<br>把序列id重命名成1-n的数字</li>
+<h1 id="2-Hifiasm-trio-算法：Trio-binning模式"><a href="#2-Hifiasm-trio-算法：Trio-binning模式" class="headerlink" title="2. Hifiasm(trio)算法：Trio-binning模式"></a>2. Hifiasm(trio)算法：Trio-binning模式</h1><p>如果样本的亲本也进行了测序，则可以使用Trio-binning模式。Trio-binning模式主要在单倍体分型组装步骤上与上面不同。</p>
+<p>Hifiasm的trio-binning模式基于2018年发表的TrioCanu软件的trio-binning策略，并进行改进。</p>
+<h2 id="2-1-早期软件TrioCanu"><a href="#2-1-早期软件TrioCanu" class="headerlink" title="2.1. 早期软件TrioCanu"></a>2.1. 早期软件TrioCanu</h2><ol>
+<li>2018年开发的<strong>TrioCanu软件的trio-binning策略</strong>先将三代reads区分为来自父本、母本以及部分无法区分的reads，对区分后的reads分别组装获得了子代的两套单倍体序列。</li>
+<li>TrioCanu软件的trio-binning策略的一个主要缺陷是，一部分杂合子reads无法明确地划分为亲本单倍型：如果双亲在某个位点上都是杂合，那么这个位点无法给reads提供有效的kmer信息，并且reads不能被唯一地被分配给一个亲本单倍型；如果父本在一个位点是杂合的，而母本是纯合的，则reads也无法唯一地被分配给母本单倍型。在标准的trio-binning策略中，无法区分的杂合reads在两个亲本数据集中都会使用。因此，两个亲本的等位基因可能存在于一个单倍型组合中，从而导致错误的重复。另外还可能存在将reads错误划分到其中一个亲本的情况。</li>
+<li>总言之，TrioCanu软件的标准trio-binning策略无法清楚地分离两个亲本单倍型。</li>
 </ol>
-<p><code>awk &#39;BEGIN&#123;i=0 ; FS=&quot;,&quot; ; OFS=&quot;,&quot;&#125;&#123; if(/&gt;/)&#123;gsub($1,&quot;&gt;&quot;++i,$1);print $0&#125;else&#123;print $0&#125;&#125;&#39; genome.sort.fa&gt;genome.fa</code> </p>
+<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fnbt.4277/MediaObjects/41587_2018_Article_BFnbt4277_Fig1_HTML.jpg?as=webp" width=90% title="Outline of trio binning and haplotype assembly" alt="Outline of trio binning and haplotype assembly" align=center/>
+
+<p><strong><p align="center">Figure 2. TrioCanu软件的trio-binning策略</strong> 图片来源：<a href="https://www.nature.com/articles/nbt.4277">https://www.nature.com/articles/nbt.4277</a></p></p>
+<h2 id="2-2-Hifiasm的trio-binning模式"><a href="#2-2-Hifiasm的trio-binning模式" class="headerlink" title="2.2. Hifiasm的trio-binning模式"></a>2.2. Hifiasm的trio-binning模式</h2><ol>
+<li>与TrioCanu软件的trio-binning策略不同，Hifiasm使用了graph-binning的策略对此进行了改进。</li>
+</ol>
+<ul>
+<li>Hifiasm 不预先划分reads，而是利用亲本特有的 k-mer trio binning 对字符串图（string graph）的reads进行标记。</li>
+<li>因此在一个代表一对杂合等位基因的长bubble中，即使只有一小部分reads被正确标记，hifiasm也可以正确地将其分型。</li>
+<li>标记之后，hifiasm 会有效地剔除母本的unitigs来生成父本的序列，反之亦然。</li>
+</ul>
+<ol start="2">
+<li>Hifiasm的trio-binning模式的优势</li>
+</ol>
+<ul>
+<li>通过这种方式，可以避免因为reads分型错误而引入的错误位点和组装断裂，避免错误地将双亲等位基因放在一个单倍型组装中，从而获得更完整和更准确的单倍体组装结果。</li>
+<li>这种基于三元组分选的图（graph-based trio binning）可能会经过三元组（trio）中所有三个样本的杂合区域，对错误分型的reads的鲁棒性更高。</li>
+</ul>
+<ol start="3">
+<li>Hifiasm的trio-binning模式修复分型错误</li>
+</ol>
+<ul>
+<li>基于 Hi-C 或 Strand-seq 的分型可以明确地对大多数杂合reads进行分型，自然不会出现错误的重复。</li>
+<li>不过，它们也有标准trio-binning策略的共同问题：分配给错误亲本单倍型的reads可能会破坏contigs（Figure 3）。</li>
+<li>通过考虑 HiFi reads 分型和组装图（assembly graph）的结构，Hifiasm 可以识别并修复这类分型错误。</li>
+</ul>
+<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41592-020-01056-5/MediaObjects/41592_2020_1056_Fig2_HTML.png?as=webp" width=90% title="Effect of false read binning" alt="Effect of false read binning" align=center/>
+
+<p><strong><p align="center">Figure 3. 错误的reads分型在Hifiasm的trio-binning模式中会被修正</strong> 图片来源：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></p></p>
+<h1 id="3-新算法Hifiasm-Hi-C-：Hi-C-Integrated-assembly-模式"><a href="#3-新算法Hifiasm-Hi-C-：Hi-C-Integrated-assembly-模式" class="headerlink" title="3. 新算法Hifiasm(Hi-C)：Hi-C Integrated assembly 模式"></a>3. 新算法Hifiasm(Hi-C)：Hi-C Integrated assembly 模式</h1><p>李恒团队2022年在Nature biotechnology上发表论文Haplotype-resolved assembly of diploid genomes without parental data（<a href="https://www.nature.com/articles/s41587-022-01261-x%EF%BC%89%EF%BC%8C%E5%9C%A8Hifiasm%E4%B8%AD%E5%BC%95%E5%85%A5%E4%BA%86%E6%96%B0%E7%AE%97%E6%B3%95Hifiasm(Hi-C)%EF%BC%8C%E5%8F%AF%E4%BB%A5%E4%BD%BF%E7%94%A8Hi-C">https://www.nature.com/articles/s41587-022-01261-x），在Hifiasm中引入了新算法Hifiasm(Hi-C)，可以使用Hi-C</a> Integrated assembly 模式进行单倍体分型组装。</p>
+<h2 id="3-1-Hi-C-Integrated-assembly模式"><a href="#3-1-Hi-C-Integrated-assembly模式" class="headerlink" title="3.1. Hi-C Integrated assembly模式"></a>3.1. Hi-C Integrated assembly模式</h2><ol>
+<li>Hi-C Integrated assembly模式针对PacBio HiFi (High-Fidelity) 长读长测序技术和Hi-C (High-Throughput Chromatin Confirmation Capture) 测序技术进行了全新的设计。</li>
+<li>在无亲本数据的情况下，利用至少30倍覆盖度的HiFi数据和至少30倍覆盖度的Hi-C数据也可以获得二倍体生物的单倍型解决的组装结果。</li>
+<li>它建立在分型 hifiasm 组装图 （assembly graphs）的基础上，但在序列分类（sequence partition）方面与已发表的 hifiasm (trio) 算法不同。</li>
+<li>在 hifiasm graph中，每个节点（node）都是由相位正确的 HiFi reads组装而成的unitig，每条边（edge）代表两个unitigs之间的重叠。</li>
+<li>Hifiasm（trio）算法在亲本 k-mers 的unitigs中标记reads，但 Hifiasm（Hi-C）用 Hi-C reads对相对较短的unitigs进行分类。</li>
+</ol>
+<h2 id="3-2-Hifiasm-Hi-C-算法"><a href="#3-2-Hifiasm-Hi-C-算法" class="headerlink" title="3.2. Hifiasm(Hi-C)算法"></a>3.2. Hifiasm(Hi-C)算法</h2><ol>
+<li>Hifiasm（Hi-C）算法，先检索unitigs中的 31-mers，并将 Hi-C reads 映射到这些unitigs中，而不进行详细的碱基比对。如果来自 Hi-C 片段的一对reads与两个unitigs上的两个远距离杂合子相匹配，就会在unitigs之间添加一个单倍型特异性 “link”，从而提供远距离的相位信息。</li>
+<li>然后，把unitigs分类到两个类别中，使每个类别中的unitigs几乎没有冗余，并且每个类别共享许多 Hi-C “link”。</li>
+<li>这样就将单元双分类问题简化为图最大切割（Max-Cut）问题，并通过随机算法(stochastic algorithm)找到接近最优的解决方案。</li>
+<li>还考虑了组装图 （assembly graphs）的拓扑结构，以减少局部最优的概率。</li>
+<li>最后，使用与 Hifiasm（trio）算法相同的图分选策略(graph binning strategy)，生成最终的 Hifiasm（Hi-C）组装结果。</li>
+</ol>
+<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41587-022-01261-x/MediaObjects/41587_2022_1261_Fig1_HTML.png?as=webp" width=90% title="Haplotype-resolved assembly using Hi-C data" alt="Haplotype-resolved assembly using Hi-C data" align=center/>
+
+<p><strong><p align="center">Figure 4. Hifiasm（Hi-C）算法</strong> 图片来源：<a href="https://www.nature.com/articles/s41587-022-01261-x">https://www.nature.com/articles/s41587-022-01261-x</a></p></p>
+<h2 id="3-3-Hifiasm（Hi-C）算法与其他工具相比的优势"><a href="#3-3-Hifiasm（Hi-C）算法与其他工具相比的优势" class="headerlink" title="3.3. Hifiasm（Hi-C）算法与其他工具相比的优势"></a>3.3. Hifiasm（Hi-C）算法与其他工具相比的优势</h2><p>与现有基于Hi-C组装单倍体基因组的方法不同，Hifiasm（Hi-C）算法直接在 HiFi 组装图上运行，并将 Hi-C read mapping、分型（phasing）和组装紧密集成到一个单一的可执行程序中，而不依赖外部工具。它更易于使用，运行速度更快。</p>
+<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
+<li>Hifiasm paper：<a href="https://www.nature.com/articles/s41592-020-01056-5">https://www.nature.com/articles/s41592-020-01056-5</a></li>
+<li>Hifiasm （Hi-C Integrated assembly 模式）paper：<a href="https://www.nature.com/articles/s41587-022-01261-x">https://www.nature.com/articles/s41587-022-01261-x</a></li>
+<li>Hifiasm介绍：<a href="https://www.bilibili.com/read/cv18775152/">https://www.bilibili.com/read/cv18775152/</a></li>
+<li><a href="https://www.jianshu.com/p/9dc0b5c5af81">https://www.jianshu.com/p/9dc0b5c5af81</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>omics</category>
+        <category>genome</category>
+        <category>genome assembly</category>
+      </categories>
+      <tags>
+        <tag>Hi-C</tag>
+        <tag>genome assembly</tag>
+        <tag>third generation sequencing</tag>
+        <tag>TGS</tag>
+        <tag>Hifiasm</tag>
+        <tag>HiFi</tag>
+        <tag>PacBio</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>基因组组装和注释结果的整理</title>
+    <url>/2021/08/10/omics_genome.management/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1833352&auto=1&height=32"></iframe></div>
+
+
+<h1 id="1-基因组整理【组装之后，注释之前】"><a href="#1-基因组整理【组装之后，注释之前】" class="headerlink" title="1. 基因组整理【组装之后，注释之前】"></a>1. 基因组整理【组装之后，注释之前】</h1><h2 id="1-1-基因组碱基整理"><a href="#1-1-基因组碱基整理" class="headerlink" title="1.1. 基因组碱基整理"></a>1.1. 基因组碱基整理</h2><p>基因组碱基-u修改成大写字母形式，同时建议使用-w 0把序列输出限定为一行序列。</p>
+<p><code>seqkit seq -u -w 0 genome.old.fa&gt;genome.new.fa</code></p>
+<h2 id="1-2-基因组碱基小写带来的问题："><a href="#1-2-基因组碱基小写带来的问题：" class="headerlink" title="1.2. 基因组碱基小写带来的问题："></a>1.2. 基因组碱基小写带来的问题：</h2><ol>
+<li>edta运行TIR预测时，报错并中断运行。</li>
+</ol>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">EDTA/bin/TIR-Learner2.5/Module2/RunGRF.py&quot;, line 79, in &lt;module&gt;</span><br><span class="line">    if (len(str(records[0].seq))&gt;int(length)+500):</span><br><span class="line">IndexError: list index out of range</span><br><span class="line">cp: cannot stat &#x27;TIR-Learner/*-p&#x27;: No such file or directory</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>geta生成基因注释结果减少。<br>前期运行不受影响，会在geta生成的&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;中合并前面步骤预测结果时把许多基因预测结果识别为partial而筛除掉本为完整的基因，可看到&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;genome.gff3的gene数量是正常的，而out.tmp&#x2F;6.combineGeneModels&#x2F;genome.completed.gff3明显降低，&#x2F;out.tmp&#x2F;6.combineGeneModels&#x2F;genome.partial.gff3明显升高。out.tmp&#x2F;6.combineGeneModels&#x2F;genome.filter.gff3是最后的基因注释结果，即out.gff3也相应减少。</li>
+<li>maker的某些运行步骤也会出错。</li>
+</ol>
+<h2 id="1-3-基因组序列ID修改"><a href="#1-3-基因组序列ID修改" class="headerlink" title="1.3. 基因组序列ID修改"></a>1.3. 基因组序列ID修改</h2><h3 id="1-3-1-排序【optional】–根据需要选择"><a href="#1-3-1-排序【optional】–根据需要选择" class="headerlink" title="1.3.1. 排序【optional】–根据需要选择"></a>1.3.1. 排序【optional】–根据需要选择</h3><p>按长度对基因组contigs进行从长到短的排序</p>
+<p><code>seqkit sort -l -r -w 0 genome.old.fa &gt;genome.sort.fa</code> </p>
+<h3 id="1-3-2-重命名【推荐】"><a href="#1-3-2-重命名【推荐】" class="headerlink" title="1.3.2. 重命名【推荐】"></a>1.3.2. 重命名【推荐】</h3><p>最好重命名成长度一致的数字，避免后期分析遇到contig1和contig11同时被匹配的问题。</p>
+<ol>
+<li>solution A【速度最快，推荐】<br>序列IDs按顺序命名为1-n的数字，–nr-width可以设置数字的长度，长度为5时第一条序列名称为00001。</li>
+</ol>
+<p><code>seqkit replace -p &#39;.*&#39; -r &#123;nr&#125; --nr-width 5 genome.sort.fa</code> </p>
+<ol start="2">
+<li>solution B<br>把序列id重命名成1-n的数字</li>
+</ol>
+<p><code>awk &#39;BEGIN&#123;i=0 ; FS=&quot;,&quot; ; OFS=&quot;,&quot;&#125;&#123; if(/&gt;/)&#123;gsub($1,&quot;&gt;&quot;++i,$1);print $0&#125;else&#123;print $0&#125;&#125;&#39; genome.sort.fa&gt;genome.fa</code> </p>
 <p>基因组的序列id改为scaf001的形式</p>
 <p><code>sed -i &quot;s/&gt;/&gt;scaf/g&quot; genome.fa</code> </p>
 <ol start="3">
@@ -14044,6 +13563,127 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <tag>Nanopore</tag>
       </tags>
   </entry>
+  <entry>
+    <title>用Hifiasm组装基因组：（三）Hifiasm软件组装基因组的多种模式</title>
+    <url>/2024/06/21/omics_genome.assembly_TGS_Hifiasm_03.modes/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=2153506013&auto=1&height=32"></iframe></div>
+
+
+<h1 id="1-Hifiasm组装基因组的模式"><a href="#1-Hifiasm组装基因组的模式" class="headerlink" title="1. Hifiasm组装基因组的模式"></a>1. Hifiasm组装基因组的模式</h1><p>根据可用的数据不同，Hifiasm组装基因组在HiFi数据基础上，还有几种模式可以增加组装的完整度和准确度。</p>
+<ol>
+<li>HiFi-only assembly 模式（只有HiFi数据）</li>
+<li>Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）</li>
+<li>Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）</li>
+<li>端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）</li>
+</ol>
+<h1 id="2-HiFi-only-assembly-模式（只有HiFi数据）"><a href="#2-HiFi-only-assembly-模式（只有HiFi数据）" class="headerlink" title="2. HiFi-only assembly 模式（只有HiFi数据）"></a>2. HiFi-only assembly 模式（只有HiFi数据）</h1><h2 id="2-1-经典模式"><a href="#2-1-经典模式" class="headerlink" title="2.1. 经典模式"></a>2.1. 经典模式</h2><ol>
+<li>命令</li>
+</ol>
+<ul>
+<li><code>nohup hifiasm -o sample_prefix -t 32 Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
+</ul>
+<ol start="2">
+<li>参数</li>
+</ol>
+<ul>
+<li>HiFi reads可以是fq或fa格式（fq的质量值会被忽略），可以是gz压缩格式。</li>
+<li>-o指定输出文件前缀；-t指定线程。</li>
+<li>用命令<code>2&gt;&amp;1 &gt;hifiasm.log</code>保存日志和报错内容到hifiasm.log文件。</li>
+</ul>
+<h2 id="2-2-两种组装方式"><a href="#2-2-两种组装方式" class="headerlink" title="2.2. 两种组装方式"></a>2.2. 两种组装方式</h2><ol>
+<li>单倍体分型组装（two partially phased assembly）</li>
+</ol>
+<ul>
+<li>默认是以此方式组装。</li>
+<li>单倍体分型组装生成一对文件（asm.bp.hap1.p_ctg.gfa和asm.bp.hap2.p_ctg.gfa），代表二倍体的两个单倍型。同时也会生成primary contigs文件asm.bp.p_ctg.gfa。</li>
+</ul>
+<ol start="2">
+<li>primary&#x2F;alternate组装</li>
+</ol>
+<ul>
+<li>加一个参数<code>--primary</code>则指定primary&#x2F;alternate组装方式。</li>
+<li>命令：<code>nohup hifiasm -o sample_prefix -t 32 --primary Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
+<li>分别生成primary contigs和alternate contigs文件asm.p_ctg.gfa和asm._ctg.gfa。</li>
+</ul>
+<h1 id="3-Trio-binning模式（HiFi数据-父母本二代Illumina测序数据）"><a href="#3-Trio-binning模式（HiFi数据-父母本二代Illumina测序数据）" class="headerlink" title="3. Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）"></a>3. Trio-binning模式（HiFi数据+父母本二代Illumina测序数据）</h1><ol>
+<li>介绍</li>
+</ol>
+<ul>
+<li>当父母本的二代Illumina reads可用时，也可以通过trio binning生成一对解析的单倍型的组装。</li>
+<li>Hifiasm中用到的trio binning 技术是指利用父本、母本和子代的遗传信息对子代的单倍型划分的方法。该方法的有效性随着杂合度的增加而提高，极大地提升了等位基因组的组装质量。</li>
+</ul>
+<ol start="2">
+<li>命令</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_"># </span><span class="language-bash">trio-binning模式需要额外安装yak，两种安装方式任选一种</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash"><span class="built_in">source</span> code</span></span><br><span class="line">git clone https://github.com/lh3/yak</span><br><span class="line">cd yak &amp;&amp; make</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">bioncda</span></span><br><span class="line">conda install -c bioconda yak</span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">运行组装</span></span><br><span class="line">yak count -b37 -t16 -o pat.yak &lt;(cat paternal_1.fq.gz paternal_2.fq.gz) &lt;(cat paternal_1.fq.gz paternal_2.fq.gz)</span><br><span class="line">yak count -b37 -t16 -o mat.yak &lt;(cat maternal_1.fq.gz maternal_2.fq.gz) &lt;(cat maternal_1.fq.gz maternal_2.fq.gz)</span><br><span class="line">hifiasm -o sample_prefix -t 32 -1 pat.yak -2 mat.yak Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>参数解释</li>
+</ol>
+<ul>
+<li>命令中Illumina双端测序的父本paternal数据和母本maternal数据同时使用</li>
+</ul>
+<h1 id="4-Hi-C-Integrated-assembly-模式（HiFi数据-Hi-C数据）"><a href="#4-Hi-C-Integrated-assembly-模式（HiFi数据-Hi-C数据）" class="headerlink" title="4. Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）"></a>4. Hi-C Integrated assembly 模式（HiFi数据+Hi-C数据）</h1><ol>
+<li>介绍</li>
+</ol>
+<ul>
+<li>当Hi-C数据可用时，可以生成一对解析的单倍型的组装。</li>
+<li>李恒团队2022年在Nature biotechnology上发表论文Haplotype-resolved assembly of diploid genomes without parental data（<a href="https://www.nature.com/articles/s41587-022-01261-x%EF%BC%89%EF%BC%8C%E5%9C%A8Hifiasm%E4%B8%AD%E5%BC%95%E5%85%A5%E4%BA%86Hi-C">https://www.nature.com/articles/s41587-022-01261-x），在Hifiasm中引入了Hi-C</a> Integrated assembly 模式。</li>
+<li>Hi-C Integrated assembly模式针对PacBio HiFi (High-Fidelity) 长读长测序技术和Hi-C (High-Throughput Chromatin Confirmation Capture) 测序技术进行了全新的设计。</li>
+<li>该算法结合了HiFi数据中精确的局部单倍型信息和Hi-C数据中的长距离互作用信息以达到全局定相 (phasing)，从而获得不依赖亲本信息的染色体级别的单倍型组装结果。为了进一步提高组装质量，作者充分利用了组装图中的结构信息，以及其前期研究中的Graph-binning等策略。</li>
+<li>这个模式组装后的基因组还未挂载在染色体上，仍然需要Juicer+3ddna+juicebox等软件进行染色体挂载。</li>
+<li>这个模式的数据最易获得，所以也很常用。</li>
+</ul>
+<ol start="2">
+<li>命令</li>
+</ol>
+<ul>
+<li><code>nohup hifiasm -o sample_prefix -t 32  --h1 HiC_1.fq.gz --h2 sample_HiC_2.fq.gz Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
+</ul>
+<ol start="3">
+<li>参数</li>
+</ol>
+<ul>
+<li>用–h1和–h2指定Hi-C数据。</li>
+</ul>
+<h1 id="5-端对端组装：HiFi-ONT模式（HiFi数据-ONT超长reads数据）"><a href="#5-端对端组装：HiFi-ONT模式（HiFi数据-ONT超长reads数据）" class="headerlink" title="5. 端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）"></a>5. 端对端组装：HiFi+ONT模式（HiFi数据+ONT超长reads数据）</h1><p>当ONT数据可用时，可以集成超长ONT数据生成端粒到端粒的组装</p>
+<ol>
+<li>命令</li>
+</ol>
+<ul>
+<li><code>nohup hifiasm -o sample_prefix -t 32 --ul ONT.fq.gz Hifi.fq.gz 2&gt;&amp;1 &gt; hifiasm.log &amp;</code></li>
+</ul>
+<ol start="2">
+<li>参数</li>
+</ol>
+<ul>
+<li>用–ul指定ONT数据。</li>
+</ul>
+<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
+<li>Hifiasm manual：<a href="https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/">https://hifiasm.readthedocs.io/_/downloads/en/latest/pdf/</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>omics</category>
+        <category>genome</category>
+        <category>genome assembly</category>
+      </categories>
+      <tags>
+        <tag>Hi-C</tag>
+        <tag>genome assembly</tag>
+        <tag>third generation sequencing</tag>
+        <tag>TGS</tag>
+        <tag>Hifiasm</tag>
+        <tag>HiFi</tag>
+        <tag>PacBio</tag>
+        <tag>ONT</tag>
+      </tags>
+  </entry>
   <entry>
     <title>用k-mer分析进行基因组调查：（一）基本原理</title>
     <url>/2022/05/25/omics_genome.survey_01.intro/</url>
@@ -14724,9 +14364,9 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>用k-mer分析进行基因组调查：（五）用GenomeScope评估基因组特征</title>
-    <url>/2022/06/05/omics_genome.survey_05.GenomeScope/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283098&auto=1&height=32"></iframe> </div>
+    <title>用k-mer分析进行基因组调查：（六）用GCE分步实现</title>
+    <url>/2022/06/07/omics_genome.survey_06.GCE/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283100&auto=1&height=32"></iframe></div>
 
 <p><strong>用k-mer分析进行基因组调查系列：</strong></p>
 <ul>
@@ -14739,163 +14379,22 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 <li>（七）用KmerGenie一步实现：<a href="https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/">https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/</a></li>
 </ul>
 <p><strong>【推荐】用Smudgeplot评估物种倍性后，用组合jellyfish+GenomeScope1.0做二倍体物种的基因组调查，用组合KMC+GenomeScope2.0做多倍体物种的基因组调查。</strong></p>
-<h1 id="1-k-mer进行基因组调查的软件概况"><a href="#1-k-mer进行基因组调查的软件概况" class="headerlink" title="1. k-mer进行基因组调查的软件概况"></a>1. k-mer进行基因组调查的软件概况</h1><p>k-mer进行基因组调查分为<strong>k-mer频数统计</strong>和<strong>基因组特征评估</strong>两步。</p>
+<h1 id="1-k-mer进行基因组调查的软件"><a href="#1-k-mer进行基因组调查的软件" class="headerlink" title="1. k-mer进行基因组调查的软件"></a>1. k-mer进行基因组调查的软件</h1><p>k-mer进行基因组调查分为<strong>k-mer频数统计</strong>和<strong>基因组特征评估</strong>两步。</p>
 <ul>
-<li>GenomeScope可以实现第二步基因组特征评估。</li>
-<li>需要在jellyfish&#x2F;KMC等软件的第一步结果k-mer频数分布表的基础上，GenomeScope才可实现。</li>
+<li>GCE可以分步实现两步。第一步k-mer频数统计和第二步基因组特征评估。</li>
+<li>GCE第一步的结果sample.histo可以用在GenomeScope和其他基因组特征评估的软件上，实现第二步。</li>
 </ul>
-<p>推荐第一步获取k-mer频数分布表的命令：</p>
-<ol>
-<li><p>jellyfish</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">jellyfish count -C -m 21 -s 1000000000 -t 10 *.fastq -o sample.jf #计算k-mer频率，生成sample.jf</span><br><span class="line">jellyfish histo -t 10 sample.jf &gt; sample.histo #生成k-mer频数直方表sample.histo和k-mer直方图</span><br></pre></td></tr></table></figure>
+<h1 id="2-GCE-简介"><a href="#2-GCE-简介" class="headerlink" title="2. GCE 简介"></a>2. GCE 简介</h1><p>GCE (genomic charactor estimator)是华大基因在2013年开发的一款基于贝叶斯模型的用于基因组调查的软件，在2020年发布了版本v2。</p>
+<h1 id="3-GCE-安装"><a href="#3-GCE-安装" class="headerlink" title="3. GCE 安装"></a>3. GCE 安装</h1><h2 id="3-1-GCE-下载"><a href="#3-1-GCE-下载" class="headerlink" title="3.1. GCE 下载"></a>3.1. GCE 下载</h2><ul>
+<li>GCE主要托管在BGI的ftp站点：<a href="ftp://ftp.genomics.org.cn/pub/gce%E3%80%82">ftp://ftp.genomics.org.cn/pub/gce。</a></li>
+<li>也可以在GCE github：<a href="https://github.com/fanagislab/GCE%E4%B8%8A%E6%89%BE%E5%88%B0%E3%80%82">https://github.com/fanagislab/GCE上找到。</a></li>
+</ul>
+<h2 id="3-2-GCE-安装"><a href="#3-2-GCE-安装" class="headerlink" title="3.2. GCE 安装"></a>3.2. GCE 安装</h2><ol>
+<li><p>已编译【推荐】</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone git@github.com:fanagislab/GCE.git</span><br></pre></td></tr></table></figure>
 </li>
-<li><p>KMC</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">mkdir tmp</span><br><span class="line">ls *.fastq.gz &gt; FILES</span><br><span class="line">kmc -k21 -t16 -m64 -ci1 -cs10000 @FILES kmcdb tmp #计算k-mer频率</span><br><span class="line">kmc_tools transform kmcdb histogram sample.histo -cx10000 #生成k-mer直方图</span><br></pre></td></tr></table></figure></li>
-</ol>
-<h1 id="2-GenomeScope概况"><a href="#2-GenomeScope概况" class="headerlink" title="2. GenomeScope概况"></a>2. GenomeScope概况</h1><ul>
-<li>GenomeScope可以利用第一步jellyfish或KMC等其他软件分析得到k-mer频数分布表(sample.histo文件)实现第二步<strong>基因组特征评估</strong>。</li>
-<li>GenomeScope1.0在2017年发表，用于二倍体物种的基因组调查；2020年又发表了GenomeScope 2.0版本，用于多倍体物种的基因组调查，并发布了用于判断物种倍性的Smudgeplot。</li>
-<li><strong>GenomeScope1.0</strong>的基因组特征结果包括基因组大小(genome size)，杂合度(heterozygosity)，重复序列比例，GC含量等。</li>
-<li><strong>GenomeScope2.0</strong>的基因组特征结果包括基因组大小(genome size)，杂合度(heterozygosity)，重复序列比例，GC含量，基因型比例，和基因组结构(同源&#x2F;异源多倍体)等。</li>
-<li>GenomeScope有网页版和Linux本地版，功能一样；推荐网页版，免去安装的麻烦。</li>
-</ul>
-<h1 id="3-GenomeScope1-0-网页版【推荐】——-适用于二倍体物种"><a href="#3-GenomeScope1-0-网页版【推荐】——-适用于二倍体物种" class="headerlink" title="3. GenomeScope1.0 网页版【推荐】—— 适用于二倍体物种"></a>3. GenomeScope1.0 网页版【推荐】—— 适用于二倍体物种</h1><p><a href="http://qb.cshl.edu/genomescope">GenomeScope1.0 网页版</a>：<a href="http://qb.cshl.edu/genomescope">http://qb.cshl.edu/genomescope</a></p>
-<ol>
-<li><p>上传第一步获得的k-mer频数分布表sample.histo文件；</p>
-</li>
-<li><p>设置参数Kmer length为第一步选择的k-mer长度值，这里是17；</p>
-</li>
-<li><p>参数Read length为序列读长，一般为150；</p>
-</li>
-<li><p>参数Max kmer coverage默认是1000。</p>
-<p> 建议按照物种情况修改，比如10000，以统计更准确。</p>
-<p> 这个参数太小，可能造成过滤过多的Kmer，导致估计的基因组大小偏小的情况。</p>
-<p> 这个参数太大则可能把高拷贝数量的DNA，比如叶绿体DNA，包括进Kmer的统计，造成GenomeScope算法的误差，所以还是不推荐使用-1或太大的值。</p>
-</li>
-<li><p>提交后几分钟就可以得到结果，保存结果图片可用于发表。</p>
-</li>
-</ol>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/omics_genome.survey_GenomeScope1.0.png?raw=true" width=70% title="GenomeScope1.0结果示例" align=center/>
-
-<p><strong><p align="center">Figure 1. GenomeScope1.0结果示例</p></strong></p>
-<h1 id="4-GenomeScope2-0-网页版-【推荐】-——-适用于多倍体物种"><a href="#4-GenomeScope2-0-网页版-【推荐】-——-适用于多倍体物种" class="headerlink" title="4. GenomeScope2.0 网页版 【推荐】 —— 适用于多倍体物种"></a>4. GenomeScope2.0 网页版 【推荐】 —— 适用于多倍体物种</h1><p><a href="http://qb.cshl.edu/genomescope/genomescope2.0">GenomeScope2.0 网页版</a>：<a href="http://qb.cshl.edu/genomescope/genomescope2.0">http://qb.cshl.edu/genomescope/genomescope2.0</a></p>
-<p>GenomeScope2.0版本相较于1.0，进行了许多改进，主要是增加了多倍体物种的基因组调查，并提出Smudgeplot方法来估计基因组的倍性和基因组结构。</p>
-<h2 id="4-1-GenomeScope-2-0-使用步骤"><a href="#4-1-GenomeScope-2-0-使用步骤" class="headerlink" title="4.1. GenomeScope 2.0 使用步骤"></a>4.1. GenomeScope 2.0 使用步骤</h2><ol>
-<li>上传第一步获得的k-mer频数分布表histo文件；</li>
-<li>设置参数Kmer length为第一步选择的k-mer长度值，这里是17；</li>
-<li>参数倍性Ploidy根据物种的倍性设定，默认是二倍体，设置成2；</li>
-<li>参数Max k-mer coverage默认是-1，即不限制最大k-mer深度。建议按照物种情况修改，比如10000，以统计更准确。这个参数太小，可能造成过滤过多的Kmer，导致估计的基因组大小偏小的情况。这个参数太大则可能把高拷贝数量的DNA，比如叶绿体DNA，包括进Kmer的统计，造成GenomeScope算法的误差，所以还是不推荐使用-1或太大的值。</li>
-<li>参数Average k-mer coverage for polyploid genome默认是-1，即不进行筛选，可以根据情况调整。</li>
-<li>提交后几分钟就可以得到结果，保存结果图片可用于发表。</li>
-</ol>
-<h2 id="4-2-GenomeScope-2-0-结果"><a href="#4-2-GenomeScope-2-0-结果" class="headerlink" title="4.2. GenomeScope 2.0 结果"></a>4.2. GenomeScope 2.0 结果</h2><h3 id="4-2-1-二倍体结果"><a href="#4-2-1-二倍体结果" class="headerlink" title="4.2.1. 二倍体结果"></a>4.2.1. <strong>二倍体</strong>结果</h3><p>二倍体的GenomeScope 2.0 结果与GenomeScope 1.0 结果的主要不同之处在于杂合度结果(het)变成了2.0版本的代表基因型的aa和ab的比例，其中杂合基因型ab的比例即为杂合度。2.0结果中的p值代表设置的物种倍性。</p>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/omics_genome.survey_GenomeScope2.0.png?raw=true" width=70% title="GenomeScope2.0二倍体结果示例" align=center/>
-
-<p><strong><p align="center">Figure 2. GenomeScope2.0 二倍体结果示例</p></strong></p>
-<h3 id="4-2-2-区分异源四倍体和同源四倍体"><a href="#4-2-2-区分异源四倍体和同源四倍体" class="headerlink" title="4.2.2. 区分异源四倍体和同源四倍体"></a>4.2.2. 区分<strong>异源四倍体</strong>和<strong>同源四倍体</strong></h3><p>GenomeScope2.0添加了参数倍性<strong>Ploidy</strong>，可以评估多倍体的基因组特征。</p>
-<ol>
-<li>四倍体共有两种可能的拓扑结构，代表着同源四倍体和异源四倍体，每种拓扑包含三种杂合基因型和一种纯合基因型，共有五种基因型。(五倍体有五种可能的拓扑，六倍体有十六种)</li>
-<li>根据结果中杂合基因型的分布模式可以区分异源四倍体和同源四倍体。</li>
-</ol>
-<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-14998-3/MediaObjects/41467_2020_14998_Fig2_HTML.png?as=webp" width=80% title="四倍体的两种拓扑结构可能性" align=center/>
-
-<p><strong><p align="center">Figure 3. 四倍体的两种拓扑结构可能性 a异源四倍体，b同源四倍体。 图片来源：<a href="https://www.nature.com/articles/s41467-020-14998-3">GenomeScope 2.0 paper</a></p></strong></p>
-<ol start="3">
-<li>GenomeScope2.0的<strong>四倍体</strong>结果</li>
-</ol>
-<p>在GenomeScope 2.0 的结果中，如果杂合基因型aaab的比例大于aabb，则认为该物种是异源四倍体；如果杂合基因型aaab的比例小于aabb，则认为该物种是同源四倍体。</p>
-<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-14998-3/MediaObjects/41467_2020_14998_Fig6_HTML.png?as=webp" width=100% title="GenomeScope2.0多倍体结果示例" align=center/>
-
-<p><strong><p align="center">Figure 4. GenomeScope2.0 多倍体结果示例 a异源四倍体，b同源四倍体。 图片来源：<a href="https://www.nature.com/articles/s41467-020-14998-3">GenomeScope 2.0 paper</a></p></strong></p>
-<h1 id="5-GenomeScope1-0本地版-——-适用于二倍体物种"><a href="#5-GenomeScope1-0本地版-——-适用于二倍体物种" class="headerlink" title="5. GenomeScope1.0本地版 —— 适用于二倍体物种"></a>5. GenomeScope1.0本地版 —— 适用于二倍体物种</h1><p>GenomeScope1.0的本地版是用一个R脚本实现的，在<a href="https://github.com/schatzlab/genomescope">GenomeScope github</a>可以下载genomescope.R脚本，下载后把genomescope.R文件加入环境变量即可使用。。</p>
-<p><code>Rscript genomescope.R sample.histo k-mer_length read_length output_dir [kmer_max] [verbose]</code></p>
-<p>必需参数：</p>
-<ul>
-<li>sample.histo：频数分布直方表，jellyfish的结果。</li>
-<li>k-mer_length：k-mer长度，通常是17，21，与jellyfish一致。</li>
-<li>read_length：reads长度，这里是150bp的PE reads，所以是150。</li>
-<li>output_dir：输出目录，结果图和文本都输出到这个目录。</li>
-</ul>
-<h1 id="6-GenomeScope2-0本地版-——-适用于多倍体物种"><a href="#6-GenomeScope2-0本地版-——-适用于多倍体物种" class="headerlink" title="6. GenomeScope2.0本地版 —— 适用于多倍体物种"></a>6. GenomeScope2.0本地版 —— 适用于多倍体物种</h1><h2 id="6-1-下载和安装"><a href="#6-1-下载和安装" class="headerlink" title="6.1. 下载和安装"></a>6.1. 下载和安装</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/tbenavi1/genomescope2.0.git #下载</span><br><span class="line">cd genomescope2.0/</span><br><span class="line">mkdir ~/R_libs #创建主目录下的R_libs文件夹用于安装本地R库</span><br><span class="line">echo &quot;R_LIBS=~/R_libs/&quot; &gt;&gt; ~/.Renviron #创建/编辑.Renviron文件，使得R在创建的R_libs文件夹加载库</span><br><span class="line">Rscript install.R #安装</span><br></pre></td></tr></table></figure>
-
-<p>安装后把目录下的genomescope.R文件加入环境变量即可使用。</p>
-<h2 id="6-2-使用"><a href="#6-2-使用" class="headerlink" title="6.2. 使用"></a>6.2. 使用</h2><p><code>genomescope.R -i histogram_file -o output_dir -k k-mer_length</code></p>
-<p>参数：</p>
-<ul>
-<li>-i histogram_file：频数分布直方表，jellyfish或KMC的结果。</li>
-<li>-k k-mer_length：k-mer长度，通常是17，21，与jellyfish&#x2F;KMC的设置一致。</li>
-<li>-o output_dir：输出目录，结果图和文本都输出到这个目录。</li>
-<li>-p ploidy：设置倍性。</li>
-<li>-l lambda：设置测序的平均k-mer覆盖率的初始猜测。</li>
-<li>-n name_prefix：设置输出文件的前缀。</li>
-<li>-m max_kmercov：设置从分析中排除高频k-mers的截止值，根据物种情况确定，推荐1000或10000。</li>
-</ul>
-<h1 id="7-GenomeScope实践经验"><a href="#7-GenomeScope实践经验" class="headerlink" title="7. GenomeScope实践经验"></a>7. GenomeScope实践经验</h1><ol>
-<li>实际使用中发现，GenomeScope1.0和2.0常常估算差异较大。建议二倍体还是使用GenomeScope1.0。</li>
-</ol>
-<ul>
-<li>在估算一个约300Mb的二倍体基因组时，GenomeScope1.0估算出来267Mb，GenomeScope2.0估算出来149Mb。</li>
-<li>在估算一个约6Gb的四倍体基因组时发现，GenomeScope1.0估算出来5.5Gb，GenomeScope2.0估算出来2.7Gb。</li>
-</ul>
-<h1 id="8-references"><a href="#8-references" class="headerlink" title="8. references"></a>8. references</h1><ol>
-<li>GenomeScope 1.0 github：<a href="https://github.com/schatzlab/genomescope">https://github.com/schatzlab/genomescope</a></li>
-<li>GenomeScope 2.0 github：<a href="https://github.com/tbenavi1/genomescope2.0">https://github.com/tbenavi1/genomescope2.0</a></li>
-<li>GenomeScope 1.0 paper：<a href="https://academic.oup.com/bioinformatics/article/33/14/2202/3089939">https://academic.oup.com/bioinformatics/article/33/14/2202/3089939</a></li>
-<li>GenomeScope 2.0 + Smudgeplot paper：<a href="https://www.nature.com/articles/s41467-020-14998-3">https://www.nature.com/articles/s41467-020-14998-3</a></li>
-<li>Smudgeplot github：<a href="https://github.com/KamilSJaron/smudgeplot">https://github.com/KamilSJaron/smudgeplot</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>omics</category>
-        <category>genome</category>
-        <category>genome survey</category>
-      </categories>
-      <tags>
-        <tag>genome survey</tag>
-        <tag>genome</tag>
-        <tag>k-mer</tag>
-        <tag>KMC</tag>
-        <tag>GenomeScope</tag>
-        <tag>Smudgeplot</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用k-mer分析进行基因组调查：（六）用GCE分步实现</title>
-    <url>/2022/06/07/omics_genome.survey_06.GCE/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283100&auto=1&height=32"></iframe></div>
-
-<p><strong>用k-mer分析进行基因组调查系列：</strong></p>
-<ul>
-<li>（一）基本原理：<a href="https://yanzhongsino.github.io/2022/05/25/omics_genome.survey_01.intro/">https://yanzhongsino.github.io/2022/05/25/omics_genome.survey_01.intro/</a></li>
-<li>（二）用Smudgeplot估计倍性：<a href="https://yanzhongsino.github.io/2022/12/31/omics_genome.survey_02.Smudgeplot/">https://yanzhongsino.github.io/2022/12/31/omics_genome.survey_02.Smudgeplot/</a></li>
-<li>（三）用jellyfish进行k-mer频数统计：<a href="https://yanzhongsino.github.io/2022/05/27/omics_genome.survey_03.jellyfish/">https://yanzhongsino.github.io/2022/05/27/omics_genome.survey_03.jellyfish/</a></li>
-<li>（四）用KMC进行k-mer频数统计：<a href="https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_04.KMC/">https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_04.KMC/</a></li>
-<li>（五）用GenomeScope评估基因组特征：<a href="https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_05.GenomeScope/">https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_05.GenomeScope/</a></li>
-<li>（六）用GCE分步实现：<a href="https://yanzhongsino.github.io/2022/06/07/omics_genome.survey_06.GCE/">https://yanzhongsino.github.io/2022/06/07/omics_genome.survey_06.GCE/</a></li>
-<li>（七）用KmerGenie一步实现：<a href="https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/">https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/</a></li>
-</ul>
-<p><strong>【推荐】用Smudgeplot评估物种倍性后，用组合jellyfish+GenomeScope1.0做二倍体物种的基因组调查，用组合KMC+GenomeScope2.0做多倍体物种的基因组调查。</strong></p>
-<h1 id="1-k-mer进行基因组调查的软件"><a href="#1-k-mer进行基因组调查的软件" class="headerlink" title="1. k-mer进行基因组调查的软件"></a>1. k-mer进行基因组调查的软件</h1><p>k-mer进行基因组调查分为<strong>k-mer频数统计</strong>和<strong>基因组特征评估</strong>两步。</p>
-<ul>
-<li>GCE可以分步实现两步。第一步k-mer频数统计和第二步基因组特征评估。</li>
-<li>GCE第一步的结果sample.histo可以用在GenomeScope和其他基因组特征评估的软件上，实现第二步。</li>
-</ul>
-<h1 id="2-GCE-简介"><a href="#2-GCE-简介" class="headerlink" title="2. GCE 简介"></a>2. GCE 简介</h1><p>GCE (genomic charactor estimator)是华大基因在2013年开发的一款基于贝叶斯模型的用于基因组调查的软件，在2020年发布了版本v2。</p>
-<h1 id="3-GCE-安装"><a href="#3-GCE-安装" class="headerlink" title="3. GCE 安装"></a>3. GCE 安装</h1><h2 id="3-1-GCE-下载"><a href="#3-1-GCE-下载" class="headerlink" title="3.1. GCE 下载"></a>3.1. GCE 下载</h2><ul>
-<li>GCE主要托管在BGI的ftp站点：<a href="ftp://ftp.genomics.org.cn/pub/gce%E3%80%82">ftp://ftp.genomics.org.cn/pub/gce。</a></li>
-<li>也可以在GCE github：<a href="https://github.com/fanagislab/GCE%E4%B8%8A%E6%89%BE%E5%88%B0%E3%80%82">https://github.com/fanagislab/GCE上找到。</a></li>
-</ul>
-<h2 id="3-2-GCE-安装"><a href="#3-2-GCE-安装" class="headerlink" title="3.2. GCE 安装"></a>3.2. GCE 安装</h2><ol>
-<li><p>已编译【推荐】</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">git clone git@github.com:fanagislab/GCE.git</span><br></pre></td></tr></table></figure>
-</li>
-<li><p>未编译</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">wget ftp://ftp.genomics.org.cn/pub/gce/gce-1.0.2.tar.gz</span><br><span class="line">tar -xzvf gce-1.0.2.tar.gz #解压缩和解包</span><br><span class="line">make #编译</span><br></pre></td></tr></table></figure></li>
+<li><p>未编译</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">wget ftp://ftp.genomics.org.cn/pub/gce/gce-1.0.2.tar.gz</span><br><span class="line">tar -xzvf gce-1.0.2.tar.gz #解压缩和解包</span><br><span class="line">make #编译</span><br></pre></td></tr></table></figure></li>
 </ol>
 <p>文件夹下有<code>kmerfreq</code>和<code>gce</code>两个命令。</p>
 <p>老版本是<code>kmer_freq_hash</code>代替<code>kmerfreq</code>命令。</p>
@@ -15616,8 +15115,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <category>quality assessment</category>
       </categories>
       <tags>
-        <tag>organelle</tag>
         <tag>genome</tag>
+        <tag>organelle</tag>
         <tag>quality assessment</tag>
         <tag>transcriptome</tag>
       </tags>
@@ -15928,8 +15427,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </categories>
       <tags>
         <tag>mitogenome</tag>
-        <tag>organelle</tag>
         <tag>genome</tag>
+        <tag>organelle</tag>
         <tag>samtools</tag>
         <tag>quality assessment</tag>
         <tag>mapping</tag>
@@ -16031,8 +15530,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </categories>
       <tags>
         <tag>mitogenome</tag>
-        <tag>organelle</tag>
         <tag>genome</tag>
+        <tag>organelle</tag>
         <tag>samtools</tag>
         <tag>quality assessment</tag>
         <tag>mapping</tag>
@@ -16226,8 +15725,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </categories>
       <tags>
         <tag>mitogenome</tag>
-        <tag>organelle</tag>
         <tag>genome</tag>
+        <tag>organelle</tag>
         <tag>quality assessment</tag>
         <tag>transcriptome</tag>
         <tag>mapping</tag>
@@ -16240,75 +15739,125 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>基因组质量评估：（五）mapping法：7. 用软件mosdepth统计BAM文件的深度</title>
-    <url>/2022/07/31/omics_genome_quality.assessment_mapping_mosdepth/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1514742&auto=1&height=32"></iframe></div>
-
-<h1 id="1-mosdepth简介"><a href="#1-mosdepth简介" class="headerlink" title="1. mosdepth简介"></a>1. mosdepth简介</h1><p>mosdepth（<a href="https://github.com/brentp/mosdepth%EF%BC%89%E6%98%AF%E7%94%A8%E4%BA%8EWGS%EF%BC%8Cexome%EF%BC%8Ctargeted">https://github.com/brentp/mosdepth）是用于WGS，exome，targeted</a> sequencing的BAM&#x2F;CRAM文件的测序深度计算的软件，主要是用Nim语言写的（第一次听说这种语言）。</p>
-<h1 id="2-mosdepth可以得到的数据包括"><a href="#2-mosdepth可以得到的数据包括" class="headerlink" title="2. mosdepth可以得到的数据包括"></a>2. mosdepth可以得到的数据包括</h1><ol>
-<li>每个碱基深度的计算速度是samtools depth的约2倍。——对于 30X 基因组，大约需要 25 分钟的 CPU 时间。</li>
-<li>给定窗口大小的平均每个窗口深度，可用于 CNV calling。</li>
-<li>给定区域的 BED 文件的每个区域的平均值。</li>
-<li>给定窗口大小的每个区域累积覆盖率（cumulative coverage）直方图的平均值或中值。</li>
-<li>对于每个染色体和全基因组，在给定阈值或以上覆盖的碱基比例分布。</li>
-<li>合并相邻碱基的量化输出，只要它们落在相同的覆盖范围内，例如（10-20）。</li>
-<li>阈值输出以指示在给定阈值下每个区域中有多少个碱基被覆盖。</li>
-<li>每条染色体和每条染色体指定区域内的平均深度的总结。</li>
-<li>一个d4文件（比bigwig好）。</li>
-</ol>
-<h1 id="3-下载安装"><a href="#3-下载安装" class="headerlink" title="3. 下载安装"></a>3. 下载安装</h1><p>直接下载已编译文件，修改权限即可使用。</p>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">wget https://github.com/brentp/mosdepth/releases/download/v0.3.3/mosdepth</span><br><span class="line">chmod +x mosdepth</span><br><span class="line">./mosdepth -h</span><br></pre></td></tr></table></figure>
+    <title>用k-mer分析进行基因组调查：（五）用GenomeScope评估基因组特征</title>
+    <url>/2022/06/05/omics_genome.survey_05.GenomeScope/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=283098&auto=1&height=32"></iframe> </div>
 
-<h1 id="4-使用"><a href="#4-使用" class="headerlink" title="4. 使用"></a>4. 使用</h1><ol>
-<li>准备</li>
-</ol>
+<p><strong>用k-mer分析进行基因组调查系列：</strong></p>
 <ul>
-<li><code>samtools index sample.bam</code> # 生成bam文件的索引文件sample.bam.bai</li>
+<li>（一）基本原理：<a href="https://yanzhongsino.github.io/2022/05/25/omics_genome.survey_01.intro/">https://yanzhongsino.github.io/2022/05/25/omics_genome.survey_01.intro/</a></li>
+<li>（二）用Smudgeplot估计倍性：<a href="https://yanzhongsino.github.io/2022/12/31/omics_genome.survey_02.Smudgeplot/">https://yanzhongsino.github.io/2022/12/31/omics_genome.survey_02.Smudgeplot/</a></li>
+<li>（三）用jellyfish进行k-mer频数统计：<a href="https://yanzhongsino.github.io/2022/05/27/omics_genome.survey_03.jellyfish/">https://yanzhongsino.github.io/2022/05/27/omics_genome.survey_03.jellyfish/</a></li>
+<li>（四）用KMC进行k-mer频数统计：<a href="https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_04.KMC/">https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_04.KMC/</a></li>
+<li>（五）用GenomeScope评估基因组特征：<a href="https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_05.GenomeScope/">https://yanzhongsino.github.io/2022/06/05/omics_genome.survey_05.GenomeScope/</a></li>
+<li>（六）用GCE分步实现：<a href="https://yanzhongsino.github.io/2022/06/07/omics_genome.survey_06.GCE/">https://yanzhongsino.github.io/2022/06/07/omics_genome.survey_06.GCE/</a></li>
+<li>（七）用KmerGenie一步实现：<a href="https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/">https://yanzhongsino.github.io/2022/06/19/omics_genome.survey_07.KmerGenie/</a></li>
 </ul>
-<ol start="2">
-<li>计算深度</li>
-</ol>
+<p><strong>【推荐】用Smudgeplot评估物种倍性后，用组合jellyfish+GenomeScope1.0做二倍体物种的基因组调查，用组合KMC+GenomeScope2.0做多倍体物种的基因组调查。</strong></p>
+<h1 id="1-k-mer进行基因组调查的软件概况"><a href="#1-k-mer进行基因组调查的软件概况" class="headerlink" title="1. k-mer进行基因组调查的软件概况"></a>1. k-mer进行基因组调查的软件概况</h1><p>k-mer进行基因组调查分为<strong>k-mer频数统计</strong>和<strong>基因组特征评估</strong>两步。</p>
 <ul>
-<li><code>mosdepth -t 4 out sample.bam</code></li>
+<li>GenomeScope可以实现第二步基因组特征评估。</li>
+<li>需要在jellyfish&#x2F;KMC等软件的第一步结果k-mer频数分布表的基础上，GenomeScope才可实现。</li>
 </ul>
-<ol start="3">
-<li>参数</li>
+<p>推荐第一步获取k-mer频数分布表的命令：</p>
+<ol>
+<li><p>jellyfish</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">jellyfish count -C -m 21 -s 1000000000 -t 10 *.fastq -o sample.jf #计算k-mer频率，生成sample.jf</span><br><span class="line">jellyfish histo -t 10 sample.jf &gt; sample.histo #生成k-mer频数直方表sample.histo和k-mer直方图</span><br></pre></td></tr></table></figure>
+</li>
+<li><p>KMC</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">mkdir tmp</span><br><span class="line">ls *.fastq.gz &gt; FILES</span><br><span class="line">kmc -k21 -t16 -m64 -ci1 -cs10000 @FILES kmcdb tmp #计算k-mer频率</span><br><span class="line">kmc_tools transform kmcdb histogram sample.histo -cx10000 #生成k-mer直方图</span><br></pre></td></tr></table></figure></li>
 </ol>
-<ul>
-<li>-t 4：线程，需要&lt;&#x3D;4</li>
-<li>out：输出文件的前缀</li>
-<li>sample.bam：待分析的bam文件</li>
-<li>–by sample.bed：指定区域的bed文件，我分析整个基因组，没加这个参数。</li>
+<h1 id="2-GenomeScope概况"><a href="#2-GenomeScope概况" class="headerlink" title="2. GenomeScope概况"></a>2. GenomeScope概况</h1><ul>
+<li>GenomeScope可以利用第一步jellyfish或KMC等其他软件分析得到k-mer频数分布表(sample.histo文件)实现第二步<strong>基因组特征评估</strong>。</li>
+<li>GenomeScope1.0在2017年发表，用于二倍体物种的基因组调查；2020年又发表了GenomeScope 2.0版本，用于多倍体物种的基因组调查，并发布了用于判断物种倍性的Smudgeplot。</li>
+<li><strong>GenomeScope1.0</strong>的基因组特征结果包括基因组大小(genome size)，杂合度(heterozygosity)，重复序列比例，GC含量等。</li>
+<li><strong>GenomeScope2.0</strong>的基因组特征结果包括基因组大小(genome size)，杂合度(heterozygosity)，重复序列比例，GC含量，基因型比例，和基因组结构(同源&#x2F;异源多倍体)等。</li>
+<li>GenomeScope有网页版和Linux本地版，功能一样；推荐网页版，免去安装的麻烦。</li>
 </ul>
-<p>还有许多参数等着探索…</p>
-<h1 id="5-结果"><a href="#5-结果" class="headerlink" title="5. 结果"></a>5. 结果</h1><ol>
-<li>out.mosdepth.summary.txt</li>
+<h1 id="3-GenomeScope1-0-网页版【推荐】——-适用于二倍体物种"><a href="#3-GenomeScope1-0-网页版【推荐】——-适用于二倍体物种" class="headerlink" title="3. GenomeScope1.0 网页版【推荐】—— 适用于二倍体物种"></a>3. GenomeScope1.0 网页版【推荐】—— 适用于二倍体物种</h1><p><a href="http://qb.cshl.edu/genomescope">GenomeScope1.0 网页版</a>：<a href="http://qb.cshl.edu/genomescope">http://qb.cshl.edu/genomescope</a></p>
+<ol>
+<li><p>上传第一步获得的k-mer频数分布表sample.histo文件；</p>
+</li>
+<li><p>设置参数Kmer length为第一步选择的k-mer长度值，这里是17；</p>
+</li>
+<li><p>参数Read length为序列读长，一般为150；</p>
+</li>
+<li><p>参数Max kmer coverage默认是1000。</p>
+<p> 建议按照物种情况修改，比如10000，以统计更准确。</p>
+<p> 这个参数太小，可能造成过滤过多的Kmer，导致估计的基因组大小偏小的情况。</p>
+<p> 这个参数太大则可能把高拷贝数量的DNA，比如叶绿体DNA，包括进Kmer的统计，造成GenomeScope算法的误差，所以还是不推荐使用-1或太大的值。</p>
+</li>
+<li><p>提交后几分钟就可以得到结果，保存结果图片可用于发表。</p>
+</li>
 </ol>
-<ul>
-<li>文件包含每条染色体和整个基因组的信息，长度，mapped 碱基数量，平均深度，最小深度和最大深度。示例：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">chrom	length	bases	mean	min	max</span><br><span class="line">MCscaf001	12541575	440858440	35.15	0	3561</span><br><span class="line">MCscaf002	20211832	749371193	37.08	0	8181</span><br><span class="line">... ...</span><br><span class="line">MCscaf265	25000	897138	35.89	0	84</span><br><span class="line">MCscaf266	25000	913514	36.54	0	82</span><br><span class="line">total	256218469	10404013912	40.61	0	92318</span><br></pre></td></tr></table></figure>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/omics_genome.survey_GenomeScope1.0.png?raw=true" width=70% title="GenomeScope1.0结果示例" align=center/>
 
-<ol start="2">
-<li>out.mosdepth.global.dist.txt</li>
+<p><strong><p align="center">Figure 1. GenomeScope1.0结果示例</p></strong></p>
+<h1 id="4-GenomeScope2-0-网页版-【推荐】-——-适用于多倍体物种"><a href="#4-GenomeScope2-0-网页版-【推荐】-——-适用于多倍体物种" class="headerlink" title="4. GenomeScope2.0 网页版 【推荐】 —— 适用于多倍体物种"></a>4. GenomeScope2.0 网页版 【推荐】 —— 适用于多倍体物种</h1><p><a href="http://qb.cshl.edu/genomescope/genomescope2.0">GenomeScope2.0 网页版</a>：<a href="http://qb.cshl.edu/genomescope/genomescope2.0">http://qb.cshl.edu/genomescope/genomescope2.0</a></p>
+<p>GenomeScope2.0版本相较于1.0，进行了许多改进，主要是增加了多倍体物种的基因组调查，并提出Smudgeplot方法来估计基因组的倍性和基因组结构。</p>
+<h2 id="4-1-GenomeScope-2-0-使用步骤"><a href="#4-1-GenomeScope-2-0-使用步骤" class="headerlink" title="4.1. GenomeScope 2.0 使用步骤"></a>4.1. GenomeScope 2.0 使用步骤</h2><ol>
+<li>上传第一步获得的k-mer频数分布表histo文件；</li>
+<li>设置参数Kmer length为第一步选择的k-mer长度值，这里是17；</li>
+<li>参数倍性Ploidy根据物种的倍性设定，默认是二倍体，设置成2；</li>
+<li>参数Max k-mer coverage默认是-1，即不限制最大k-mer深度。建议按照物种情况修改，比如10000，以统计更准确。这个参数太小，可能造成过滤过多的Kmer，导致估计的基因组大小偏小的情况。这个参数太大则可能把高拷贝数量的DNA，比如叶绿体DNA，包括进Kmer的统计，造成GenomeScope算法的误差，所以还是不推荐使用-1或太大的值。</li>
+<li>参数Average k-mer coverage for polyploid genome默认是-1，即不进行筛选，可以根据情况调整。</li>
+<li>提交后几分钟就可以得到结果，保存结果图片可用于发表。</li>
 </ol>
-<ul>
-<li>文件包含累积分布，指示给定覆盖率阈值下覆盖的总碱基的比例。包含三列：染色体&#x2F;total，覆盖水平，该级别覆盖的碱基比例。示例：</li>
-</ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">MCscaf001	1961	0.00</span><br><span class="line">MCscaf001	1960	0.00</span><br><span class="line">MCscaf001	1957	0.00</span><br><span class="line">... ...</span><br><span class="line">total	2	0.99</span><br><span class="line">total	1	1.00</span><br><span class="line">total	0	1.00</span><br></pre></td></tr></table></figure>
+<h2 id="4-2-GenomeScope-2-0-结果"><a href="#4-2-GenomeScope-2-0-结果" class="headerlink" title="4.2. GenomeScope 2.0 结果"></a>4.2. GenomeScope 2.0 结果</h2><h3 id="4-2-1-二倍体结果"><a href="#4-2-1-二倍体结果" class="headerlink" title="4.2.1. 二倍体结果"></a>4.2.1. <strong>二倍体</strong>结果</h3><p>二倍体的GenomeScope 2.0 结果与GenomeScope 1.0 结果的主要不同之处在于杂合度结果(het)变成了2.0版本的代表基因型的aa和ab的比例，其中杂合基因型ab的比例即为杂合度。2.0结果中的p值代表设置的物种倍性。</p>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/images/omics_genome.survey_GenomeScope2.0.png?raw=true" width=70% title="GenomeScope2.0二倍体结果示例" align=center/>
 
-<p>还可以用脚本<code>python scripts/plot-dist.py \*global.dist.txt</code>画图，输出<code>dist.html</code>，可以看出整个基因组的覆盖度的分布。</p>
-<ol start="3">
-<li>out.per-base.bed.gz</li>
-</ol>
-<ul>
-<li>每个碱基的输出数据。</li>
+<p><strong><p align="center">Figure 2. GenomeScope2.0 二倍体结果示例</p></strong></p>
+<h3 id="4-2-2-区分异源四倍体和同源四倍体"><a href="#4-2-2-区分异源四倍体和同源四倍体" class="headerlink" title="4.2.2. 区分异源四倍体和同源四倍体"></a>4.2.2. 区分<strong>异源四倍体</strong>和<strong>同源四倍体</strong></h3><p>GenomeScope2.0添加了参数倍性<strong>Ploidy</strong>，可以评估多倍体的基因组特征。</p>
+<ol>
+<li>四倍体共有两种可能的拓扑结构，代表着同源四倍体和异源四倍体，每种拓扑包含三种杂合基因型和一种纯合基因型，共有五种基因型。(五倍体有五种可能的拓扑，六倍体有十六种)</li>
+<li>根据结果中杂合基因型的分布模式可以区分异源四倍体和同源四倍体。</li>
+</ol>
+<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-14998-3/MediaObjects/41467_2020_14998_Fig2_HTML.png?as=webp" width=80% title="四倍体的两种拓扑结构可能性" align=center/>
+
+<p><strong><p align="center">Figure 3. 四倍体的两种拓扑结构可能性 a异源四倍体，b同源四倍体。 图片来源：<a href="https://www.nature.com/articles/s41467-020-14998-3">GenomeScope 2.0 paper</a></p></strong></p>
+<ol start="3">
+<li>GenomeScope2.0的<strong>四倍体</strong>结果</li>
+</ol>
+<p>在GenomeScope 2.0 的结果中，如果杂合基因型aaab的比例大于aabb，则认为该物种是异源四倍体；如果杂合基因型aaab的比例小于aabb，则认为该物种是同源四倍体。</p>
+<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-14998-3/MediaObjects/41467_2020_14998_Fig6_HTML.png?as=webp" width=100% title="GenomeScope2.0多倍体结果示例" align=center/>
+
+<p><strong><p align="center">Figure 4. GenomeScope2.0 多倍体结果示例 a异源四倍体，b同源四倍体。 图片来源：<a href="https://www.nature.com/articles/s41467-020-14998-3">GenomeScope 2.0 paper</a></p></strong></p>
+<h1 id="5-GenomeScope1-0本地版-——-适用于二倍体物种"><a href="#5-GenomeScope1-0本地版-——-适用于二倍体物种" class="headerlink" title="5. GenomeScope1.0本地版 —— 适用于二倍体物种"></a>5. GenomeScope1.0本地版 —— 适用于二倍体物种</h1><p>GenomeScope1.0的本地版是用一个R脚本实现的，在<a href="https://github.com/schatzlab/genomescope">GenomeScope github</a>可以下载genomescope.R脚本，下载后把genomescope.R文件加入环境变量即可使用。。</p>
+<p><code>Rscript genomescope.R sample.histo k-mer_length read_length output_dir [kmer_max] [verbose]</code></p>
+<p>必需参数：</p>
+<ul>
+<li>sample.histo：频数分布直方表，jellyfish的结果。</li>
+<li>k-mer_length：k-mer长度，通常是17，21，与jellyfish一致。</li>
+<li>read_length：reads长度，这里是150bp的PE reads，所以是150。</li>
+<li>output_dir：输出目录，结果图和文本都输出到这个目录。</li>
 </ul>
-<ol start="4">
-<li>out.per-base.bed.gz.csi</li>
+<h1 id="6-GenomeScope2-0本地版-——-适用于多倍体物种"><a href="#6-GenomeScope2-0本地版-——-适用于多倍体物种" class="headerlink" title="6. GenomeScope2.0本地版 —— 适用于多倍体物种"></a>6. GenomeScope2.0本地版 —— 适用于多倍体物种</h1><h2 id="6-1-下载和安装"><a href="#6-1-下载和安装" class="headerlink" title="6.1. 下载和安装"></a>6.1. 下载和安装</h2><figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/tbenavi1/genomescope2.0.git #下载</span><br><span class="line">cd genomescope2.0/</span><br><span class="line">mkdir ~/R_libs #创建主目录下的R_libs文件夹用于安装本地R库</span><br><span class="line">echo &quot;R_LIBS=~/R_libs/&quot; &gt;&gt; ~/.Renviron #创建/编辑.Renviron文件，使得R在创建的R_libs文件夹加载库</span><br><span class="line">Rscript install.R #安装</span><br></pre></td></tr></table></figure>
+
+<p>安装后把目录下的genomescope.R文件加入环境变量即可使用。</p>
+<h2 id="6-2-使用"><a href="#6-2-使用" class="headerlink" title="6.2. 使用"></a>6.2. 使用</h2><p><code>genomescope.R -i histogram_file -o output_dir -k k-mer_length</code></p>
+<p>参数：</p>
+<ul>
+<li>-i histogram_file：频数分布直方表，jellyfish或KMC的结果。</li>
+<li>-k k-mer_length：k-mer长度，通常是17，21，与jellyfish&#x2F;KMC的设置一致。</li>
+<li>-o output_dir：输出目录，结果图和文本都输出到这个目录。</li>
+<li>-p ploidy：设置倍性。</li>
+<li>-l lambda：设置测序的平均k-mer覆盖率的初始猜测。</li>
+<li>-n name_prefix：设置输出文件的前缀。</li>
+<li>-m max_kmercov：设置从分析中排除高频k-mers的截止值，根据物种情况确定，推荐1000或10000。</li>
+</ul>
+<h1 id="7-GenomeScope实践经验"><a href="#7-GenomeScope实践经验" class="headerlink" title="7. GenomeScope实践经验"></a>7. GenomeScope实践经验</h1><ol>
+<li>实际使用中发现，GenomeScope1.0和2.0常常估算差异较大。建议二倍体还是使用GenomeScope1.0。</li>
 </ol>
-<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
-<li><a href="https://github.com/brentp/mosdepth">https://github.com/brentp/mosdepth</a></li>
+<ul>
+<li>在估算一个约300Mb的二倍体基因组时，GenomeScope1.0估算出来267Mb，GenomeScope2.0估算出来149Mb。</li>
+<li>在估算一个约6Gb的四倍体基因组时发现，GenomeScope1.0估算出来5.5Gb，GenomeScope2.0估算出来2.7Gb。</li>
+</ul>
+<h1 id="8-references"><a href="#8-references" class="headerlink" title="8. references"></a>8. references</h1><ol>
+<li>GenomeScope 1.0 github：<a href="https://github.com/schatzlab/genomescope">https://github.com/schatzlab/genomescope</a></li>
+<li>GenomeScope 2.0 github：<a href="https://github.com/tbenavi1/genomescope2.0">https://github.com/tbenavi1/genomescope2.0</a></li>
+<li>GenomeScope 1.0 paper：<a href="https://academic.oup.com/bioinformatics/article/33/14/2202/3089939">https://academic.oup.com/bioinformatics/article/33/14/2202/3089939</a></li>
+<li>GenomeScope 2.0 + Smudgeplot paper：<a href="https://www.nature.com/articles/s41467-020-14998-3">https://www.nature.com/articles/s41467-020-14998-3</a></li>
+<li>Smudgeplot github：<a href="https://github.com/KamilSJaron/smudgeplot">https://github.com/KamilSJaron/smudgeplot</a></li>
 </ol>
 <hr>
 <ul>
@@ -16319,17 +15868,15 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       <categories>
         <category>omics</category>
         <category>genome</category>
-        <category>quality assessment</category>
+        <category>genome survey</category>
       </categories>
       <tags>
-        <tag>biosoft</tag>
+        <tag>genome survey</tag>
         <tag>genome</tag>
-        <tag>quality assessment</tag>
-        <tag>mapping</tag>
-        <tag>sam</tag>
-        <tag>bam</tag>
-        <tag>depth</tag>
-        <tag>mosdepth</tag>
+        <tag>k-mer</tag>
+        <tag>KMC</tag>
+        <tag>GenomeScope</tag>
+        <tag>Smudgeplot</tag>
       </tags>
   </entry>
   <entry>
@@ -16666,7 +16213,7 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
 </li>
 <li><strong>SEQ_FEAT.TransLen</strong><ul>
 <li>表示蛋白质长度与预测的蛋白质长度不匹配，运行错误</li>
-<li>建议重跑table2asn，报错持续存在就写邮件把sample.sqn和运行的命令行发给NCBI(<a href="mailto:&#103;&#101;&#x6e;&#x6f;&#109;&#x65;&#115;&#64;&#x6e;&#99;&#x62;&#x69;&#46;&#110;&#108;&#109;&#46;&#110;&#105;&#104;&#x2e;&#x67;&#x6f;&#x76;">&#103;&#101;&#x6e;&#x6f;&#109;&#x65;&#115;&#64;&#x6e;&#99;&#x62;&#x69;&#46;&#110;&#108;&#109;&#46;&#110;&#105;&#104;&#x2e;&#x67;&#x6f;&#x76;</a>)让帮忙修改这个错误。</li>
+<li>建议重跑table2asn，报错持续存在就写邮件把sample.sqn和运行的命令行发给NCBI(<a href="mailto:&#103;&#x65;&#x6e;&#x6f;&#109;&#101;&#x73;&#x40;&#110;&#99;&#x62;&#x69;&#x2e;&#110;&#x6c;&#x6d;&#x2e;&#110;&#105;&#x68;&#x2e;&#103;&#111;&#x76;">&#103;&#x65;&#x6e;&#x6f;&#109;&#101;&#x73;&#x40;&#110;&#99;&#x62;&#x69;&#x2e;&#110;&#x6c;&#x6d;&#x2e;&#110;&#105;&#x68;&#x2e;&#103;&#111;&#x76;</a>)让帮忙修改这个错误。</li>
 </ul>
 </li>
 <li><strong>SEQ_FEAT.BadInternalCharacter</strong><ul>
@@ -17093,8 +16640,8 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
         <category>transcriptome</category>
       </categories>
       <tags>
-        <tag>Trinity</tag>
         <tag>DESeq2</tag>
+        <tag>Trinity</tag>
         <tag>RNA-sequencing</tag>
         <tag>RNA-seq</tag>
         <tag>HISAT2</tag>
@@ -17107,360 +16654,155 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>线粒体的重复介导重组的鉴定和计算重组频率</title>
-    <url>/2023/03/14/omics_organelle_recombination.frequency/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105213&auto=1&height=32"></iframe></div>
+    <title>用RSEM进行转录本定量分析</title>
+    <url>/2024/10/28/omics_transcriptome_expression_RSEM/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1400229069&auto=1&height=32"></iframe></div>
 
-<h1 id="1-线粒体的重复介导重组（repeat-mediated-recombination）"><a href="#1-线粒体的重复介导重组（repeat-mediated-recombination）" class="headerlink" title="1. 线粒体的重复介导重组（repeat-mediated recombination）"></a>1. 线粒体的重复介导重组（repeat-mediated recombination）</h1><ol>
-<li>线粒体的重复序列</li>
+<p><strong>写在前面</strong>：如果是Trinity组装的转录组，可以直接在Trinity自带脚本<code>align_and_estimate_abundance.pl</code>中实现转录本的定量分析，调用RSEM，更方便快捷，参考博客<a href="https://yanzhongsino.github.io/2024/10/28/omics_transcriptome_expression_Trinity.estimat.abundance/">用Trinity的脚本进行转录本定量分析</a>。</p>
+<h1 id="1-常用的转录本定量分析软件"><a href="#1-常用的转录本定量分析软件" class="headerlink" title="1. 常用的转录本定量分析软件"></a>1. 常用的转录本定量分析软件</h1><p>常用的转录本定量分析软件包括RSEM, Salmon, Kallisto, FeatureCounts等。</p>
+<ol>
+<li>RSEM是基于比对进行的转录本表达定量分析</li>
 </ol>
 <ul>
-<li>通常在植物线粒体上存在重复序列（这里的重复序列不是核基因组的TE等转座元件的概念，就是一段序列在另一个位置同时存在完全一致的序列），则有可能介导重组，从而生成线粒体的多种构象。重复序列常常成对存在（也有一些超过两个），被称作重复对（repeat pairs）。</li>
+<li>优点是<strong>高精度定量</strong>和<strong>详细的输出指标</strong>。RSEM支持有参和无参，基因和转录本，多种比对软件（Bowtie, Bowtie2, STAR），输出更详细的定量信息（包括reads count, TPM, FPKM等定量指标）。</li>
+<li>缺点是基于比对，所以运算速度较慢，需要更多计算资源，更适应小规模数据集。</li>
 </ul>
 <ol start="2">
-<li>鉴定的背景</li>
+<li>Salmon和Kallisto两者都是采用伪比对技术，不需要比对，速度更快。</li>
 </ol>
 <ul>
-<li>Illumina PE测序是双端测序，测序获得的reads常见的长度是150bp。一对reads的联合体的长度被称为插入尺寸（insert size）。插入尺寸（insert size）常在350bp左右，但具体的每对测序reads都不一样，要通过把reads进行mapping到参考序列上，才能测量insert size。</li>
-<li>如果是repeat pairs的长度小于测序reads的insert size长度，可以构建两种构象（参考构象和重组构象），把reads mapping到两种构象来判断repeat pairs是否介导重组（如果两种构象都有mapped reads则是repeat pair介导重组的证据），并且通过mapped reads的计数来计算重组频率（recombination frequency）。</li>
+<li>优点是速度快，适合大规模数据集。</li>
+<li>缺点是对序列的准确性要求更高，输出文件的信息相对RSEM较少，只包含reads count，TPM。</li>
 </ul>
-<h1 id="2-鉴定重复"><a href="#2-鉴定重复" class="headerlink" title="2. 鉴定重复"></a>2. 鉴定重复</h1><h2 id="2-1-鉴定重复的脚本"><a href="#2-1-鉴定重复的脚本" class="headerlink" title="2.1. 鉴定重复的脚本"></a>2.1. 鉴定重复的脚本</h2><p>这篇文章Repeats of Unusual Size in Plant Mitochondrial Genomes: Identification, Incidence and Evolution： <a href="https://academic.oup.com/g3journal/article/9/2/549/6026745">https://academic.oup.com/g3journal/article/9/2/549/6026745</a> 总结了植物线粒体基因组的重复序列的特征。研究表明，植物线粒体基因组比动物的要大，包含大量的非编码DNA，突变率低，重排率高。</p>
+<ol start="3">
+<li>FeatureCounts直接从已比对的BAM文件中进行read计数，只适用于有参考基因组的情况，需要自行使用hisat2或bowtie2比对。</li>
+</ol>
+<h1 id="2-RSEM简介"><a href="#2-RSEM简介" class="headerlink" title="2. RSEM简介"></a>2. RSEM简介</h1><p>RSEM（RNA-Seq by Expectation-Maximization）是从RNA-Seq数据定量估计基因和转录本表达水平的软件包。</p>
+<ol>
+<li>RSEM的特点</li>
+</ol>
 <ul>
-<li>文章提供了python脚本ROUSFinder.py，用在鉴定线粒体的重复序列上。</li>
-<li>脚本是python2写的，依赖主要有blastn。</li>
-<li>有以下三个版本。</li>
+<li>RSEM包提供了一个用户友好的界面，支持并行计算EM算法的线程，单端和双端reads，质量评分，长reads读取和RSPD估计。</li>
+<li>此外，它还提供了表达水平的后验均值和95%可信区间估计。</li>
+<li>RSEM也有自己的脚本来生成pdf格式的文本阅读深度图。RSEM的独特之处在于，读取深度图可以堆叠，黑色表示对唯一读取的贡献，红色表示对多读取的贡献。</li>
+<li>此外，从数据中学习到的模型也可以可视化。</li>
 </ul>
-<ol>
-<li>ROUSFinder.py</li>
+<ol start="2">
+<li>RSEM包含的功能和模块</li>
 </ol>
 <ul>
-<li>调用blastn进行一条序列与自身的成对比对。</li>
-<li>默认参数是最小重复50bp，E value 10,000， match的赏分是+1，mismatch的罚分是-20。</li>
-<li>脚本贴在下面：</li>
+<li>计算定量表达结果，包括reads count, TPM, FPKM。</li>
+<li>可视化表达结果，包括bam2gbam，可以生成转录坐标和基因组坐标的BAM和Wiggle文件，Wiggle图。基因组坐标文件可以通过UCSC基因组浏览器和Broad研究所的IGV可视化。转录坐标文件可以通过IGV可视化。</li>
+<li>建模 (Simulation)：还可以用<code>rsem-simulate-reads</code>模块基于参数建模，生成RNA-seq数据。</li>
+<li>差异表达分析：RSEM推荐用软件EBSeq来进行差异表达分析。常用的差异表达分析工具，如 edgeR 和 DESeq，并没有考虑到reads映射不确定性造成的变异。 EBSeq 是UW-Madison开发的一种经验贝叶斯差异表达分析工具，它可以根据父基因的同工型数量对同工型进行分组，从而将读图映射不确定性造成的变异考虑在内。 此外，它对异常值也有更强的鲁棒性。 有关 EBSeq 的更多信息，请访问 EBSeq 网站：<a href="https://www.biostat.wisc.edu/~kendzior/EBSEQ/%E3%80%82RSEM%E5%B8%A6%E6%9C%89EBSeq%E5%8C%85%EF%BC%8C%E5%8F%AA%E9%9C%80%E5%AE%89%E8%A3%85%60make">https://www.biostat.wisc.edu/~kendzior/EBSEQ/。RSEM带有EBSeq包，只需安装`make</a> ebseq&#96;即可使用EBSeq。</li>
+<li>先验增强的RSEM(Prior-Enhanced RSEM, pRSEM)：pRSEM使用更多的数据（如 ChIP-seq 数据，scRNA-seq数据）来分配 RNA-seq 多映射片段。 在 pRSEM 子文件夹以及 RSEM 的 rsem-prepare-reference 和 rsem-calculate-expression 脚本中包含了 pRSEM 代码。</li>
+</ul>
+<h1 id="3-RSEM安装"><a href="#3-RSEM安装" class="headerlink" title="3. RSEM安装"></a>3. RSEM安装</h1><ol>
+<li>依赖</li>
+</ol>
+<ul>
+<li>C++编译器：如GCC。</li>
+<li>Perl：用于运行一些脚本。</li>
+<li>R：用于数据分析和可视化。</li>
+<li>Python（可选）：如果需要使用–gff3选项。</li>
+<li>Bowtie&#x2F;Bowtie2&#x2F;STAR&#x2F;HISAT2（可选）：如果需要使用这些比对工具。</li>
 </ul>
-<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># January 16, 2018 version 1.1  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># Percent identity is limited to &gt;=99%, to allow for sequencing errors of &lt;1%.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=24&#x27;</span>, default=<span class="string">&#x27;24&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp&quot; &gt; &#x27;</span>+temprepeats)  </span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rt.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumRepeats\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
-
 <ol start="2">
-<li>MultipleRepeats.py</li>
+<li>RSEM安装</li>
 </ol>
 <ul>
-<li>MultipleRepeats.py批量运行一个文件夹下的每个序列文件。</li>
-<li>脚本贴在下面：</li>
+<li>conda安装：<code>conda install bioconda::rsem</code></li>
+<li>github下载和安装</li>
 </ul>
-<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Usage: -din directory of files to find repeats in  </span></span><br><span class="line"><span class="comment">#        -word word_size  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a directory of fasta sequence files&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-din&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;din&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta directory&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-word&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;word&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Word size for blast&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">din = results.din  </span><br><span class="line">word = results.word  </span><br><span class="line">  </span><br><span class="line">li = os.listdir(din)  </span><br><span class="line">inputs = <span class="built_in">filter</span>(<span class="keyword">lambda</span> x: <span class="string">&#x27;.fasta&#x27;</span> <span class="keyword">in</span> x, li)  </span><br><span class="line">inputs.sort()  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(inputs)):  </span><br><span class="line">    infile = <span class="built_in">str</span>(inputs[i])  </span><br><span class="line">    os.system(<span class="string">&quot;/home/alan/applications/ROUSFinder.py -m &quot;</span>+word+<span class="string">&quot; &quot;</span>+din+infile) </span><br></pre></td></tr></table></figure>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/deweylab/RSEM.git</span><br><span class="line">cd RSEM</span><br><span class="line">make</span><br><span class="line">make install</span><br></pre></td></tr></table></figure>
 
 <ol start="3">
-<li>ROUSFinder2.py</li>
+<li>添加到环境变量</li>
 </ol>
 <ul>
-<li>ROUSFinder2.py可以在命令行设置match赏分和mismatch罚分。</li>
-<li>脚本贴在下面：</li>
+<li>添加之后，如果可以使用<code>rsem-calculate-expression</code>命令则表示安装成果。</li>
 </ul>
-<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Version 2.0, November 21, 2018  </span></span><br><span class="line"><span class="comment"># Changes: uses variable parameters  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line"><span class="comment">#      -rew reward for match (default is 1)  </span></span><br><span class="line"><span class="comment">#      -pen penalty for mismatch (default is 20)  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=50&#x27;</span>, default=<span class="string">&#x27;50&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-rew&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;reward&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Reward for match&#x27;</span>, default=<span class="string">&#x27;1&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-pen&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;penalty&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Penalty for mismatch&#x27;</span>, default=<span class="string">&#x27;20&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">reward = results.reward  </span><br><span class="line">penalty = results.penalty  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname      </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10  -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp&quot; &gt; &#x27;</span>+temprepeats)  </span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rt.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumROUS\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
-
-<h2 id="2-2-运行脚本鉴定重复"><a href="#2-2-运行脚本鉴定重复" class="headerlink" title="2.2. 运行脚本鉴定重复"></a>2.2. 运行脚本鉴定重复</h2><p>选择ROUSFinder2.py脚本来鉴定线粒体的重复序列。</p>
-<ol>
-<li>脚本参数</li>
+<h1 id="4-RSEM使用"><a href="#4-RSEM使用" class="headerlink" title="4. RSEM使用"></a>4. RSEM使用</h1><h2 id="4-1-建立转录组的索引文件"><a href="#4-1-建立转录组的索引文件" class="headerlink" title="4.1. 建立转录组的索引文件"></a>4.1. 建立转录组的索引文件</h2><ol>
+<li>有参考基因组</li>
 </ol>
 <ul>
-<li>必需参数：fasta格式的输入序列</li>
-<li>-o out：输出文件名称</li>
-<li>-m 50：鉴定的重复序列的最小长度，默认是50bp。</li>
-<li>-b &#x2F;path&#x2F;to&#x2F;blastn&#x2F;：blastn命令的路径，默认是&#x2F;usr&#x2F;bin&#x2F;。</li>
-<li>-k：保留临时文件，out_tempblast.txt和out_temprepeats.txt</li>
-<li>-gb：生成重复序列的genbank格式文件，out_repeats.gb.txt</li>
-<li>-rew 1：match赏分，默认是+1</li>
-<li>-pen 20：mismatch罚分，默认是-20</li>
+<li>提供参考基因组genome.fa序列和GTF注释文件genome.annotation.gtf（或者用<code>-gff3 genome.annotation.gff3</code>指定gff3格式的注释文件），RSEM则会通过GTF注释文件从参考基因组序列中提取出各个转录本的序列，然后利用Bowtie2 or STAR等软件来建索引，索引文件前缀ref。</li>
+<li><code>rsem-prepare-reference -gtf genome.annotation.gtf --bowtie2 genome.fa ref</code></li>
+<li>生成提取的转录本序列文件，和bowtie2生成的索引文件ref.bt2</li>
 </ul>
 <ol start="2">
-<li>运行脚本</li>
+<li>无参考基因组</li>
 </ol>
 <ul>
-<li><code>ROUSFinder2.py input.fa -m 30 -gb -o out</code></li>
+<li>没有参考基因组的情况，可以用Trinity无参组装的转录本文件Trinity.fasta，生成转录组的索引，索引文件前缀Trinity_ref。</li>
+<li><code>rsem-prepare-reference --bowtie2 Trinity.fasta Trinity_ref</code></li>
+<li><ul>
+<li>用bowtie2生成索引文件Trinity_ref.bt2</li>
 </ul>
-<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><ol>
-<li>out_rep_table.txt：包含每个repeat单元的名称，长度，起始位置和终止位置，正负链（plus或minus）。</li>
-</ol>
-<ul>
-<li>第一行显示了第一个染色体的名称和长度。</li>
-<li>后面的行有五列，分别是repeat名称，repeat长度，repeat在染色体序列上的起始位置和终止位置，repeat的正负链方向。</li>
-<li>通常前面只有一行信息的重复单元（如Repeat_1和Repeat_2）需要手动去除，留下后面有2个或以上重复单元的信息（如Repeat_3和Repeat_4）作为重复序列鉴定的结果。</li>
+</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr01	147501</span><br><span class="line">Repeat_1	2702	1	2702	plus</span><br><span class="line">Repeat_2	2480	1	2480	plus</span><br><span class="line">Repeat_3	1171	1933	3103	plus</span><br><span class="line">Repeat_3	1171	2322	3492	plus</span><br><span class="line">Repeat_4	938	1696	2633	plus</span><br><span class="line">Repeat_4	938	2237	3174	plus</span><br></pre></td></tr></table></figure>
-
-<ol start="2">
-<li>out_rep.fasta：包含fasta格式的repeat序列</li>
-<li>out_rep_counts.txt：包含repeat的总数（NumROUS），平均长度（AvgSize），平均数量（AvgCopyNum），和每个repeat的长度和数量。</li>
-<li>out_repeats.gb.txt：使用-gb参数则生成此文件，是genbank格式的repeat的注释文件。</li>
-<li>out_binned.txt：包含&gt;50bp,100bp,150bp,…,1000bp,1250bp,1500bp,…,10000bp的repeat的数量</li>
-<li>out_tempblast.txt：使用-k参数会保留两个临时temp文件</li>
-<li>out_temprepeats.txt：使用-k参数会保留两个临时temp文件。</li>
+<ol start="3">
+<li>其他参数</li>
 </ol>
 <ul>
-<li>out_temprepeats.txt是用重复单元与输入序列做blastn的-outfmt 10的结果，包含qseqid length sstart send sstrand qcovhsp六列内容。</li>
-<li>out_rep_table.txt结果文件是从这个out_temprepeats.txt临时文件中提取生成的。</li>
+<li><code>--bowtie2</code>：指定建立索引文件的软件，可选<code>--bowtie</code>,<code>--bowtie2</code>,<code>--star</code>。</li>
+<li><code>--transcript-to-gene-map map.txt</code>：如果想生成基于基因水平的定量结果，则需要加上这个参数指定包含转录本和基因的对应关系的map.txt文件，文件包含两列gene_id transcript_id。指定这个参数后，除了生成transcript的定量结果，还会生成gene（一个基因有多个转录本）的定量结果。推荐使用。RSEM还提供了脚本从Trinity的结果生成map.txt文件，<code>extract-transcript-to-gene-map-from-trinity Trinity.fasta map.txt</code>。</li>
 </ul>
-<h2 id="2-4-修改鉴定重复的脚本，使其显示重复序列所在染色体位置"><a href="#2-4-修改鉴定重复的脚本，使其显示重复序列所在染色体位置" class="headerlink" title="2.4. 修改鉴定重复的脚本，使其显示重复序列所在染色体位置"></a>2.4. 修改鉴定重复的脚本，使其显示重复序列所在染色体位置</h2><p>ROUSFinder2.py脚本鉴定线粒体的重复序列是被设计的用于鉴定一条序列与自身的重复，所以结果文件中不包含重复所在染色体位置信息。好在脚本容易修改，就修改了脚本使其显示染色体位置，可用于多染色体线粒体基因组的重复序列鉴定。</p>
-<p>使用修改后脚本的输出结果中两个文件（out_rep_table.txt和out_temprepeats.txt）有所不同，其他结果文件都与修改前的脚本的结果一致。</p>
-<ol>
-<li>修改后的脚本ROUSFinder2.0_adjusted.by.yz.py</li>
+<h2 id="4-2-计算表达量"><a href="#4-2-计算表达量" class="headerlink" title="4.2. 计算表达量"></a>4.2. 计算表达量</h2><ol>
+<li>命令</li>
 </ol>
 <ul>
-<li>ROUSFinder2.0_adjusted.by.yz.py可以在结果文件out_rep_table.txt中显示染色体位置信息。</li>
-<li>修改后脚本保存在：<a href="https://github.com/yanzhongsino/bioscripts/blob/main/modified_scripts/ROUSFinder2.0_adjusted.by.yz.py">https://github.com/yanzhongsino/bioscripts/blob/main/modified_scripts/ROUSFinder2.0_adjusted.by.yz.py</a></li>
-<li>修改后脚本也贴在下面：</li>
+<li><code>rsem-calculate-expression --bowtie2 --paired-end -p 12 read_1.fastq read_2.fastq Trinity_ref sample_output</code></li>
 </ul>
-<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line"></span><br><span class="line"><span class="comment"># minior adjustments by Yan Zhong, November 27, 2023  </span></span><br><span class="line"><span class="comment"># Version 2.0, November 21, 2018  </span></span><br><span class="line"><span class="comment"># Changes: uses variable parameters  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line"><span class="comment">#      -rew reward for match (default is 1)  </span></span><br><span class="line"><span class="comment">#      -pen penalty for mismatch (default is 20)  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=50&#x27;</span>, default=<span class="string">&#x27;50&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-rew&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;reward&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Reward for match&#x27;</span>, default=<span class="string">&#x27;1&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-pen&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;penalty&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Penalty for mismatch&#x27;</span>, default=<span class="string">&#x27;20&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">reward = results.reward  </span><br><span class="line">penalty = results.penalty  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname      </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10  -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp sseqid qstart qend&quot; &gt; &#x27;</span>+temprepeats)  <span class="comment"># adjusted by yz</span></span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="comment"># rt.write(seqname+&#x27;\t&#x27;+str(seqlen)+&#x27;\n&#x27;) # adjusted by yz</span></span><br><span class="line">rt.write(<span class="string">&#x27;repeat.name&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;alignment.length&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;repeat.start&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;repeat.end&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;strand&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;qcovhsp&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.name&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.start&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.end&#x27;</span>+<span class="string">&#x27;\n&#x27;</span>)  <span class="comment"># adjusted by yz</span></span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">7</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">8</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">5</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">6</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\n&#x27;</span>)  <span class="comment"># adjusted by yz</span></span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumROUS\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
-
 <ol start="2">
-<li>修改前脚本的结果文件out_rep_table.txt</li>
+<li>参数</li>
 </ol>
 <ul>
-<li>第一行显示了第一个染色体的名称和长度。</li>
-<li>后面的行有五列，分别是重复单元的名称(repeat.name)，重复单元的长度(alignment.length)，重复单元在输入的染色体序列上的起始位置和终止位置(chr.start和chr.end)，重复单元的正负链方向(strand)。</li>
+<li>–bowtie2: 指定使用 Bowtie2 进行比对。</li>
+<li>–paired-end: 数据是PE测序的。</li>
+<li>-p 12: 指定使用 12 个线程进行计算。</li>
+<li>read_1.fastq read_2.fastq: 输入的配对 RNA-Seq 读段文件。</li>
+<li>Trinity_ref: RSEM 参考数据索引文件的前缀，上一步的ref或Trinity_ref。</li>
+<li>sample_output: 指定输出文件的前缀。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr01	147501</span><br><span class="line">Repeat_1	2702	1	2702	plus</span><br><span class="line">Repeat_2	2480	1	2480	plus</span><br><span class="line">Repeat_3	1171	1933	3103	plus</span><br><span class="line">Repeat_3	1171	2322	3492	plus</span><br><span class="line">Repeat_4	938	1696	2633	plus</span><br><span class="line">Repeat_4	938	2237	3174	plus</span><br></pre></td></tr></table></figure>
-
 <ol start="3">
-<li>修改后脚本的结果文件out_rep_table.txt</li>
+<li>常用参数</li>
 </ol>
 <ul>
-<li>添加了标题行。包含重复单元的名称(repeat.name)，重复单元的长度(alignment.length)，重复单元在重复序列上起始和终止位置(repeat.start和repeat.end)，重复单元的正负链方向(strand)，每个高度相似片段HSP中的重复单元的覆盖度(Query Coverage Per HSP, qcovhsp)，染色体序列名称(chr.name)，重复单元在输入的染色体序列上的起始位置和终止位置(chr.start和chr.end)。</li>
-<li>添加了列的展示，主要是染色体序列名称(chr.name)，还包括repeat.start和repeat.end，qcovhsp。</li>
-<li>调整了列的展示顺序。</li>
+<li>–estimate-rspd: 估算每个转录本的reads位置分布，默认启用。estimate the read start position distribution，看是否有positional biases。</li>
+<li>–calc-ci: 计算转录本丰度的置信区间。</li>
+<li>–append-names：用于在结果中附加上gene name和transcript name。</li>
+<li>–seed-length: 设置用于比对的种子长度，默认是 25。</li>
+<li>–output-genome-bam: 输出基于基因的 bam 文件（默认基于转录本的bam文件）。</li>
+<li>–no-bam-output: 不输出 BAM 文件。</li>
+<li>–time: 显示命令执行时间。</li>
 </ul>
-<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">repeat.name	alignment.length	repeat.start	repeat.end	strand	qcovhsp	chr.name	chr.start	chr.end</span><br><span class="line">Repeat_1	2702	1	2702	plus	100	Chr38	1	2702</span><br><span class="line">Repeat_2	2480	1	2480	plus	100	Chr39	1	2480</span><br><span class="line">Repeat_3	1171	1	1171	plus	100	Chr34	1933	3103</span><br><span class="line">Repeat_3	1171	1	1171	plus	100	Chr09	2322	3492</span><br><span class="line">Repeat_4	938	1	938	plus	100	Chr38	1696	2633</span><br><span class="line">Repeat_4	938	1	938	plus	100	Chr32	2237	3174</span><br></pre></td></tr></table></figure>
-
 <ol start="4">
-<li>修改后脚本的结果文件out_temprepeats.txt</li>
+<li>bowtie2的参数</li>
 </ol>
 <ul>
-<li>修改后脚本的结果文件out_temprepeats.txt比起修改前在后面多加了三列信息chr.name,repeat.start和repeat.end。</li>
+<li>RSEM调用bowtie2的参数可以通过–bowtie2-mismatch-rate，–bowtie2-k以及–bowtie2-sensitivity-level来设定。</li>
+<li>默认参数是：<code>bowtie2 -q --phred33 --sensitive --dpad 0 --gbar 99999999 --mp 1,1 --np 1 --score-min L,0,-0.1 -I 1 -X 1000 --no-mixed --no-discordant -p 6 -k 200 -x /path/to/index/ -1 RNA-seq.clean_1.fastq -2 RNA-seq.clean_2.fastq | samtools view -S -b -o sample.bam</code></li>
+<li>默认参数中：–gbar表示，RSEM不支持有gap的比对；–no-mixed则表示RSEM不支持单个reads的比对；–no-discordant则告诉我们RSEM也不支持paired reads discordant的比对; -k 200表明RSEM希望bowtie2输出最佳的200个比对结果。</li>
 </ul>
 <ol start="5">
-<li>notes（与脚本的修改无关）</li>
+<li>输出文件</li>
 </ol>
 <ul>
-<li>从修改后脚本的结果文件out_rep_table.txt的示例文件可看出，qcovhsp列绝大部分情况下是100，因为这个值高于98才会被鉴定为重复单元。</li>
-<li>repeat.start和repeat.end两列的值也一般是固定的，从1到重复单元的末位。这两列值是用重复序列与输入序列做blast，比对结果中比对到的重复序列的重复单元的位置，通常只有完全比对上（repeat.start和repeat.end两列值从1到重复单元的末位）的才被鉴定为重复单元。</li>
+<li>sample_output.genes.results: 包含基因水平的表达定量结果。</li>
+<li>sample_output.isoforms.results: 包含转录本水平的表达定量结果。</li>
+<li>sample_output.transcript.bam: 如果没有使用 –no-bam-output，会生成基于转录本比对的 BAM 文件。</li>
+<li>sample_output.genome.bam: 如果用 –output-genome-bam，会生成基于基因的比对的 BAM 文件。</li>
+<li>sample_output.stat：文件夹，包含一些统计结果。</li>
 </ul>
-<h2 id="2-5-重复的应用"><a href="#2-5-重复的应用" class="headerlink" title="2.5. 重复的应用"></a>2.5. 重复的应用</h2><ol>
-<li>辅助线粒体组装</li>
+<h2 id="4-3-一些解释"><a href="#4-3-一些解释" class="headerlink" title="4.3. 一些解释"></a>4.3. 一些解释</h2><ol>
+<li>结果文件sample_output.isoforms.results的每一列</li>
 </ol>
 <ul>
-<li>通过鉴定出来的重复，可以帮助线粒体组装得更完整。</li>
-<li>调整线粒体的已有组装。比如通过计算重组率，把主导的优势构象作为最后的线粒体组装构象。</li>
-</ul>
-<ol start="2">
-<li>鉴定重组</li>
-</ol>
-<ul>
-<li>重复序列可能介导重组，所以鉴定重复是鉴定重组的基础。</li>
-</ul>
-<h1 id="3-鉴定重组和计算重组率"><a href="#3-鉴定重组和计算重组率" class="headerlink" title="3. 鉴定重组和计算重组率"></a>3. 鉴定重组和计算重组率</h1><p>鉴定出线粒体的重复序列后，还可以进一步鉴定这些重复序列是否介导了重组。</p>
-<p>重组会导致构象的变化，构象变化的形式包括：</p>
-<ol>
-<li>位于不同染色体环上的一对重复序列可能介导重组，使得两个小环变成一个大环。</li>
-<li>位于同一个染色体环上的同方向的一对重复序列可能介导重组，使得一个大环变成两个小环。</li>
-<li>位于同一个染色体环上的反方向的一对重复序列，可能在其他重复对的重组变换下变成同方向或者两个小环的状态。</li>
-</ol>
-<h2 id="3-1-鉴定重组的步骤"><a href="#3-1-鉴定重组的步骤" class="headerlink" title="3.1. 鉴定重组的步骤"></a>3.1. 鉴定重组的步骤</h2><h3 id="3-1-1-构建参考构象和重组构象的序列"><a href="#3-1-1-构建参考构象和重组构象的序列" class="headerlink" title="3.1.1. 构建参考构象和重组构象的序列"></a>3.1.1. 构建参考构象和重组构象的序列</h3><ol>
-<li>针对一对可能造成重组的重复序列，构建参考构象和重组构象</li>
-<li>在参考构象和重组构象中，以重复序列加上两翼各300bp作为参考构象序列(ref_1和ref_2)和重组构象序列(rec_1和rec_2)</li>
-<li>构建参考序列和mapping注意事项</li>
-</ol>
-<ul>
-<li>可以把参考构象序列和重组构象序列（一共四条）一同作为参考序列（reference）用于mapping，避免同一条reads mapping到多个构象序列从而重复计数的情况。</li>
-<li>如果担心叶绿体派生的reads影响重组率的计算，还可以加上叶绿体基因组作为参考序列（reference），这样叶绿体的reads会被mapping到叶绿体上，从而起到过滤的作用。</li>
-<li>如果担心核基因派生的reads影响，可以用一个cutoff值(比如100bp)，小于100bp的mapping被筛除。</li>
-</ul>
-<h3 id="3-1-2-把reads往参考序列进行mapping"><a href="#3-1-2-把reads往参考序列进行mapping" class="headerlink" title="3.1.2. 把reads往参考序列进行mapping"></a>3.1.2. 把reads往参考序列进行mapping</h3><ol>
-<li>把reads往参考序列进行mapping，之后再根据mapping的reads的位置进行筛选，筛选出横跨repeat区域的有效mapping。</li>
-<li>比如repeat长度为220bp，两边各截取300bp，共820bp长度的参考序列。这里把跨越220bp的repeat，并且两边各至少映射上10bp的reads作为有效mapping。这意味着，mapping的起始位置需要小于290，终止位置需要大于530bp。</li>
-<li>bwa进行mapping后得到的bam文件的第4列代表read比对到的参考序列最左侧的位置坐标（未必对上第4列为0）；第9列代表pair read完全匹配到同一条参考序列时，两个read之间的长度。可以理解为insert size。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">a = 530 # 设置终止位置需要大于的值，这里是530bp</span><br><span class="line">bwa index reference.fa # 为参考序列建立索引</span><br><span class="line">bwa mem -t 4 reference.fa  sample_1.clean.fq sample_2.clean.fq | samtools sort -@ 4 -m 4G &gt; reference.bam # 映射reads到参考序列</span><br><span class="line">samtools view reference.bam |awk -F&quot;\t&quot; -v awka=&quot;$a&quot; &#x27;$4&lt;290 &amp;&amp; ($4+$9)&gt;awka &#123;print $0&#125;&#x27; &gt; reference.bam.filter # 筛选起始位置小于290，终止位置大于530bp的mapped reads。这里的`awk -F&quot;\t&quot;`参数设定列分隔符非常重要，已经坑过我两把了。</span><br></pre></td></tr></table></figure>
-
-<h3 id="3-1-3-计算重组率-recombination-rate"><a href="#3-1-3-计算重组率-recombination-rate" class="headerlink" title="3.1.3. 计算重组率(recombination rate)"></a>3.1.3. 计算重组率(recombination rate)</h3><ol>
-<li>把reads往参考序列进行mapping得到有效映射的比对文件reference.bam.filter后，计算不同参考序列的有效映射的reads数量（即数据行数）。</li>
-<li>重组构象的两条序列(rec_1和rec_2)的比对reads数量之和作为分子，重组构象的两条序列(rec_1和rec_2)的比对reads数量与参考构象的两条序列(ref_1和ref_2)的比对reads数量之和作为分母，两者的比值可作为重组率。</li>
-</ol>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">n_ref_1 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;ref_1&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列ref_1上有效映射reads的数量</span><br><span class="line">n_ref_2 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;ref_2&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列ref_2上有效映射reads的数量</span><br><span class="line">n_rec_1 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;rec_1&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列rec_1上有效映射reads的数量</span><br><span class="line">n_rec_2 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;rec_2&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列rec_2上有效映射reads的数量</span><br><span class="line">mapping_rate = $(($n_rec_1+$n_rec_2)/($n_ref_1+$n_ref_2+$n_rec_1+$n_rec_2)) # 计算重组率</span><br></pre></td></tr></table></figure>
-
-<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
-<li>ROUSFinder.py脚本：<a href="https://academic.oup.com/g3journal/article/9/2/549/6026745">https://academic.oup.com/g3journal/article/9/2/549/6026745</a></li>
-</ol>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=30% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>omics</category>
-        <category>organelle</category>
-        <category>recombination</category>
-      </categories>
-      <tags>
-        <tag>mitogenome</tag>
-        <tag>repeat</tag>
-        <tag>recombination</tag>
-        <tag>rearrangement</tag>
-        <tag>insert size</tag>
-        <tag>recombination frequency</tag>
-        <tag>ROUSFinder</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>用RSEM进行转录本定量分析</title>
-    <url>/2024/10/28/omics_transcriptome_expression_RSEM/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1400229069&auto=1&height=32"></iframe></div>
-
-<p><strong>写在前面</strong>：如果是Trinity组装的转录组，可以直接在Trinity自带脚本<code>align_and_estimate_abundance.pl</code>中实现转录本的定量分析，调用RSEM，更方便快捷，参考博客<a href="https://yanzhongsino.github.io/2024/10/28/omics_transcriptome_expression_Trinity.estimat.abundance/">用Trinity的脚本进行转录本定量分析</a>。</p>
-<h1 id="1-常用的转录本定量分析软件"><a href="#1-常用的转录本定量分析软件" class="headerlink" title="1. 常用的转录本定量分析软件"></a>1. 常用的转录本定量分析软件</h1><p>常用的转录本定量分析软件包括RSEM, Salmon, Kallisto, FeatureCounts等。</p>
-<ol>
-<li>RSEM是基于比对进行的转录本表达定量分析</li>
-</ol>
-<ul>
-<li>优点是<strong>高精度定量</strong>和<strong>详细的输出指标</strong>。RSEM支持有参和无参，基因和转录本，多种比对软件（Bowtie, Bowtie2, STAR），输出更详细的定量信息（包括reads count, TPM, FPKM等定量指标）。</li>
-<li>缺点是基于比对，所以运算速度较慢，需要更多计算资源，更适应小规模数据集。</li>
-</ul>
-<ol start="2">
-<li>Salmon和Kallisto两者都是采用伪比对技术，不需要比对，速度更快。</li>
-</ol>
-<ul>
-<li>优点是速度快，适合大规模数据集。</li>
-<li>缺点是对序列的准确性要求更高，输出文件的信息相对RSEM较少，只包含reads count，TPM。</li>
-</ul>
-<ol start="3">
-<li>FeatureCounts直接从已比对的BAM文件中进行read计数，只适用于有参考基因组的情况，需要自行使用hisat2或bowtie2比对。</li>
-</ol>
-<h1 id="2-RSEM简介"><a href="#2-RSEM简介" class="headerlink" title="2. RSEM简介"></a>2. RSEM简介</h1><p>RSEM（RNA-Seq by Expectation-Maximization）是从RNA-Seq数据定量估计基因和转录本表达水平的软件包。</p>
-<ol>
-<li>RSEM的特点</li>
-</ol>
-<ul>
-<li>RSEM包提供了一个用户友好的界面，支持并行计算EM算法的线程，单端和双端reads，质量评分，长reads读取和RSPD估计。</li>
-<li>此外，它还提供了表达水平的后验均值和95%可信区间估计。</li>
-<li>RSEM也有自己的脚本来生成pdf格式的文本阅读深度图。RSEM的独特之处在于，读取深度图可以堆叠，黑色表示对唯一读取的贡献，红色表示对多读取的贡献。</li>
-<li>此外，从数据中学习到的模型也可以可视化。</li>
-</ul>
-<ol start="2">
-<li>RSEM包含的功能和模块</li>
-</ol>
-<ul>
-<li>计算定量表达结果，包括reads count, TPM, FPKM。</li>
-<li>可视化表达结果，包括bam2gbam，可以生成转录坐标和基因组坐标的BAM和Wiggle文件，Wiggle图。基因组坐标文件可以通过UCSC基因组浏览器和Broad研究所的IGV可视化。转录坐标文件可以通过IGV可视化。</li>
-<li>建模 (Simulation)：还可以用<code>rsem-simulate-reads</code>模块基于参数建模，生成RNA-seq数据。</li>
-<li>差异表达分析：RSEM推荐用软件EBSeq来进行差异表达分析。常用的差异表达分析工具，如 edgeR 和 DESeq，并没有考虑到reads映射不确定性造成的变异。 EBSeq 是UW-Madison开发的一种经验贝叶斯差异表达分析工具，它可以根据父基因的同工型数量对同工型进行分组，从而将读图映射不确定性造成的变异考虑在内。 此外，它对异常值也有更强的鲁棒性。 有关 EBSeq 的更多信息，请访问 EBSeq 网站：<a href="https://www.biostat.wisc.edu/~kendzior/EBSEQ/%E3%80%82RSEM%E5%B8%A6%E6%9C%89EBSeq%E5%8C%85%EF%BC%8C%E5%8F%AA%E9%9C%80%E5%AE%89%E8%A3%85%60make">https://www.biostat.wisc.edu/~kendzior/EBSEQ/。RSEM带有EBSeq包，只需安装`make</a> ebseq&#96;即可使用EBSeq。</li>
-<li>先验增强的RSEM(Prior-Enhanced RSEM, pRSEM)：pRSEM使用更多的数据（如 ChIP-seq 数据，scRNA-seq数据）来分配 RNA-seq 多映射片段。 在 pRSEM 子文件夹以及 RSEM 的 rsem-prepare-reference 和 rsem-calculate-expression 脚本中包含了 pRSEM 代码。</li>
-</ul>
-<h1 id="3-RSEM安装"><a href="#3-RSEM安装" class="headerlink" title="3. RSEM安装"></a>3. RSEM安装</h1><ol>
-<li>依赖</li>
-</ol>
-<ul>
-<li>C++编译器：如GCC。</li>
-<li>Perl：用于运行一些脚本。</li>
-<li>R：用于数据分析和可视化。</li>
-<li>Python（可选）：如果需要使用–gff3选项。</li>
-<li>Bowtie&#x2F;Bowtie2&#x2F;STAR&#x2F;HISAT2（可选）：如果需要使用这些比对工具。</li>
-</ul>
-<ol start="2">
-<li>RSEM安装</li>
-</ol>
-<ul>
-<li>conda安装：<code>conda install bioconda::rsem</code></li>
-<li>github下载和安装</li>
-</ul>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/deweylab/RSEM.git</span><br><span class="line">cd RSEM</span><br><span class="line">make</span><br><span class="line">make install</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>添加到环境变量</li>
-</ol>
-<ul>
-<li>添加之后，如果可以使用<code>rsem-calculate-expression</code>命令则表示安装成果。</li>
-</ul>
-<h1 id="4-RSEM使用"><a href="#4-RSEM使用" class="headerlink" title="4. RSEM使用"></a>4. RSEM使用</h1><h2 id="4-1-建立转录组的索引文件"><a href="#4-1-建立转录组的索引文件" class="headerlink" title="4.1. 建立转录组的索引文件"></a>4.1. 建立转录组的索引文件</h2><ol>
-<li>有参考基因组</li>
-</ol>
-<ul>
-<li>提供参考基因组genome.fa序列和GTF注释文件genome.annotation.gtf（或者用<code>-gff3 genome.annotation.gff3</code>指定gff3格式的注释文件），RSEM则会通过GTF注释文件从参考基因组序列中提取出各个转录本的序列，然后利用Bowtie2 or STAR等软件来建索引，索引文件前缀ref。</li>
-<li><code>rsem-prepare-reference -gtf genome.annotation.gtf --bowtie2 genome.fa ref</code></li>
-<li>生成提取的转录本序列文件，和bowtie2生成的索引文件ref.bt2</li>
-</ul>
-<ol start="2">
-<li>无参考基因组</li>
-</ol>
-<ul>
-<li>没有参考基因组的情况，可以用Trinity无参组装的转录本文件Trinity.fasta，生成转录组的索引，索引文件前缀Trinity_ref。</li>
-<li><code>rsem-prepare-reference --bowtie2 Trinity.fasta Trinity_ref</code></li>
-<li><ul>
-<li>用bowtie2生成索引文件Trinity_ref.bt2</li>
-</ul>
-</li>
-</ul>
-<ol start="3">
-<li>其他参数</li>
-</ol>
-<ul>
-<li><code>--bowtie2</code>：指定建立索引文件的软件，可选<code>--bowtie</code>,<code>--bowtie2</code>,<code>--star</code>。</li>
-<li><code>--transcript-to-gene-map map.txt</code>：如果想生成基于基因水平的定量结果，则需要加上这个参数指定包含转录本和基因的对应关系的map.txt文件，文件包含两列gene_id transcript_id。指定这个参数后，除了生成transcript的定量结果，还会生成gene（一个基因有多个转录本）的定量结果。推荐使用。RSEM还提供了脚本从Trinity的结果生成map.txt文件，<code>extract-transcript-to-gene-map-from-trinity Trinity.fasta map.txt</code>。</li>
-</ul>
-<h2 id="4-2-计算表达量"><a href="#4-2-计算表达量" class="headerlink" title="4.2. 计算表达量"></a>4.2. 计算表达量</h2><ol>
-<li>命令</li>
-</ol>
-<ul>
-<li><code>rsem-calculate-expression --bowtie2 --paired-end -p 12 read_1.fastq read_2.fastq Trinity_ref sample_output</code></li>
-</ul>
-<ol start="2">
-<li>参数</li>
-</ol>
-<ul>
-<li>–bowtie2: 指定使用 Bowtie2 进行比对。</li>
-<li>–paired-end: 数据是PE测序的。</li>
-<li>-p 12: 指定使用 12 个线程进行计算。</li>
-<li>read_1.fastq read_2.fastq: 输入的配对 RNA-Seq 读段文件。</li>
-<li>Trinity_ref: RSEM 参考数据索引文件的前缀，上一步的ref或Trinity_ref。</li>
-<li>sample_output: 指定输出文件的前缀。</li>
-</ul>
-<ol start="3">
-<li>常用参数</li>
-</ol>
-<ul>
-<li>–estimate-rspd: 估算每个转录本的reads位置分布，默认启用。estimate the read start position distribution，看是否有positional biases。</li>
-<li>–calc-ci: 计算转录本丰度的置信区间。</li>
-<li>–append-names：用于在结果中附加上gene name和transcript name。</li>
-<li>–seed-length: 设置用于比对的种子长度，默认是 25。</li>
-<li>–output-genome-bam: 输出基于基因的 bam 文件（默认基于转录本的bam文件）。</li>
-<li>–no-bam-output: 不输出 BAM 文件。</li>
-<li>–time: 显示命令执行时间。</li>
-</ul>
-<ol start="4">
-<li>bowtie2的参数</li>
-</ol>
-<ul>
-<li>RSEM调用bowtie2的参数可以通过–bowtie2-mismatch-rate，–bowtie2-k以及–bowtie2-sensitivity-level来设定。</li>
-<li>默认参数是：<code>bowtie2 -q --phred33 --sensitive --dpad 0 --gbar 99999999 --mp 1,1 --np 1 --score-min L,0,-0.1 -I 1 -X 1000 --no-mixed --no-discordant -p 6 -k 200 -x /path/to/index/ -1 RNA-seq.clean_1.fastq -2 RNA-seq.clean_2.fastq | samtools view -S -b -o sample.bam</code></li>
-<li>默认参数中：–gbar表示，RSEM不支持有gap的比对；–no-mixed则表示RSEM不支持单个reads的比对；–no-discordant则告诉我们RSEM也不支持paired reads discordant的比对; -k 200表明RSEM希望bowtie2输出最佳的200个比对结果。</li>
-</ul>
-<ol start="5">
-<li>输出文件</li>
-</ol>
-<ul>
-<li>sample_output.genes.results: 包含基因水平的表达定量结果。</li>
-<li>sample_output.isoforms.results: 包含转录本水平的表达定量结果。</li>
-<li>sample_output.transcript.bam: 如果没有使用 –no-bam-output，会生成基于转录本比对的 BAM 文件。</li>
-<li>sample_output.genome.bam: 如果用 –output-genome-bam，会生成基于基因的比对的 BAM 文件。</li>
-<li>sample_output.stat：文件夹，包含一些统计结果。</li>
-</ul>
-<h2 id="4-3-一些解释"><a href="#4-3-一些解释" class="headerlink" title="4.3. 一些解释"></a>4.3. 一些解释</h2><ol>
-<li>结果文件sample_output.isoforms.results的每一列</li>
-</ol>
-<ul>
-<li>第1，2列分别是transcript id和gene id，如果像我一样设置了–append-names参数，那么在ID后面还会附上transcript name和gene name（以下划线_分隔）</li>
-<li>第3，4列分别是transcript length和effective_length，这里是length是没有包括转录本的poly(A) tail的，而effective_length则是指可以有fragment覆盖的区域，计算公式：transcript length - mean fragment length + 1；如果是.genes.results中的length，则是所有transcript length的加权平均数</li>
-<li>第4，5，6列则分别是expected_count，TPM，FPKM；expected_count比较复杂，跟一般的count数定义有点不一样，是指the sum of the posterior probability of each read comes from this transcript over all reads，RSEM给出的解释是（复制黏贴了）：Because (1) each read aligning to this transcript has a probability of being generated from background noise; (2) RSEM may filter some alignable low quality reads, the sum of expected counts for all transcript are generally less than the total number of reads aligned；TPM和FPKM则比较常见了</li>
-<li>第7列则是IsoPct，其含义是转录本上的丰度占其基因上的丰度的百分比。</li>
+<li>第1，2列分别是transcript id和gene id，如果像我一样设置了–append-names参数，那么在ID后面还会附上transcript name和gene name（以下划线_分隔）</li>
+<li>第3，4列分别是transcript length和effective_length，这里是length是没有包括转录本的poly(A) tail的，而effective_length则是指可以有fragment覆盖的区域，计算公式：transcript length - mean fragment length + 1；如果是.genes.results中的length，则是所有transcript length的加权平均数</li>
+<li>第4，5，6列则分别是expected_count，TPM，FPKM；expected_count比较复杂，跟一般的count数定义有点不一样，是指the sum of the posterior probability of each read comes from this transcript over all reads，RSEM给出的解释是（复制黏贴了）：Because (1) each read aligning to this transcript has a probability of being generated from background noise; (2) RSEM may filter some alignable low quality reads, the sum of expected counts for all transcript are generally less than the total number of reads aligned；TPM和FPKM则比较常见了</li>
+<li>第7列则是IsoPct，其含义是转录本上的丰度占其基因上的丰度的百分比。</li>
 </ul>
 <ol start="2">
 <li>multimapping reads（多重比对reads）的处理</li>
@@ -17629,34 +16971,239 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>scripts</title>
-    <url>/2021/04/01/programming_scripts/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=476751845&auto=1&height=32"></iframe></div>
-
-<h1 id="1-我写的脚本"><a href="#1-我写的脚本" class="headerlink" title="1. 我写的脚本"></a>1. 我写的脚本</h1><h2 id="1-1-多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测"><a href="#1-1-多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测" class="headerlink" title="1.1. 多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测"></a>1.1. 多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/multi-transcriptome_assembly_with_ref.sh">multi-transcriptome_assembly_with_ref.sh</a></p>
-<h2 id="1-2-提取，合并，和比对单拷贝基因和蛋白"><a href="#1-2-提取，合并，和比对单拷贝基因和蛋白" class="headerlink" title="1.2. 提取，合并，和比对单拷贝基因和蛋白"></a>1.2. 提取，合并，和比对单拷贝基因和蛋白</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/extract_single_copy_genes.sh">extract_single_copy_genes.sh</a></p>
-<h2 id="1-3-合并不同物种数量的多序列比对文件，保持序列的比对"><a href="#1-3-合并不同物种数量的多序列比对文件，保持序列的比对" class="headerlink" title="1.3. 合并不同物种数量的多序列比对文件，保持序列的比对"></a>1.3. 合并不同物种数量的多序列比对文件，保持序列的比对</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/concat_msa_with_differenct_species.sh">concat_msa_with_differenct_species.sh</a></p>
-<h1 id="2-我修改的脚本（其他人写的）"><a href="#2-我修改的脚本（其他人写的）" class="headerlink" title="2. 我修改的脚本（其他人写的）"></a>2. 我修改的脚本（其他人写的）</h1><h2 id="2-1-从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息"><a href="#2-1-从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息" class="headerlink" title="2.1. 从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息"></a>2.1. 从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/modifiedscripts/extract_intron_info.pl">extract_intron_info.pl</a></p>
-<h1 id="3-保存的脚本"><a href="#3-保存的脚本" class="headerlink" title="3. 保存的脚本"></a>3. 保存的脚本</h1><h2 id="3-1-batch-run-sh-——-多线程并行运行批量化命令"><a href="#3-1-batch-run-sh-——-多线程并行运行批量化命令" class="headerlink" title="3.1. batch_run.sh —— 多线程并行运行批量化命令"></a>3.1. batch_run.sh —— 多线程并行运行批量化命令</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/saved_scripts/batch_run.sh">batch_run.sh</a></p>
-<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_">#</span><span class="language-bash">!/bin/bash</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">batch_run.sh脚本用于多线程并行批量化循环命令，并控制并行数量始终为<span class="variable">$&#123;thread&#125;</span>。</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">usage: 修改thread=后的值为想要设定的并行线程数，把需要并行的命令替换23行的run something。运行脚本batch_run.sh，实现并行运行循环任务。</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">ref：网上找的脚本，来源找不到了，侵删。</span></span><br><span class="line"></span><br><span class="line">thread=16</span><br><span class="line"></span><br><span class="line">start_time=`date +%s`              # 定义脚本运行的开始时间</span><br><span class="line">echo $&#123;start_time&#125;</span><br><span class="line">tmpFifo=/tmp/$$.fifo # 声明管道名称，$$表示脚本当前运行的进程PID</span><br><span class="line">mkfifo $&#123;tmpFifo&#125; # 创建有名管道</span><br><span class="line">exec 3&lt;&gt;$&#123;tmpFifo&#125;                   #创建文件描述符，以可读（&lt;）可写（&gt;）的方式关联管道文件，这时候文件描述符3就有了有名管道文件的所有特性</span><br><span class="line">rm -rf $&#123;tmpFifo&#125;                    #关联后的文件描述符拥有管道文件的所有特性,所以这时候管道文件可以删除，我们留下文件描述符来用就可以了</span><br><span class="line">for ((i=1;i&lt;=$&#123;thread&#125;;i++))</span><br><span class="line">do</span><br><span class="line">        echo &quot;&quot; &gt;&amp;3                   #&amp;3代表引用文件描述符3，这条命令代表往管道里面放入了一个&quot;令牌&quot;</span><br><span class="line">done</span><br><span class="line"></span><br><span class="line">for sample in $(cat sample_list.txt) #并行运行的循环</span><br><span class="line">do</span><br><span class="line">        read -u3                           #代表从管道中读取一个令牌</span><br><span class="line">        &#123;</span><br><span class="line">                echo $&#123;sample&#125;</span><br><span class="line">                run something # 需要并行运行的真实命令</span><br><span class="line">                echo &#x27;success&#x27; $&#123;sample&#125;       </span><br><span class="line">                echo &quot;&quot; &gt;&amp;3                   #代表我这一次命令执行到最后，把令牌放回管道</span><br><span class="line">        &#125; &amp;</span><br><span class="line">done</span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_">#</span><span class="language-bash"><span class="comment"># ps:可以使用while read line; do echo $line; done &lt; file.txt代替for i in $(cat file.txt);do echo $i; done循环。但是不能用cat file.txt | while read line; do echo $line; done代替。</span></span></span><br><span class="line"></span><br><span class="line">wait</span><br><span class="line"> </span><br><span class="line">stop_time=`date +%s`  #定义脚本运行的结束时间</span><br><span class="line"> </span><br><span class="line">echo &quot;TIME:`expr $stop_time - $start_time`&quot;</span><br><span class="line">exec 3&lt;&amp;-                       #关闭文件描述符的读</span><br><span class="line">exec 3&gt;&amp;-                       #关闭文件描述符的写</span><br></pre></td></tr></table></figure>
+    <title>线粒体的重复介导重组的鉴定和计算重组频率</title>
+    <url>/2023/03/14/omics_organelle_recombination.frequency/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105213&auto=1&height=32"></iframe></div>
 
-<h2 id="3-2-infernal-tblout2gff-pl-——-基因组ncRNA的infernal注释结果tblout格式转换成gff格式"><a href="#3-2-infernal-tblout2gff-pl-——-基因组ncRNA的infernal注释结果tblout格式转换成gff格式" class="headerlink" title="3.2. infernal-tblout2gff.pl —— 基因组ncRNA的infernal注释结果tblout格式转换成gff格式"></a>3.2. infernal-tblout2gff.pl —— 基因组ncRNA的infernal注释结果tblout格式转换成gff格式</h2><ol>
-<li>背景<br>用Infernal注释基因组的ncRNA，得到的结果可以用脚本整理成gff3格式。</li>
+<h1 id="1-线粒体的重复介导重组（repeat-mediated-recombination）"><a href="#1-线粒体的重复介导重组（repeat-mediated-recombination）" class="headerlink" title="1. 线粒体的重复介导重组（repeat-mediated recombination）"></a>1. 线粒体的重复介导重组（repeat-mediated recombination）</h1><ol>
+<li>线粒体的重复序列</li>
 </ol>
-<p>脚本来源：<a href="https://www.cnblogs.com/jessepeng/p/15392809.html">https://www.cnblogs.com/jessepeng/p/15392809.html</a></p>
+<ul>
+<li>通常在植物线粒体上存在重复序列（这里的重复序列不是核基因组的TE等转座元件的概念，就是一段序列在另一个位置同时存在完全一致的序列），则有可能介导重组，从而生成线粒体的多种构象。重复序列常常成对存在（也有一些超过两个），被称作重复对（repeat pairs）。</li>
+</ul>
 <ol start="2">
-<li>脚本</li>
+<li>鉴定的背景</li>
 </ol>
-<figure class="highlight perl"><table><tr><td class="code"><pre><span class="line"><span class="comment">#!/usr/bin/env perl</span></span><br><span class="line"><span class="comment"># infernal-tblout2gff.pl: convert cmsearch or cmscan tblout files to GFF format</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># an important point of above ref:</span></span><br><span class="line"><span class="comment"># &quot;Start is always less than or equal to end&quot;</span></span><br><span class="line"><span class="comment"># EPN, Fri Jun  7 11:07:38 2019</span></span><br><span class="line"><span class="comment"># </span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="keyword">use</span> strict;</span><br><span class="line"><span class="keyword">use</span> warnings;</span><br><span class="line"><span class="keyword">use</span> Getopt::Long;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $in_tblout  = <span class="string">&quot;&quot;</span>;   <span class="comment"># name of input tblout file</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $usage;</span><br><span class="line">$usage  = <span class="string">&quot;infernal-tblout2gff.pl\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;Usage:\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl [OPTIONS] &lt;cmsearch tblout file&gt;\n\tOR\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl --cmscan [OPTIONS] &lt;cmscan tblout file&gt;\n\tOR\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl --cmscan --fmt2 [OPTIONS] &lt;cmscan --fmt 2 tblout file&gt;\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\tOPTIONS:\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t-T &lt;n&gt;       : minimum bit score to include is &lt;n&gt;\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t-E &lt;x&gt;       : maximum E-value to include is &lt;x&gt;\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--cmscan     : tblout file was created by cmscan\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--source &lt;s&gt; : specify &#x27;source&#x27; field should be &lt;s&gt; (e.g. tRNAscan-SE)\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--fmt2       : tblout file was created with cmscan --fmt 2 option\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--all        : output all info in &#x27;attributes&#x27; column   [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--none       : output no info in &#x27;attributes&#x27; column    [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--desc       : output desc field in &#x27;attributes&#x27; column [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--version &lt;s&gt;: append \&quot;-&lt;s&gt;\&quot; to &#x27;source&#x27; column\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--extra &lt;s&gt;  : append \&quot;&lt;s&gt;;\&quot; to &#x27;attributes&#x27; column\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--hidedesc   : do not includ \&quot;desc\:\&quot; prior to desc value in &#x27;attributes&#x27; column\n&quot;</span>;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $do_minscore = <span class="number">0</span>;       <span class="comment"># set to &#x27;1&#x27; if -T used</span></span><br><span class="line"><span class="keyword">my</span> $do_maxevalue = <span class="number">0</span>;      <span class="comment"># set to &#x27;1&#x27; if -E used</span></span><br><span class="line"><span class="keyword">my</span> $minscore   = <span class="keyword">undef</span>;    <span class="comment"># defined if if -T used</span></span><br><span class="line"><span class="keyword">my</span> $maxevalue  = <span class="keyword">undef</span>;    <span class="comment"># defined if -E used</span></span><br><span class="line"><span class="keyword">my</span> $do_cmscan  = <span class="number">0</span>;        <span class="comment"># set to &#x27;1&#x27; if --cmscan used</span></span><br><span class="line"><span class="keyword">my</span> $do_fmt2    = <span class="number">0</span>;        <span class="comment"># set to &#x27;1&#x27; if --fmt</span></span><br><span class="line"><span class="keyword">my</span> $do_all_attributes = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --all</span></span><br><span class="line"><span class="keyword">my</span> $do_no_attributes  = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --none</span></span><br><span class="line"><span class="keyword">my</span> $do_de_attributes  = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --desc</span></span><br><span class="line"><span class="keyword">my</span> $version = <span class="keyword">undef</span>;       <span class="comment"># defined if --version used</span></span><br><span class="line"><span class="keyword">my</span> $extra = <span class="keyword">undef</span>;         <span class="comment"># defined if --extra used</span></span><br><span class="line"><span class="keyword">my</span> $do_hidedesc = <span class="number">0</span>;       <span class="comment"># set to 1 if --hidedesc used</span></span><br><span class="line"><span class="keyword">my</span> $opt_source = <span class="keyword">undef</span>;    <span class="comment"># set to &lt;s&gt; if --source &lt;s&gt; used</span></span><br><span class="line"></span><br><span class="line">&amp;GetOptions( <span class="string">&quot;T=s&quot;</span>       =&gt; \$minscore,</span><br><span class="line">             <span class="string">&quot;E=s&quot;</span>       =&gt; \$maxevalue,</span><br><span class="line">             <span class="string">&quot;cmscan&quot;</span>    =&gt; \$do_cmscan,</span><br><span class="line">             <span class="string">&quot;source=s&quot;</span>  =&gt; \$opt_source,</span><br><span class="line">             <span class="string">&quot;fmt2&quot;</span>      =&gt; \$do_fmt2,</span><br><span class="line">             <span class="string">&quot;all&quot;</span>       =&gt; \$do_all_attributes,</span><br><span class="line">             <span class="string">&quot;none&quot;</span>      =&gt; \$do_no_attributes,</span><br><span class="line">             <span class="string">&quot;desc&quot;</span>      =&gt; \$do_de_attributes,</span><br><span class="line">             <span class="string">&quot;version=s&quot;</span> =&gt; \$version, </span><br><span class="line">             <span class="string">&quot;extra=s&quot;</span>   =&gt; \$extra,</span><br><span class="line">             <span class="string">&quot;hidedesc&quot;</span>  =&gt; \$do_hidedesc);</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">scalar</span>(@ARGV) != <span class="number">1</span>) &#123; <span class="keyword">die</span> $usage; &#125;</span><br><span class="line"><span class="keyword">my</span> ($tblout_file) = @ARGV;</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $minscore)  &#123; $do_minscore  = <span class="number">1</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $maxevalue) &#123; $do_maxevalue = <span class="number">1</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>($do_minscore &amp;&amp; $do_maxevalue) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, -T and -E cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_all_attributes) &amp;&amp; ($do_no_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --all and --none cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_all_attributes) &amp;&amp; ($do_de_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --all and --desc cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_no_attributes) &amp;&amp; ($do_de_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --none and --desc cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_fmt2) &amp;&amp; (! $do_cmscan)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --fmt2 only makes sense in combination with --cmscan&quot;</span>; </span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>((<span class="keyword">defined</span> $opt_source) &amp;&amp; (<span class="keyword">defined</span> $version)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --source and --version are incompatible&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(! -e $tblout_file) &#123; <span class="keyword">die</span> <span class="string">&quot;ERROR tblout file $tblout_file does not exist&quot;</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>(! -s $tblout_file) &#123; <span class="keyword">die</span> <span class="string">&quot;ERROR tblout file $tblout_file is empty&quot;</span>; &#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $source = ($do_cmscan) ? <span class="string">&quot;cmscan&quot;</span> : <span class="string">&quot;cmsearch&quot;</span>;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $version)    &#123; $source .= <span class="string">&quot;-&quot;</span> . $version; &#125;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $opt_source) &#123; $source = $opt_source; &#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">open</span>(IN, $tblout_file) || <span class="keyword">die</span> <span class="string">&quot;ERROR unable to open $tblout_file for reading&quot;</span>; </span><br><span class="line"><span class="keyword">my</span> $line;</span><br><span class="line"><span class="keyword">my</span> $i;</span><br><span class="line"><span class="keyword">while</span>($line = &lt;IN&gt;) &#123; </span><br><span class="line">  <span class="keyword">if</span>($line !~ <span class="regexp">m/^\#/</span>) &#123; </span><br><span class="line">    <span class="keyword">chomp</span> $line;</span><br><span class="line">    <span class="keyword">my</span> @el_A = <span class="keyword">split</span>(<span class="regexp">/\s+/</span>, $line);</span><br><span class="line">    <span class="keyword">my</span> $nfields = <span class="keyword">scalar</span>(@el_A);</span><br><span class="line">    <span class="keyword">if</span>((! $do_fmt2) &amp;&amp; ($nfields &lt; <span class="number">18</span>)) &#123; </span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR expected at least 18 space delimited fields in tblout line (fmt 1, default) but got $nfields on line:\n$line\n&quot;</span>; </span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(($do_fmt2) &amp;&amp; ($nfields &lt; <span class="number">27</span>)) &#123; </span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR expected at least 27 space delimited fields in tblout line (fmt 2, --fmt2) but got $nfields on line:\n$line\n&quot;</span>; </span><br><span class="line">    &#125;</span><br><span class="line">    <span class="comment"># ref Infernal 1.1.2 user guide, pages 59-61</span></span><br><span class="line">    <span class="keyword">my</span> $idx     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqname = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqaccn = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlname = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlaccn = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $clan    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdl     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlfrom = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlto   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqfrom = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqto   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $strand  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $trunc   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $pass    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $gc      = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $bias    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $score   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $evalue  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $inc     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $olp     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyidx  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyfrct1= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyfrct2= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winidx  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winfrct1= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winfrct2= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $desc    = <span class="keyword">undef</span>;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">if</span>($do_fmt2) &#123; <span class="comment"># 27 fields</span></span><br><span class="line">      $idx     = $el_A[<span class="number">0</span>];</span><br><span class="line">      $seqname = ($do_cmscan) ? $el_A[<span class="number">3</span>] : $el_A[<span class="number">1</span>];</span><br><span class="line">      $seqaccn = ($do_cmscan) ? $el_A[<span class="number">4</span>] : $el_A[<span class="number">2</span>];</span><br><span class="line">      $mdlname = ($do_cmscan) ? $el_A[<span class="number">1</span>] : $el_A[<span class="number">3</span>];</span><br><span class="line">      $mdlaccn = ($do_cmscan) ? $el_A[<span class="number">2</span>] : $el_A[<span class="number">4</span>];</span><br><span class="line">      $clan    = $el_A[<span class="number">5</span>];</span><br><span class="line">      $mdl     = $el_A[<span class="number">6</span>];</span><br><span class="line">      $mdlfrom = $el_A[<span class="number">7</span>];</span><br><span class="line">      $mdlto   = $el_A[<span class="number">8</span>];</span><br><span class="line">      $seqfrom = $el_A[<span class="number">9</span>];</span><br><span class="line">      $seqto   = $el_A[<span class="number">10</span>];</span><br><span class="line">      $strand  = $el_A[<span class="number">11</span>];</span><br><span class="line">      $trunc   = $el_A[<span class="number">12</span>];</span><br><span class="line">      $pass    = $el_A[<span class="number">13</span>];</span><br><span class="line">      $gc      = $el_A[<span class="number">14</span>];</span><br><span class="line">      $bias    = $el_A[<span class="number">15</span>];</span><br><span class="line">      $score   = $el_A[<span class="number">16</span>];</span><br><span class="line">      $evalue  = $el_A[<span class="number">17</span>];</span><br><span class="line">      $inc     = $el_A[<span class="number">18</span>];</span><br><span class="line">      $olp     = $el_A[<span class="number">19</span>];</span><br><span class="line">      $anyidx  = $el_A[<span class="number">20</span>];</span><br><span class="line">      $anyfrct1= $el_A[<span class="number">21</span>];</span><br><span class="line">      $anyfrct2= $el_A[<span class="number">22</span>];</span><br><span class="line">      $winidx  = $el_A[<span class="number">23</span>];</span><br><span class="line">      $winfrct1= $el_A[<span class="number">24</span>];</span><br><span class="line">      $winfrct2= $el_A[<span class="number">25</span>];</span><br><span class="line">      $desc    = $el_A[<span class="number">26</span>]; </span><br><span class="line">      <span class="keyword">for</span>($i = <span class="number">27</span>; $i &lt; $nfields; $i++) &#123; $desc .= <span class="string">&quot;_&quot;</span> . $el_A[$i]; &#125;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">else</span> &#123; <span class="comment"># fmt 1, default</span></span><br><span class="line">      $seqname = ($do_cmscan) ? $el_A[<span class="number">2</span>] : $el_A[<span class="number">0</span>];</span><br><span class="line">      $seqaccn = ($do_cmscan) ? $el_A[<span class="number">3</span>] : $el_A[<span class="number">1</span>];</span><br><span class="line">      $mdlname = ($do_cmscan) ? $el_A[<span class="number">0</span>] : $el_A[<span class="number">2</span>];</span><br><span class="line">      $mdlaccn = ($do_cmscan) ? $el_A[<span class="number">1</span>] : $el_A[<span class="number">3</span>];</span><br><span class="line">      $mdl     = $el_A[<span class="number">4</span>];</span><br><span class="line">      $mdlfrom = $el_A[<span class="number">5</span>];</span><br><span class="line">      $mdlto   = $el_A[<span class="number">6</span>];</span><br><span class="line">      $seqfrom = $el_A[<span class="number">7</span>];</span><br><span class="line">      $seqto   = $el_A[<span class="number">8</span>];</span><br><span class="line">      $strand  = $el_A[<span class="number">9</span>];</span><br><span class="line">      $trunc   = $el_A[<span class="number">10</span>];</span><br><span class="line">      $pass    = $el_A[<span class="number">11</span>];</span><br><span class="line">      $gc      = $el_A[<span class="number">12</span>];</span><br><span class="line">      $bias    = $el_A[<span class="number">13</span>];</span><br><span class="line">      $score   = $el_A[<span class="number">14</span>];</span><br><span class="line">      $evalue  = $el_A[<span class="number">15</span>];</span><br><span class="line">      $inc     = $el_A[<span class="number">16</span>];</span><br><span class="line">      $desc    = $el_A[<span class="number">17</span>]; </span><br><span class="line">      <span class="keyword">for</span>($i = <span class="number">18</span>; $i &lt; $nfields; $i++) &#123; $desc .= <span class="string">&quot;_&quot;</span> . $el_A[$i]; &#125;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="comment"># one sanity check, strand should make sense</span></span><br><span class="line">    <span class="keyword">if</span>(($strand <span class="keyword">ne</span> <span class="string">&quot;+&quot;</span>) &amp;&amp; ($strand <span class="keyword">ne</span> <span class="string">&quot;-&quot;</span>)) &#123; </span><br><span class="line">      <span class="keyword">if</span>(($do_fmt2) &amp;&amp; (($seqfrom eq <span class="string">&quot;+&quot;</span>) || ($seqfrom eq <span class="string">&quot;-&quot;</span>))) &#123; </span><br><span class="line">        <span class="keyword">die</span> <span class="string">&quot;ERROR problem parsing, you specified --fmt2 but tblout file appears to have NOT been created with --fmt 2, retry without --fmt2\nproblematic line:\n$line\n&quot;</span>; </span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">if</span>((! $do_fmt2) &amp;&amp; (($pass eq <span class="string">&quot;+&quot;</span>) || ($pass eq <span class="string">&quot;-&quot;</span>))) &#123; </span><br><span class="line">        <span class="keyword">die</span> <span class="string">&quot;ERROR problem parsing, you did not specify --fmt2 but tblout file appears to have been created with --fmt 2, retry with --fmt2\nproblematic line:\n$line\n&quot;</span>; </span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR unable to parse, problematic line:\n$line\n&quot;</span>;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(($do_minscore) &amp;&amp; ($score &lt; $minscore)) &#123; </span><br><span class="line">      ; <span class="comment"># skip</span></span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">elsif</span>(($do_maxevalue) &amp;&amp; ($evalue &gt; $maxevalue)) &#123; </span><br><span class="line">      ; <span class="comment"># skip</span></span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">else</span> &#123; </span><br><span class="line">      <span class="keyword">my</span> $attributes = <span class="string">&quot;evalue=&quot;</span> . $evalue; <span class="comment"># default to just evalue</span></span><br><span class="line">      <span class="keyword">if</span>($do_all_attributes) &#123; </span><br><span class="line">        <span class="keyword">if</span>($do_fmt2) &#123; </span><br><span class="line">          $attributes .= <span class="keyword">sprintf</span>(<span class="string">&quot;;idx=$idx;seqaccn=$seqaccn;mdlaccn=$mdlaccn;clan=$clan;mdl=$mdl;mdlfrom=$mdlfrom;mdlto=$mdlto;trunc=$trunc;pass=$pass;gc=$gc;bias=$bias;inc=$inc;olp=$olp;anyidx=$anyidx;anyfrct1=$anyfrct1;anyfrct2=$anyfrct2;winidx=$winidx;winfrct1=$winfrct1;winfrct2=$winfrct2;%s$desc&quot;</span>, ($do_hidedesc ? <span class="string">&quot;&quot;</span> : <span class="string">&quot;desc=&quot;</span>));</span><br><span class="line">        &#125;</span><br><span class="line">        <span class="keyword">else</span> &#123; </span><br><span class="line">          $attributes .= <span class="keyword">sprintf</span>(<span class="string">&quot;;seqaccn=$seqaccn;mdlaccn=$mdlaccn;mdl=$mdl;mdlfrom=$mdlfrom;mdlto=$mdlto;trunc=$trunc;pass=$pass;gc=$gc;bias=$bias;inc=$inc;%s$desc&quot;</span>, ($do_hidedesc ? <span class="string">&quot;&quot;</span> : <span class="string">&quot;desc=&quot;</span>));</span><br><span class="line">        &#125;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">elsif</span>($do_no_attributes) &#123; </span><br><span class="line">        $attributes = <span class="string">&quot;-&quot;</span>;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">elsif</span>($do_de_attributes) &#123; </span><br><span class="line">        $attributes = $desc;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">if</span>(<span class="keyword">defined</span> $extra) &#123; </span><br><span class="line">        <span class="keyword">if</span>($attributes eq <span class="string">&quot;-&quot;</span>)       &#123; $attributes = <span class="string">&quot;&quot;</span>; &#125;</span><br><span class="line">        <span class="keyword">elsif</span>($attributes !~ <span class="regexp">m/\;$/</span>) &#123; $attributes .= <span class="string">&quot;;&quot;</span>; &#125;</span><br><span class="line">        $attributes .= $extra . <span class="string">&quot;;&quot;</span>;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">printf</span>(<span class="string">&quot;%s\t%s\t%s\t%d\t%d\t%.1f\t%s\t%s\t%s\n&quot;</span>, </span><br><span class="line">             $seqname,                             <span class="comment"># token 1: &#x27;sequence&#x27; (sequence name)</span></span><br><span class="line">             $source,                              <span class="comment"># token 2: &#x27;source&#x27;</span></span><br><span class="line">             $mdlname,                             <span class="comment"># token 3: &#x27;feature&#x27; (model name) you may want to change this to &#x27;ncRNA&#x27;</span></span><br><span class="line">             ($strand eq <span class="string">&quot;+&quot;</span>) ? $seqfrom : $seqto, <span class="comment"># token 4: &#x27;start&#x27; in coordinate space [1..seqlen], must be &lt;= &#x27;end&#x27;</span></span><br><span class="line">             ($strand eq <span class="string">&quot;+&quot;</span>) ? $seqto : $seqfrom, <span class="comment"># token 5: &#x27;end&#x27; in coordinate space [1..seqlen], must be &gt;= &#x27;start&#x27;</span></span><br><span class="line">             $score,                               <span class="comment"># token 6: &#x27;score&#x27; bit score</span></span><br><span class="line">             $strand,                              <span class="comment"># token 7: &#x27;strand&#x27; (&#x27;+&#x27; or &#x27;-&#x27;)</span></span><br><span class="line">             <span class="string">&quot;.&quot;</span>,                                  <span class="comment"># token 8: &#x27;phase&#x27; irrelevant for noncoding RNAs</span></span><br><span class="line">             $attributes);                         <span class="comment"># token 9: attributes, currently only E-value, unless --all, --none or --desc</span></span><br><span class="line">    &#125;</span><br><span class="line">  &#125;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>
-
-<ol start="3">
-<li>运行<br><code>perl infernal-tblout2gff.pl --cmscan --fmt2 genome.tblout &gt;genome.infernal.ncRNA.gff3</code></li>
+<ul>
+<li>Illumina PE测序是双端测序，测序获得的reads常见的长度是150bp。一对reads的联合体的长度被称为插入尺寸（insert size）。插入尺寸（insert size）常在350bp左右，但具体的每对测序reads都不一样，要通过把reads进行mapping到参考序列上，才能测量insert size。</li>
+<li>如果是repeat pairs的长度小于测序reads的insert size长度，可以构建两种构象（参考构象和重组构象），把reads mapping到两种构象来判断repeat pairs是否介导重组（如果两种构象都有mapped reads则是repeat pair介导重组的证据），并且通过mapped reads的计数来计算重组频率（recombination frequency）。</li>
+</ul>
+<h1 id="2-鉴定重复"><a href="#2-鉴定重复" class="headerlink" title="2. 鉴定重复"></a>2. 鉴定重复</h1><h2 id="2-1-鉴定重复的脚本"><a href="#2-1-鉴定重复的脚本" class="headerlink" title="2.1. 鉴定重复的脚本"></a>2.1. 鉴定重复的脚本</h2><p>这篇文章Repeats of Unusual Size in Plant Mitochondrial Genomes: Identification, Incidence and Evolution： <a href="https://academic.oup.com/g3journal/article/9/2/549/6026745">https://academic.oup.com/g3journal/article/9/2/549/6026745</a> 总结了植物线粒体基因组的重复序列的特征。研究表明，植物线粒体基因组比动物的要大，包含大量的非编码DNA，突变率低，重排率高。</p>
+<ul>
+<li>文章提供了python脚本ROUSFinder.py，用在鉴定线粒体的重复序列上。</li>
+<li>脚本是python2写的，依赖主要有blastn。</li>
+<li>有以下三个版本。</li>
+</ul>
+<ol>
+<li>ROUSFinder.py</li>
 </ol>
-<p>输入是Infernal的table格式输出文件，输出是gff3格式的ncRNA注释文件。</p>
-<hr>
 <ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+<li>调用blastn进行一条序列与自身的成对比对。</li>
+<li>默认参数是最小重复50bp，E value 10,000， match的赏分是+1，mismatch的罚分是-20。</li>
+<li>脚本贴在下面：</li>
+</ul>
+<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># January 16, 2018 version 1.1  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># Percent identity is limited to &gt;=99%, to allow for sequencing errors of &lt;1%.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=24&#x27;</span>, default=<span class="string">&#x27;24&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp&quot; &gt; &#x27;</span>+temprepeats)  </span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rt.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumRepeats\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>MultipleRepeats.py</li>
+</ol>
+<ul>
+<li>MultipleRepeats.py批量运行一个文件夹下的每个序列文件。</li>
+<li>脚本贴在下面：</li>
+</ul>
+<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Usage: -din directory of files to find repeats in  </span></span><br><span class="line"><span class="comment">#        -word word_size  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a directory of fasta sequence files&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-din&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;din&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta directory&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-word&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;word&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Word size for blast&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">din = results.din  </span><br><span class="line">word = results.word  </span><br><span class="line">  </span><br><span class="line">li = os.listdir(din)  </span><br><span class="line">inputs = <span class="built_in">filter</span>(<span class="keyword">lambda</span> x: <span class="string">&#x27;.fasta&#x27;</span> <span class="keyword">in</span> x, li)  </span><br><span class="line">inputs.sort()  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(inputs)):  </span><br><span class="line">    infile = <span class="built_in">str</span>(inputs[i])  </span><br><span class="line">    os.system(<span class="string">&quot;/home/alan/applications/ROUSFinder.py -m &quot;</span>+word+<span class="string">&quot; &quot;</span>+din+infile) </span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>ROUSFinder2.py</li>
+</ol>
+<ul>
+<li>ROUSFinder2.py可以在命令行设置match赏分和mismatch罚分。</li>
+<li>脚本贴在下面：</li>
+</ul>
+<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Version 2.0, November 21, 2018  </span></span><br><span class="line"><span class="comment"># Changes: uses variable parameters  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line"><span class="comment">#      -rew reward for match (default is 1)  </span></span><br><span class="line"><span class="comment">#      -pen penalty for mismatch (default is 20)  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=50&#x27;</span>, default=<span class="string">&#x27;50&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-rew&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;reward&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Reward for match&#x27;</span>, default=<span class="string">&#x27;1&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-pen&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;penalty&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Penalty for mismatch&#x27;</span>, default=<span class="string">&#x27;20&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">reward = results.reward  </span><br><span class="line">penalty = results.penalty  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname      </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10  -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp&quot; &gt; &#x27;</span>+temprepeats)  </span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rt.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumROUS\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
+
+<h2 id="2-2-运行脚本鉴定重复"><a href="#2-2-运行脚本鉴定重复" class="headerlink" title="2.2. 运行脚本鉴定重复"></a>2.2. 运行脚本鉴定重复</h2><p>选择ROUSFinder2.py脚本来鉴定线粒体的重复序列。</p>
+<ol>
+<li>脚本参数</li>
+</ol>
+<ul>
+<li>必需参数：fasta格式的输入序列</li>
+<li>-o out：输出文件名称</li>
+<li>-m 50：鉴定的重复序列的最小长度，默认是50bp。</li>
+<li>-b &#x2F;path&#x2F;to&#x2F;blastn&#x2F;：blastn命令的路径，默认是&#x2F;usr&#x2F;bin&#x2F;。</li>
+<li>-k：保留临时文件，out_tempblast.txt和out_temprepeats.txt</li>
+<li>-gb：生成重复序列的genbank格式文件，out_repeats.gb.txt</li>
+<li>-rew 1：match赏分，默认是+1</li>
+<li>-pen 20：mismatch罚分，默认是-20</li>
+</ul>
+<ol start="2">
+<li>运行脚本</li>
+</ol>
+<ul>
+<li><code>ROUSFinder2.py input.fa -m 30 -gb -o out</code></li>
+</ul>
+<h2 id="2-3-结果文件"><a href="#2-3-结果文件" class="headerlink" title="2.3. 结果文件"></a>2.3. 结果文件</h2><ol>
+<li>out_rep_table.txt：包含每个repeat单元的名称，长度，起始位置和终止位置，正负链（plus或minus）。</li>
+</ol>
+<ul>
+<li>第一行显示了第一个染色体的名称和长度。</li>
+<li>后面的行有五列，分别是repeat名称，repeat长度，repeat在染色体序列上的起始位置和终止位置，repeat的正负链方向。</li>
+<li>通常前面只有一行信息的重复单元（如Repeat_1和Repeat_2）需要手动去除，留下后面有2个或以上重复单元的信息（如Repeat_3和Repeat_4）作为重复序列鉴定的结果。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr01	147501</span><br><span class="line">Repeat_1	2702	1	2702	plus</span><br><span class="line">Repeat_2	2480	1	2480	plus</span><br><span class="line">Repeat_3	1171	1933	3103	plus</span><br><span class="line">Repeat_3	1171	2322	3492	plus</span><br><span class="line">Repeat_4	938	1696	2633	plus</span><br><span class="line">Repeat_4	938	2237	3174	plus</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>out_rep.fasta：包含fasta格式的repeat序列</li>
+<li>out_rep_counts.txt：包含repeat的总数（NumROUS），平均长度（AvgSize），平均数量（AvgCopyNum），和每个repeat的长度和数量。</li>
+<li>out_repeats.gb.txt：使用-gb参数则生成此文件，是genbank格式的repeat的注释文件。</li>
+<li>out_binned.txt：包含&gt;50bp,100bp,150bp,…,1000bp,1250bp,1500bp,…,10000bp的repeat的数量</li>
+<li>out_tempblast.txt：使用-k参数会保留两个临时temp文件</li>
+<li>out_temprepeats.txt：使用-k参数会保留两个临时temp文件。</li>
+</ol>
+<ul>
+<li>out_temprepeats.txt是用重复单元与输入序列做blastn的-outfmt 10的结果，包含qseqid length sstart send sstrand qcovhsp六列内容。</li>
+<li>out_rep_table.txt结果文件是从这个out_temprepeats.txt临时文件中提取生成的。</li>
+</ul>
+<h2 id="2-4-修改鉴定重复的脚本，使其显示重复序列所在染色体位置"><a href="#2-4-修改鉴定重复的脚本，使其显示重复序列所在染色体位置" class="headerlink" title="2.4. 修改鉴定重复的脚本，使其显示重复序列所在染色体位置"></a>2.4. 修改鉴定重复的脚本，使其显示重复序列所在染色体位置</h2><p>ROUSFinder2.py脚本鉴定线粒体的重复序列是被设计的用于鉴定一条序列与自身的重复，所以结果文件中不包含重复所在染色体位置信息。好在脚本容易修改，就修改了脚本使其显示染色体位置，可用于多染色体线粒体基因组的重复序列鉴定。</p>
+<p>使用修改后脚本的输出结果中两个文件（out_rep_table.txt和out_temprepeats.txt）有所不同，其他结果文件都与修改前的脚本的结果一致。</p>
+<ol>
+<li>修改后的脚本ROUSFinder2.0_adjusted.by.yz.py</li>
+</ol>
+<ul>
+<li>ROUSFinder2.0_adjusted.by.yz.py可以在结果文件out_rep_table.txt中显示染色体位置信息。</li>
+<li>修改后脚本保存在：<a href="https://github.com/yanzhongsino/bioscripts/blob/main/modified_scripts/ROUSFinder2.0_adjusted.by.yz.py">https://github.com/yanzhongsino/bioscripts/blob/main/modified_scripts/ROUSFinder2.0_adjusted.by.yz.py</a></li>
+<li>修改后脚本也贴在下面：</li>
+</ul>
+<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="comment">#! /usr/bin/env python  </span></span><br><span class="line"><span class="keyword">import</span> sys, math, os, argparse, csv  </span><br><span class="line">csv.field_size_limit(sys.maxsize)  </span><br><span class="line"></span><br><span class="line"><span class="comment"># minior adjustments by Yan Zhong, November 27, 2023  </span></span><br><span class="line"><span class="comment"># Version 2.0, November 21, 2018  </span></span><br><span class="line"><span class="comment"># Changes: uses variable parameters  </span></span><br><span class="line"><span class="comment"># Find dispersed repeated sequences in genomes.   </span></span><br><span class="line"><span class="comment"># Designed for plant mitochondrial genomes of up to a few Mbp.  </span></span><br><span class="line"><span class="comment"># May be very slow with larger genomes.   </span></span><br><span class="line"><span class="comment"># Blast can also sometimes give odd results with large or highly repetetive genomes.  </span></span><br><span class="line"><span class="comment"># Gaps, or runs of &#x27;N&#x27;s in the sequence will definitely give weird results.   </span></span><br><span class="line"><span class="comment"># The program assumes there aren&#x27;t any, and that the longest repeat will be the full sequence to itself.  </span></span><br><span class="line"><span class="comment"># If there are long repeats in the output that are listed as being only at one location, this is probably what happened.  </span></span><br><span class="line"><span class="comment"># If there are a lot of repeats within repeats the results can also be odd.  </span></span><br><span class="line"><span class="comment"># Copyright Alan C. Christensen, University of Nebraska, 2018  </span></span><br><span class="line"><span class="comment"># No guarantees, warranties, support, or anything else is implicit or explicit.  </span></span><br><span class="line"><span class="comment"># Input is a fasta format file of a sequence. Genbank format works but generates lots of error messages in stdout.  </span></span><br><span class="line"><span class="comment"># Output is a list of unique, ungapped repeated sequences, fasta formatted.  </span></span><br><span class="line"><span class="comment"># The names are in the format &#x27;&gt;Repeat/ROUS_name_start_end_length&#x27;.  </span></span><br><span class="line"><span class="comment"># A table of repeats with the coordinates of each one is generated.  </span></span><br><span class="line"><span class="comment"># A list of repeat name, length and copy number is generated.  </span></span><br><span class="line"><span class="comment"># A binned table of the total number of repeats in size ranges is generated.  </span></span><br><span class="line"><span class="comment">#  </span></span><br><span class="line"><span class="comment"># PARAMETERS  </span></span><br><span class="line"><span class="comment">#   REQUIRED:  </span></span><br><span class="line"><span class="comment">#      input file in fasta format  </span></span><br><span class="line"><span class="comment">#   Optional  </span></span><br><span class="line"><span class="comment">#      -o output file name  </span></span><br><span class="line"><span class="comment">#      -m minimum length of exact matches to keep  </span></span><br><span class="line"><span class="comment">#      -b path to blastn (default is /usr/bin/)  </span></span><br><span class="line"><span class="comment">#      -k keep temp files  </span></span><br><span class="line"><span class="comment">#      -gb to write the repeats to a genbank format file  </span></span><br><span class="line"><span class="comment">#      -rew reward for match (default is 1)  </span></span><br><span class="line"><span class="comment">#      -pen penalty for mismatch (default is 20)  </span></span><br><span class="line">  </span><br><span class="line">parser = argparse.ArgumentParser(description=<span class="string">&#x27;Find repeats in a fasta sequence file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;infile&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Input .fasta file&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-o&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;outfile&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Output file name seed, default is input_repeats&#x27;</span>, default=<span class="string">&#x27;default&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-m&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;minlen&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Minimum length of matches to keep, default=50&#x27;</span>, default=<span class="string">&#x27;50&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-b&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;blast_path&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Path to blastn program, default is /usr/bin/&#x27;</span>, default=<span class="string">&#x27;/usr/bin/&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-k&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;keep&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to keep temp files&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-gb&#x27;</span>, action=<span class="string">&#x27;store_true&#x27;</span>, dest=<span class="string">&#x27;genbank&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;True to write GenBank format file&#x27;</span>, default=<span class="literal">False</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-rew&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;reward&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Reward for match&#x27;</span>, default=<span class="string">&#x27;1&#x27;</span>)  </span><br><span class="line">parser.add_argument(<span class="string">&#x27;-pen&#x27;</span>, action=<span class="string">&#x27;store&#x27;</span>, dest=<span class="string">&#x27;penalty&#x27;</span>, <span class="built_in">help</span>=<span class="string">&#x27;Penalty for mismatch&#x27;</span>, default=<span class="string">&#x27;20&#x27;</span>)  </span><br><span class="line">results = parser.parse_args()  </span><br><span class="line">infile = results.infile  </span><br><span class="line">outfile = results.outfile  </span><br><span class="line">minlen = <span class="built_in">int</span>(results.minlen)  </span><br><span class="line">blast_path = results.blast_path  </span><br><span class="line">keep = results.keep  </span><br><span class="line">genbank = results.genbank  </span><br><span class="line">reward = results.reward  </span><br><span class="line">penalty = results.penalty  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># It might be useful to define the wordsize as something less than minlen, so both variables are used.  </span></span><br><span class="line"><span class="comment"># Wordsize smaller than minlen would give smaller core identical sequences in the middle of repeats.  </span></span><br><span class="line"><span class="comment"># An example might be to change this to wordsize = str(int(minlen/2)).  </span></span><br><span class="line">wordsize = <span class="built_in">str</span>(minlen)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># If no output file seed is specified, make one by stripping leading directory information  </span></span><br><span class="line"><span class="comment"># and stripping trailing .fa or .fasta from the input file name and using that.  </span></span><br><span class="line"><span class="keyword">if</span> outfile == <span class="string">&#x27;default&#x27;</span>:  </span><br><span class="line">    outfile = infile  </span><br><span class="line">    <span class="keyword">if</span> outfile.count(<span class="string">&#x27;/&#x27;</span>) &gt; <span class="number">0</span>:  </span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(outfile.count(<span class="string">&#x27;/&#x27;</span>)):  </span><br><span class="line">            index = outfile.index(<span class="string">&#x27;/&#x27;</span>)  </span><br><span class="line">            outfile = outfile[index+<span class="number">1</span>:]  </span><br><span class="line">    <span class="keyword">if</span> outfile.endswith(<span class="string">&#x27;.fa&#x27;</span>) <span class="keyword">or</span> outfile.endswith(<span class="string">&#x27;.fasta&#x27;</span>):  </span><br><span class="line">        outfile = outfile.rstrip(<span class="string">&#x27;fasta&#x27;</span>)  </span><br><span class="line">    outfile = outfile.rstrip(<span class="string">&#x27;.&#x27;</span>)  </span><br><span class="line">outfa = outfile+<span class="string">&#x27;_rep.fasta&#x27;</span>  </span><br><span class="line">outtab = outfile+<span class="string">&#x27;_rep_table.txt&#x27;</span>  </span><br><span class="line">outbin = outfile+<span class="string">&#x27;_binned.txt&#x27;</span>  </span><br><span class="line">outcount = outfile+<span class="string">&#x27;_rep_counts.txt&#x27;</span>  </span><br><span class="line">outgb = outfile+<span class="string">&#x27;_repeats.gb.txt&#x27;</span>  </span><br><span class="line">tempblast = outfile+<span class="string">&#x27;_tempblast.txt&#x27;</span>  </span><br><span class="line">temprepeats = outfile+<span class="string">&#x27;_temprepeats.txt&#x27;</span>  </span><br><span class="line">tempparse = outfile+<span class="string">&#x27;_sequence_parsing.txt&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Get sequence name and length from fasta file.  </span></span><br><span class="line">seq = <span class="built_in">open</span>(infile, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">seqname = seq.readline()  </span><br><span class="line">seqname = seqname.lstrip(<span class="string">&#x27;&gt; &#x27;</span>)  </span><br><span class="line">seqname = seqname.rstrip()  </span><br><span class="line">seqlen = <span class="number">0</span>   </span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> seq:  </span><br><span class="line">    <span class="keyword">if</span>(line[<span class="number">0</span>] == <span class="string">&quot;&gt;&quot;</span>):  </span><br><span class="line">        <span class="keyword">continue</span>  </span><br><span class="line">    seqlen += <span class="built_in">len</span>(line.strip())  </span><br><span class="line">seq.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># run blastn with query file plus strand (removing first line which is full length sequence), minus strand, and concatenate  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Performing self-blastn comparison with &#x27;</span>+seqname      </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand plus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10  -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; | tail -n+2 &gt; tempblast1.txt&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+infile+<span class="string">&#x27; -strand minus -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward &#x27;</span>+reward+<span class="string">&#x27; -penalty -&#x27;</span>+penalty+<span class="string">&#x27; -ungapped -dust no -soft_masking false -evalue 10 -outfmt &quot;10 qstart qend length sstart send mismatch sstrand qseq&quot; &gt; tempblast2.txt&#x27;</span>)  </span><br><span class="line">os.system(<span class="string">&#x27;cat tempblast1.txt tempblast2.txt &gt; &#x27;</span>+tempblast)  </span><br><span class="line">os.system(<span class="string">&#x27;rm tempblast1.txt tempblast2.txt&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># open tempblast.txt, convert to list of lists, and sort by length and position descending  </span></span><br><span class="line"><span class="comment"># This is necessary because blastn does not output every possible pair of hits when there are more than 2 copies of a repeat  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Sorting alignments...&#x27;</span>  </span><br><span class="line">f = <span class="built_in">open</span>(tempblast, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(f)  </span><br><span class="line">alignments = <span class="built_in">list</span>(reader)  </span><br><span class="line">f.close()  </span><br><span class="line">alignments = <span class="built_in">sorted</span>(alignments, key=<span class="keyword">lambda</span> x: (-<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">2</span>]), -<span class="number">1</span>*<span class="built_in">int</span>(x[<span class="number">0</span>])))  </span><br><span class="line">alignments.append([<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;1&#x27;</span>,<span class="string">&#x27;0&#x27;</span>,<span class="string">&#x27;A&#x27;</span>,<span class="string">&#x27;X&#x27;</span>])  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># New list of uniques  </span></span><br><span class="line"><span class="comment"># Text file &#x27;_sequence_parsing.txt&#x27; includes the information on how duplicates were found.  </span></span><br><span class="line"><span class="comment"># Start at row 0. Compare to subsequent rows.   </span></span><br><span class="line"><span class="comment"># If repeat length is different from the next row, it has passed all the tests, write it to the file.  </span></span><br><span class="line"><span class="comment"># If query or subject coordinates are the same as the query or subject or reversed coordinates  </span></span><br><span class="line"><span class="comment"># of a subsequent row, it is not unique, so go to the next row and do the comparisons again.  </span></span><br><span class="line"><span class="comment"># Thanks to Alex Kozik for repeatedly testing and finding bugs in the algorithm.  </span></span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Finding unique repeats...&#x27;</span>  </span><br><span class="line">uniques = []  </span><br><span class="line">sp = <span class="built_in">open</span>(tempparse, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(alignments)):  </span><br><span class="line">    sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(alignments[row][<span class="number">2</span>]) &lt; minlen:  </span><br><span class="line">        <span class="comment"># This won&#x27;t happen unless the word_size is defined as something other than minlen.  </span></span><br><span class="line">        <span class="comment"># That could be useful under some circumstances.  </span></span><br><span class="line">        sp.write(<span class="string">&#x27;row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is less than minlength&#x27;</span>)  </span><br><span class="line">        <span class="keyword">break</span>  </span><br><span class="line">    <span class="keyword">else</span>:  </span><br><span class="line">      </span><br><span class="line">        <span class="keyword">for</span> compare <span class="keyword">in</span> <span class="built_in">range</span>(row+<span class="number">1</span>,<span class="built_in">len</span>(alignments)):  </span><br><span class="line">            <span class="keyword">if</span> alignments[row][<span class="number">2</span>] != alignments[compare][<span class="number">2</span>]:   </span><br><span class="line">                uniques.append(alignments[row])  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tadding row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; to unique list\n&#x27;</span>)  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">            <span class="keyword">else</span>:  </span><br><span class="line">                sp.write(<span class="string">&#x27;\tcomparing to &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">      </span><br><span class="line">                <span class="keyword">if</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; and &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27; are the same\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">0</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">1</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tqstart and qend of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">0</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">1</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qstart and qend of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">1</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">0</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as qend and qstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">3</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">4</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as sstart and send of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">elif</span> alignments[row][<span class="number">3</span>] == alignments[compare][<span class="number">4</span>] <span class="keyword">and</span> alignments[row][<span class="number">4</span>] == alignments[compare][<span class="number">3</span>]:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\tsstart and send of row &#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is the same as send and sstart of &#x27;</span>+<span class="built_in">str</span>(compare)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">                    <span class="keyword">break</span>  </span><br><span class="line">                <span class="keyword">else</span>:  </span><br><span class="line">                    sp.write(<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(row)+<span class="string">&#x27; is different\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">sp.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write uniques into output file  </span></span><br><span class="line"><span class="comment"># Start list for copy number table  </span></span><br><span class="line">rous_count = <span class="number">0</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">repcopies = []  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(uniques)):  </span><br><span class="line">    qstart = uniques[i][<span class="number">0</span>]  </span><br><span class="line">    qend = uniques[i][<span class="number">1</span>]  </span><br><span class="line">    length = uniques[i][<span class="number">2</span>]  </span><br><span class="line">    seq = uniques[i][<span class="number">7</span>]  </span><br><span class="line">      </span><br><span class="line">    rous_count += <span class="number">1</span>  </span><br><span class="line">    g.write(<span class="string">&#x27;&gt;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count)+<span class="string">&#x27;\n&#x27;</span>+seq+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">    repcopies.append([<span class="string">&#x27;Repeat_&#x27;</span>+<span class="built_in">str</span>(rous_count),length])  </span><br><span class="line">          </span><br><span class="line"><span class="keyword">if</span> rous_count == <span class="number">0</span>:  </span><br><span class="line">    <span class="built_in">print</span> <span class="string">&quot;\tRepeats of unusual size? I don&#x27;t think they exist&quot;</span>  </span><br><span class="line">g.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&#x27;Repeat fasta file is done, as you wish.&#x27;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Now find each copy of each repeat. Again, this is because the blastn output file does not have every possible alignment.  </span></span><br><span class="line"><span class="comment"># It is also because the information on locations and strand is not organized well in the blastn output.  </span></span><br><span class="line"><span class="comment"># In addition, this subroutine eliminates duplicates of nested repeats.  </span></span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Finding all copies of repeats...&quot;</span>  </span><br><span class="line">g = <span class="built_in">open</span>(outfa, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">os.system(blast_path+<span class="string">&#x27;blastn -query &#x27;</span>+outfa+<span class="string">&#x27; -strand both -subject &#x27;</span>+infile+<span class="string">&#x27; -word_size &#x27;</span>+wordsize+<span class="string">&#x27; -reward 1 -penalty -20 -ungapped -dust no -soft_masking false -evalue 1000 -outfmt &quot;10 qseqid length sstart send sstrand qcovhsp sseqid qstart qend&quot; &gt; &#x27;</span>+temprepeats)  <span class="comment"># adjusted by yz</span></span><br><span class="line">g.close()  </span><br><span class="line">  </span><br><span class="line">tempr = <span class="built_in">open</span>(temprepeats, <span class="string">&#x27;r&#x27;</span>)  </span><br><span class="line">reader = csv.reader(tempr)  </span><br><span class="line">replist = <span class="built_in">list</span>(reader)  </span><br><span class="line">tempr.close()  </span><br><span class="line">  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Making a table of the repeats...&quot;</span>  </span><br><span class="line">sum_rep_len = <span class="number">0</span>  </span><br><span class="line">bin_dict = &#123;&#125;  </span><br><span class="line">binned = [seqname,seqlen,<span class="number">0</span>]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># defining the bins  </span></span><br><span class="line">i = <span class="number">0</span>  </span><br><span class="line">j = <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt; <span class="number">1000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i += <span class="number">1</span>  </span><br><span class="line">    j += <span class="number">50</span>  </span><br><span class="line"><span class="keyword">while</span> j &lt;= <span class="number">10000</span>:  </span><br><span class="line">    bin_dict[i] = j  </span><br><span class="line">    binned.append(<span class="number">0</span>)  </span><br><span class="line">    i +=<span class="number">1</span>  </span><br><span class="line">    j += <span class="number">250</span>  </span><br><span class="line">      </span><br><span class="line"><span class="comment"># make list for entire sequence, set each position as 0  </span></span><br><span class="line">posit = []  </span><br><span class="line"><span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(seqlen):  </span><br><span class="line">    posit.append(<span class="number">0</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Thanks to Emily Wynn for suggesting qcovhsp for this loop.  </span></span><br><span class="line"><span class="comment"># if qcovhsp is &gt;98%, write to the file  </span></span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, start, end, strand to outtab  </span></span><br><span class="line"><span class="comment"># make list for genbank file  </span></span><br><span class="line"><span class="comment"># Keep stats on lengths  </span></span><br><span class="line">rt = <span class="built_in">open</span>(outtab, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line"><span class="comment"># rt.write(seqname+&#x27;\t&#x27;+str(seqlen)+&#x27;\n&#x27;) # adjusted by yz</span></span><br><span class="line">rt.write(<span class="string">&#x27;repeat.name&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;alignment.length&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;repeat.start&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;repeat.end&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;strand&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;qcovhsp&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.name&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.start&#x27;</span>+<span class="string">&#x27;\t&#x27;</span>+<span class="string">&#x27;chr.end&#x27;</span>+<span class="string">&#x27;\n&#x27;</span>)  <span class="comment"># adjusted by yz</span></span><br><span class="line">templist = []  </span><br><span class="line">gblist =[]  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># look at each repeat in turn  </span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(replist)):  </span><br><span class="line">    <span class="comment"># if repeat is good (&gt;98% identical to another one), write it to the file, and put the name in a list  </span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">5</span>])&gt;<span class="number">98</span>:  </span><br><span class="line">        rt.write(<span class="built_in">str</span>(replist[i][<span class="number">0</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">1</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">7</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">8</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">4</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">5</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">6</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">2</span>])+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(replist[i][<span class="number">3</span>])+<span class="string">&#x27;\n&#x27;</span>)  <span class="comment"># adjusted by yz</span></span><br><span class="line">        <span class="keyword">if</span> replist[i][<span class="number">4</span>] == <span class="string">&#x27;minus&#x27;</span>:  </span><br><span class="line">            location = <span class="string">&#x27;complement(&#x27;</span>+replist[i][<span class="number">3</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">2</span>]+<span class="string">&#x27;)&#x27;</span>  </span><br><span class="line">        <span class="keyword">else</span>:  </span><br><span class="line">            location = replist[i][<span class="number">2</span>]+<span class="string">&#x27;..&#x27;</span>+replist[i][<span class="number">3</span>]  </span><br><span class="line">        gblist.append(<span class="string">&#x27;     repeat_region   &#x27;</span>+location+<span class="string">&#x27;\n                     /rpt_type=dispersed\n                     /label=&#x27;</span>+replist[i][<span class="number">0</span>]+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">        templist.append(replist[i][<span class="number">0</span>])  </span><br><span class="line">        <span class="comment"># then write 1&#x27;s at every position in the sequence covered by that repeat  </span></span><br><span class="line">        <span class="comment"># these can then be summed to get total bases of repeats  </span></span><br><span class="line">        <span class="comment"># bases in overlapping repeats are only counted once  </span></span><br><span class="line">        <span class="keyword">for</span> n <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(replist[i][<span class="number">2</span>]), <span class="built_in">int</span>(replist[i][<span class="number">3</span>])):  </span><br><span class="line">            posit[n] = <span class="number">1</span>  </span><br><span class="line">        <span class="comment"># then scan through bin sizes and if a repeat is greater than the  </span></span><br><span class="line">        <span class="comment"># bin_dict size cutoff, add one to the bin  </span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)-<span class="number">4</span>, -<span class="number">1</span>, -<span class="number">1</span>):  </span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">int</span>(replist[i][<span class="number">1</span>]) &gt;= bin_dict[j]:  </span><br><span class="line">                binned[j+<span class="number">3</span>] +=<span class="number">1</span>  </span><br><span class="line">                <span class="keyword">break</span>  </span><br><span class="line">sum_rep_len = posit.count(<span class="number">1</span>)  </span><br><span class="line">binned[<span class="number">2</span>] = sum_rep_len  </span><br><span class="line">rt.close()  </span><br><span class="line"><span class="keyword">if</span> genbank == <span class="literal">True</span>:  </span><br><span class="line">    gb = <span class="built_in">open</span>(outgb, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(gblist)):  </span><br><span class="line">        gb.write(gblist[i])  </span><br><span class="line">    gb.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># write tab separated values of repeat name, length, copy number to outcount  </span></span><br><span class="line"><span class="comment"># first two lines are also a table of stats on repeats  </span></span><br><span class="line">rc = <span class="built_in">open</span>(outcount,<span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">rc.write(<span class="string">&#x27;Sequence\tGenome_size\tNumROUS\tAvgSize\tAvgCopyNum\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">numrous = <span class="number">0</span>  </span><br><span class="line">sizerous = <span class="number">0</span>  </span><br><span class="line">copyrous = <span class="number">0</span>  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    repname = repcopies[i][<span class="number">0</span>]  </span><br><span class="line">    replen = <span class="built_in">float</span>(repcopies[i][<span class="number">1</span>])  </span><br><span class="line">    repcop = <span class="built_in">float</span>(templist.count(repname))  </span><br><span class="line">  </span><br><span class="line">    numrous += <span class="number">1</span>  </span><br><span class="line">    sizerous += replen  </span><br><span class="line">    copyrous += repcop  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">if</span> numrous == <span class="number">0</span>:  </span><br><span class="line">    avsizerous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line">    avcopyrous = <span class="string">&#x27;NA&#x27;</span>  </span><br><span class="line"><span class="keyword">else</span>:  </span><br><span class="line">    avsizerous = sizerous/numrous  </span><br><span class="line">    avcopyrous = copyrous/numrous  </span><br><span class="line">  </span><br><span class="line">  </span><br><span class="line">rc.write(seqname+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(seqlen)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(numrous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avsizerous)+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(avcopyrous)+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(repcopies)):  </span><br><span class="line">    rc.write(repcopies[i][<span class="number">0</span>]+<span class="string">&#x27;\t&#x27;</span>+repcopies[i][<span class="number">1</span>]+<span class="string">&#x27;\t&#x27;</span>+<span class="built_in">str</span>(templist.count(repcopies[i][<span class="number">0</span>]))+<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">  </span><br><span class="line">rc.close()  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Write binned table headers, then stats for this sequence.  </span></span><br><span class="line">binfile = <span class="built_in">open</span>(outbin, <span class="string">&#x27;w&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;Sequence\tSeq_len\tRep_len\t&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(bin_dict)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(bin_dict[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(binned)):  </span><br><span class="line">    binfile.write(<span class="built_in">str</span>(binned[i])+<span class="string">&#x27;\t&#x27;</span>)  </span><br><span class="line">binfile.write(<span class="string">&#x27;\n&#x27;</span>)  </span><br><span class="line">binfile.close()  </span><br><span class="line"><span class="built_in">print</span> <span class="string">&quot;Repeat tables are done, as you wish.&quot;</span>  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Removing temp files if necessary  </span></span><br><span class="line"><span class="keyword">if</span> keep == <span class="literal">False</span>:  </span><br><span class="line">    os.system(<span class="string">&#x27;rm &#x27;</span>+tempblast+<span class="string">&#x27; &#x27;</span>+temprepeats+<span class="string">&#x27; &#x27;</span>+tempparse)  </span><br><span class="line">  </span><br><span class="line"><span class="comment"># Rachael Schulte, William Goldman and Rob Reiner inspired this section of code  </span></span><br><span class="line">quote_dict = &#123;<span class="number">0</span>:<span class="string">&quot;48656c6c6f2e204d79206e616d6520697320496e69676f204d6f6e746f79612e20596f75206b696c6c6564206d79206661746865722e205072657061726520746f206469652e&quot;</span>, <span class="number">1</span>:<span class="string">&quot;5768656e20492077617320796f7572206167652c2074656c65766973696f6e207761732063616c6c656420626f6f6b732e&quot;</span>, <span class="number">2</span>:<span class="string">&quot;486176652066756e2073746f726d696e2720646120636173746c6521&quot;</span>, <span class="number">3</span>:<span class="string">&quot;4d79207761792773206e6f7420766572792073706f7274736d616e6c696b652e&quot;</span>, <span class="number">4</span>:<span class="string">&quot;596f75206b656570207573696e67207468617420776f72642e204920646f206e6f74207468696e6b206974206d65616e73207768617420796f75207468696e6b206974206d65616e732e&quot;</span>, <span class="number">5</span>:<span class="string">&quot;4d75726465726564206279207069726174657320697320676f6f642e&quot;</span>,<span class="number">6</span>:<span class="string">&quot;496e636f6e6365697661626c6521&quot;</span>, <span class="number">7</span>:<span class="string">&quot;5468657265277320612062696720646966666572656e6365206265747765656e206d6f73746c79206465616420616e6420616c6c20646561642e&quot;</span>, <span class="number">8</span>:<span class="string">&quot;596f7520727573682061206d697261636c65206d616e2c20796f752067657420726f7474656e206d697261636c65732e&quot;</span>, <span class="number">9</span>:<span class="string">&quot;476f6f64206e696768742c20576573746c65792e20476f6f6420776f726b2e20536c6565702077656c6c2e2049276c6c206d6f7374206c696b656c79206b696c6c20796f7520696e20746865206d6f726e696e672e&quot;</span>,<span class="number">10</span>:<span class="string">&quot;4e6f206d6f7265207268796d65732c2049206d65616e2069742120416e79626f64792077616e742061207065616e75743f&quot;</span>&#125;  </span><br><span class="line"><span class="keyword">import</span> random, binascii  </span><br><span class="line">z = random.randint(<span class="number">0</span>,<span class="number">10</span>)  </span><br><span class="line"><span class="built_in">print</span> binascii.unhexlify(quote_dict[z])+<span class="string">&#x27;\n&#x27;</span>  </span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>修改前脚本的结果文件out_rep_table.txt</li>
+</ol>
+<ul>
+<li>第一行显示了第一个染色体的名称和长度。</li>
+<li>后面的行有五列，分别是重复单元的名称(repeat.name)，重复单元的长度(alignment.length)，重复单元在输入的染色体序列上的起始位置和终止位置(chr.start和chr.end)，重复单元的正负链方向(strand)。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Chr01	147501</span><br><span class="line">Repeat_1	2702	1	2702	plus</span><br><span class="line">Repeat_2	2480	1	2480	plus</span><br><span class="line">Repeat_3	1171	1933	3103	plus</span><br><span class="line">Repeat_3	1171	2322	3492	plus</span><br><span class="line">Repeat_4	938	1696	2633	plus</span><br><span class="line">Repeat_4	938	2237	3174	plus</span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>修改后脚本的结果文件out_rep_table.txt</li>
+</ol>
+<ul>
+<li>添加了标题行。包含重复单元的名称(repeat.name)，重复单元的长度(alignment.length)，重复单元在重复序列上起始和终止位置(repeat.start和repeat.end)，重复单元的正负链方向(strand)，每个高度相似片段HSP中的重复单元的覆盖度(Query Coverage Per HSP, qcovhsp)，染色体序列名称(chr.name)，重复单元在输入的染色体序列上的起始位置和终止位置(chr.start和chr.end)。</li>
+<li>添加了列的展示，主要是染色体序列名称(chr.name)，还包括repeat.start和repeat.end，qcovhsp。</li>
+<li>调整了列的展示顺序。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">repeat.name	alignment.length	repeat.start	repeat.end	strand	qcovhsp	chr.name	chr.start	chr.end</span><br><span class="line">Repeat_1	2702	1	2702	plus	100	Chr38	1	2702</span><br><span class="line">Repeat_2	2480	1	2480	plus	100	Chr39	1	2480</span><br><span class="line">Repeat_3	1171	1	1171	plus	100	Chr34	1933	3103</span><br><span class="line">Repeat_3	1171	1	1171	plus	100	Chr09	2322	3492</span><br><span class="line">Repeat_4	938	1	938	plus	100	Chr38	1696	2633</span><br><span class="line">Repeat_4	938	1	938	plus	100	Chr32	2237	3174</span><br></pre></td></tr></table></figure>
+
+<ol start="4">
+<li>修改后脚本的结果文件out_temprepeats.txt</li>
+</ol>
+<ul>
+<li>修改后脚本的结果文件out_temprepeats.txt比起修改前在后面多加了三列信息chr.name,repeat.start和repeat.end。</li>
+</ul>
+<ol start="5">
+<li>notes（与脚本的修改无关）</li>
+</ol>
+<ul>
+<li>从修改后脚本的结果文件out_rep_table.txt的示例文件可看出，qcovhsp列绝大部分情况下是100，因为这个值高于98才会被鉴定为重复单元。</li>
+<li>repeat.start和repeat.end两列的值也一般是固定的，从1到重复单元的末位。这两列值是用重复序列与输入序列做blast，比对结果中比对到的重复序列的重复单元的位置，通常只有完全比对上（repeat.start和repeat.end两列值从1到重复单元的末位）的才被鉴定为重复单元。</li>
+</ul>
+<h2 id="2-5-重复的应用"><a href="#2-5-重复的应用" class="headerlink" title="2.5. 重复的应用"></a>2.5. 重复的应用</h2><ol>
+<li>辅助线粒体组装</li>
+</ol>
+<ul>
+<li>通过鉴定出来的重复，可以帮助线粒体组装得更完整。</li>
+<li>调整线粒体的已有组装。比如通过计算重组率，把主导的优势构象作为最后的线粒体组装构象。</li>
+</ul>
+<ol start="2">
+<li>鉴定重组</li>
+</ol>
+<ul>
+<li>重复序列可能介导重组，所以鉴定重复是鉴定重组的基础。</li>
+</ul>
+<h1 id="3-鉴定重组和计算重组率"><a href="#3-鉴定重组和计算重组率" class="headerlink" title="3. 鉴定重组和计算重组率"></a>3. 鉴定重组和计算重组率</h1><p>鉴定出线粒体的重复序列后，还可以进一步鉴定这些重复序列是否介导了重组。</p>
+<p>重组会导致构象的变化，构象变化的形式包括：</p>
+<ol>
+<li>位于不同染色体环上的一对重复序列可能介导重组，使得两个小环变成一个大环。</li>
+<li>位于同一个染色体环上的同方向的一对重复序列可能介导重组，使得一个大环变成两个小环。</li>
+<li>位于同一个染色体环上的反方向的一对重复序列，可能在其他重复对的重组变换下变成同方向或者两个小环的状态。</li>
+</ol>
+<h2 id="3-1-鉴定重组的步骤"><a href="#3-1-鉴定重组的步骤" class="headerlink" title="3.1. 鉴定重组的步骤"></a>3.1. 鉴定重组的步骤</h2><h3 id="3-1-1-构建参考构象和重组构象的序列"><a href="#3-1-1-构建参考构象和重组构象的序列" class="headerlink" title="3.1.1. 构建参考构象和重组构象的序列"></a>3.1.1. 构建参考构象和重组构象的序列</h3><ol>
+<li>针对一对可能造成重组的重复序列，构建参考构象和重组构象</li>
+<li>在参考构象和重组构象中，以重复序列加上两翼各300bp作为参考构象序列(ref_1和ref_2)和重组构象序列(rec_1和rec_2)</li>
+<li>构建参考序列和mapping注意事项</li>
+</ol>
+<ul>
+<li>可以把参考构象序列和重组构象序列（一共四条）一同作为参考序列（reference）用于mapping，避免同一条reads mapping到多个构象序列从而重复计数的情况。</li>
+<li>如果担心叶绿体派生的reads影响重组率的计算，还可以加上叶绿体基因组作为参考序列（reference），这样叶绿体的reads会被mapping到叶绿体上，从而起到过滤的作用。</li>
+<li>如果担心核基因派生的reads影响，可以用一个cutoff值(比如100bp)，小于100bp的mapping被筛除。</li>
+</ul>
+<h3 id="3-1-2-把reads往参考序列进行mapping"><a href="#3-1-2-把reads往参考序列进行mapping" class="headerlink" title="3.1.2. 把reads往参考序列进行mapping"></a>3.1.2. 把reads往参考序列进行mapping</h3><ol>
+<li>把reads往参考序列进行mapping，之后再根据mapping的reads的位置进行筛选，筛选出横跨repeat区域的有效mapping。</li>
+<li>比如repeat长度为220bp，两边各截取300bp，共820bp长度的参考序列。这里把跨越220bp的repeat，并且两边各至少映射上10bp的reads作为有效mapping。这意味着，mapping的起始位置需要小于290，终止位置需要大于530bp。</li>
+<li>bwa进行mapping后得到的bam文件的第4列代表read比对到的参考序列最左侧的位置坐标（未必对上第4列为0）；第9列代表pair read完全匹配到同一条参考序列时，两个read之间的长度。可以理解为insert size。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">a = 530 # 设置终止位置需要大于的值，这里是530bp</span><br><span class="line">bwa index reference.fa # 为参考序列建立索引</span><br><span class="line">bwa mem -t 4 reference.fa  sample_1.clean.fq sample_2.clean.fq | samtools sort -@ 4 -m 4G &gt; reference.bam # 映射reads到参考序列</span><br><span class="line">samtools view reference.bam |awk -F&quot;\t&quot; -v awka=&quot;$a&quot; &#x27;$4&lt;290 &amp;&amp; ($4+$9)&gt;awka &#123;print $0&#125;&#x27; &gt; reference.bam.filter # 筛选起始位置小于290，终止位置大于530bp的mapped reads。这里的`awk -F&quot;\t&quot;`参数设定列分隔符非常重要，已经坑过我两把了。</span><br></pre></td></tr></table></figure>
+
+<h3 id="3-1-3-计算重组率-recombination-rate"><a href="#3-1-3-计算重组率-recombination-rate" class="headerlink" title="3.1.3. 计算重组率(recombination rate)"></a>3.1.3. 计算重组率(recombination rate)</h3><ol>
+<li>把reads往参考序列进行mapping得到有效映射的比对文件reference.bam.filter后，计算不同参考序列的有效映射的reads数量（即数据行数）。</li>
+<li>重组构象的两条序列(rec_1和rec_2)的比对reads数量之和作为分子，重组构象的两条序列(rec_1和rec_2)的比对reads数量与参考构象的两条序列(ref_1和ref_2)的比对reads数量之和作为分母，两者的比值可作为重组率。</li>
+</ol>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">n_ref_1 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;ref_1&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列ref_1上有效映射reads的数量</span><br><span class="line">n_ref_2 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;ref_2&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列ref_2上有效映射reads的数量</span><br><span class="line">n_rec_1 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;rec_1&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列rec_1上有效映射reads的数量</span><br><span class="line">n_rec_2 = $(cat reference.bam.filter | awk &#x27;$3 == &quot;rec_2&quot; &#123;print $0&#125;&#x27; |wc -l) # 统计参考构象序列rec_2上有效映射reads的数量</span><br><span class="line">mapping_rate = $(($n_rec_1+$n_rec_2)/($n_ref_1+$n_ref_2+$n_rec_1+$n_rec_2)) # 计算重组率</span><br></pre></td></tr></table></figure>
+
+<h1 id="4-references"><a href="#4-references" class="headerlink" title="4. references"></a>4. references</h1><ol>
+<li>ROUSFinder.py脚本：<a href="https://academic.oup.com/g3journal/article/9/2/549/6026745">https://academic.oup.com/g3journal/article/9/2/549/6026745</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=30% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>omics</category>
+        <category>organelle</category>
+        <category>recombination</category>
+      </categories>
+      <tags>
+        <tag>mitogenome</tag>
+        <tag>repeat</tag>
+        <tag>recombination</tag>
+        <tag>rearrangement</tag>
+        <tag>insert size</tag>
+        <tag>recombination frequency</tag>
+        <tag>ROUSFinder</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>scripts</title>
+    <url>/2021/04/01/programming_scripts/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=476751845&auto=1&height=32"></iframe></div>
+
+<h1 id="1-我写的脚本"><a href="#1-我写的脚本" class="headerlink" title="1. 我写的脚本"></a>1. 我写的脚本</h1><h2 id="1-1-多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测"><a href="#1-1-多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测" class="headerlink" title="1.1. 多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测"></a>1.1. 多个RNA-seq数据，基于参考基因组的转录本组装和ORF预测</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/multi-transcriptome_assembly_with_ref.sh">multi-transcriptome_assembly_with_ref.sh</a></p>
+<h2 id="1-2-提取，合并，和比对单拷贝基因和蛋白"><a href="#1-2-提取，合并，和比对单拷贝基因和蛋白" class="headerlink" title="1.2. 提取，合并，和比对单拷贝基因和蛋白"></a>1.2. 提取，合并，和比对单拷贝基因和蛋白</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/extract_single_copy_genes.sh">extract_single_copy_genes.sh</a></p>
+<h2 id="1-3-合并不同物种数量的多序列比对文件，保持序列的比对"><a href="#1-3-合并不同物种数量的多序列比对文件，保持序列的比对" class="headerlink" title="1.3. 合并不同物种数量的多序列比对文件，保持序列的比对"></a>1.3. 合并不同物种数量的多序列比对文件，保持序列的比对</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/my_scripts/concat_msa_with_differenct_species.sh">concat_msa_with_differenct_species.sh</a></p>
+<h1 id="2-我修改的脚本（其他人写的）"><a href="#2-我修改的脚本（其他人写的）" class="headerlink" title="2. 我修改的脚本（其他人写的）"></a>2. 我修改的脚本（其他人写的）</h1><h2 id="2-1-从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息"><a href="#2-1-从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息" class="headerlink" title="2.1. 从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息"></a>2.1. 从包含mRNA和CDS的gff注释文件中提取intron的位置和长度信息</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/modifiedscripts/extract_intron_info.pl">extract_intron_info.pl</a></p>
+<h1 id="3-保存的脚本"><a href="#3-保存的脚本" class="headerlink" title="3. 保存的脚本"></a>3. 保存的脚本</h1><h2 id="3-1-batch-run-sh-——-多线程并行运行批量化命令"><a href="#3-1-batch-run-sh-——-多线程并行运行批量化命令" class="headerlink" title="3.1. batch_run.sh —— 多线程并行运行批量化命令"></a>3.1. batch_run.sh —— 多线程并行运行批量化命令</h2><p><a href="https://github.com/yanzhongsino/bioscripts/blob/main/saved_scripts/batch_run.sh">batch_run.sh</a></p>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_">#</span><span class="language-bash">!/bin/bash</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">batch_run.sh脚本用于多线程并行批量化循环命令，并控制并行数量始终为<span class="variable">$&#123;thread&#125;</span>。</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">usage: 修改thread=后的值为想要设定的并行线程数，把需要并行的命令替换23行的run something。运行脚本batch_run.sh，实现并行运行循环任务。</span></span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">ref：网上找的脚本，来源找不到了，侵删。</span></span><br><span class="line"></span><br><span class="line">thread=16</span><br><span class="line"></span><br><span class="line">start_time=`date +%s`              # 定义脚本运行的开始时间</span><br><span class="line">echo $&#123;start_time&#125;</span><br><span class="line">tmpFifo=/tmp/$$.fifo # 声明管道名称，$$表示脚本当前运行的进程PID</span><br><span class="line">mkfifo $&#123;tmpFifo&#125; # 创建有名管道</span><br><span class="line">exec 3&lt;&gt;$&#123;tmpFifo&#125;                   #创建文件描述符，以可读（&lt;）可写（&gt;）的方式关联管道文件，这时候文件描述符3就有了有名管道文件的所有特性</span><br><span class="line">rm -rf $&#123;tmpFifo&#125;                    #关联后的文件描述符拥有管道文件的所有特性,所以这时候管道文件可以删除，我们留下文件描述符来用就可以了</span><br><span class="line">for ((i=1;i&lt;=$&#123;thread&#125;;i++))</span><br><span class="line">do</span><br><span class="line">        echo &quot;&quot; &gt;&amp;3                   #&amp;3代表引用文件描述符3，这条命令代表往管道里面放入了一个&quot;令牌&quot;</span><br><span class="line">done</span><br><span class="line"></span><br><span class="line">for sample in $(cat sample_list.txt) #并行运行的循环</span><br><span class="line">do</span><br><span class="line">        read -u3                           #代表从管道中读取一个令牌</span><br><span class="line">        &#123;</span><br><span class="line">                echo $&#123;sample&#125;</span><br><span class="line">                run something # 需要并行运行的真实命令</span><br><span class="line">                echo &#x27;success&#x27; $&#123;sample&#125;       </span><br><span class="line">                echo &quot;&quot; &gt;&amp;3                   #代表我这一次命令执行到最后，把令牌放回管道</span><br><span class="line">        &#125; &amp;</span><br><span class="line">done</span><br><span class="line"><span class="meta prompt_"></span></span><br><span class="line"><span class="meta prompt_">#</span><span class="language-bash"><span class="comment"># ps:可以使用while read line; do echo $line; done &lt; file.txt代替for i in $(cat file.txt);do echo $i; done循环。但是不能用cat file.txt | while read line; do echo $line; done代替。</span></span></span><br><span class="line"></span><br><span class="line">wait</span><br><span class="line"> </span><br><span class="line">stop_time=`date +%s`  #定义脚本运行的结束时间</span><br><span class="line"> </span><br><span class="line">echo &quot;TIME:`expr $stop_time - $start_time`&quot;</span><br><span class="line">exec 3&lt;&amp;-                       #关闭文件描述符的读</span><br><span class="line">exec 3&gt;&amp;-                       #关闭文件描述符的写</span><br></pre></td></tr></table></figure>
+
+<h2 id="3-2-infernal-tblout2gff-pl-——-基因组ncRNA的infernal注释结果tblout格式转换成gff格式"><a href="#3-2-infernal-tblout2gff-pl-——-基因组ncRNA的infernal注释结果tblout格式转换成gff格式" class="headerlink" title="3.2. infernal-tblout2gff.pl —— 基因组ncRNA的infernal注释结果tblout格式转换成gff格式"></a>3.2. infernal-tblout2gff.pl —— 基因组ncRNA的infernal注释结果tblout格式转换成gff格式</h2><ol>
+<li>背景<br>用Infernal注释基因组的ncRNA，得到的结果可以用脚本整理成gff3格式。</li>
+</ol>
+<p>脚本来源：<a href="https://www.cnblogs.com/jessepeng/p/15392809.html">https://www.cnblogs.com/jessepeng/p/15392809.html</a></p>
+<ol start="2">
+<li>脚本</li>
+</ol>
+<figure class="highlight perl"><table><tr><td class="code"><pre><span class="line"><span class="comment">#!/usr/bin/env perl</span></span><br><span class="line"><span class="comment"># infernal-tblout2gff.pl: convert cmsearch or cmscan tblout files to GFF format</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># an important point of above ref:</span></span><br><span class="line"><span class="comment"># &quot;Start is always less than or equal to end&quot;</span></span><br><span class="line"><span class="comment"># EPN, Fri Jun  7 11:07:38 2019</span></span><br><span class="line"><span class="comment"># </span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="keyword">use</span> strict;</span><br><span class="line"><span class="keyword">use</span> warnings;</span><br><span class="line"><span class="keyword">use</span> Getopt::Long;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $in_tblout  = <span class="string">&quot;&quot;</span>;   <span class="comment"># name of input tblout file</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $usage;</span><br><span class="line">$usage  = <span class="string">&quot;infernal-tblout2gff.pl\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;Usage:\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl [OPTIONS] &lt;cmsearch tblout file&gt;\n\tOR\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl --cmscan [OPTIONS] &lt;cmscan tblout file&gt;\n\tOR\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;infernal-tblout2gff.pl --cmscan --fmt2 [OPTIONS] &lt;cmscan --fmt 2 tblout file&gt;\n\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\tOPTIONS:\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t-T &lt;n&gt;       : minimum bit score to include is &lt;n&gt;\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t-E &lt;x&gt;       : maximum E-value to include is &lt;x&gt;\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--cmscan     : tblout file was created by cmscan\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--source &lt;s&gt; : specify &#x27;source&#x27; field should be &lt;s&gt; (e.g. tRNAscan-SE)\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--fmt2       : tblout file was created with cmscan --fmt 2 option\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--all        : output all info in &#x27;attributes&#x27; column   [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--none       : output no info in &#x27;attributes&#x27; column    [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--desc       : output desc field in &#x27;attributes&#x27; column [default: E-value]\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--version &lt;s&gt;: append \&quot;-&lt;s&gt;\&quot; to &#x27;source&#x27; column\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--extra &lt;s&gt;  : append \&quot;&lt;s&gt;;\&quot; to &#x27;attributes&#x27; column\n&quot;</span>;</span><br><span class="line">$usage .= <span class="string">&quot;\t\t--hidedesc   : do not includ \&quot;desc\:\&quot; prior to desc value in &#x27;attributes&#x27; column\n&quot;</span>;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $do_minscore = <span class="number">0</span>;       <span class="comment"># set to &#x27;1&#x27; if -T used</span></span><br><span class="line"><span class="keyword">my</span> $do_maxevalue = <span class="number">0</span>;      <span class="comment"># set to &#x27;1&#x27; if -E used</span></span><br><span class="line"><span class="keyword">my</span> $minscore   = <span class="keyword">undef</span>;    <span class="comment"># defined if if -T used</span></span><br><span class="line"><span class="keyword">my</span> $maxevalue  = <span class="keyword">undef</span>;    <span class="comment"># defined if -E used</span></span><br><span class="line"><span class="keyword">my</span> $do_cmscan  = <span class="number">0</span>;        <span class="comment"># set to &#x27;1&#x27; if --cmscan used</span></span><br><span class="line"><span class="keyword">my</span> $do_fmt2    = <span class="number">0</span>;        <span class="comment"># set to &#x27;1&#x27; if --fmt</span></span><br><span class="line"><span class="keyword">my</span> $do_all_attributes = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --all</span></span><br><span class="line"><span class="keyword">my</span> $do_no_attributes  = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --none</span></span><br><span class="line"><span class="keyword">my</span> $do_de_attributes  = <span class="number">0</span>; <span class="comment"># set to &#x27;1&#x27; if --desc</span></span><br><span class="line"><span class="keyword">my</span> $version = <span class="keyword">undef</span>;       <span class="comment"># defined if --version used</span></span><br><span class="line"><span class="keyword">my</span> $extra = <span class="keyword">undef</span>;         <span class="comment"># defined if --extra used</span></span><br><span class="line"><span class="keyword">my</span> $do_hidedesc = <span class="number">0</span>;       <span class="comment"># set to 1 if --hidedesc used</span></span><br><span class="line"><span class="keyword">my</span> $opt_source = <span class="keyword">undef</span>;    <span class="comment"># set to &lt;s&gt; if --source &lt;s&gt; used</span></span><br><span class="line"></span><br><span class="line">&amp;GetOptions( <span class="string">&quot;T=s&quot;</span>       =&gt; \$minscore,</span><br><span class="line">             <span class="string">&quot;E=s&quot;</span>       =&gt; \$maxevalue,</span><br><span class="line">             <span class="string">&quot;cmscan&quot;</span>    =&gt; \$do_cmscan,</span><br><span class="line">             <span class="string">&quot;source=s&quot;</span>  =&gt; \$opt_source,</span><br><span class="line">             <span class="string">&quot;fmt2&quot;</span>      =&gt; \$do_fmt2,</span><br><span class="line">             <span class="string">&quot;all&quot;</span>       =&gt; \$do_all_attributes,</span><br><span class="line">             <span class="string">&quot;none&quot;</span>      =&gt; \$do_no_attributes,</span><br><span class="line">             <span class="string">&quot;desc&quot;</span>      =&gt; \$do_de_attributes,</span><br><span class="line">             <span class="string">&quot;version=s&quot;</span> =&gt; \$version, </span><br><span class="line">             <span class="string">&quot;extra=s&quot;</span>   =&gt; \$extra,</span><br><span class="line">             <span class="string">&quot;hidedesc&quot;</span>  =&gt; \$do_hidedesc);</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">scalar</span>(@ARGV) != <span class="number">1</span>) &#123; <span class="keyword">die</span> $usage; &#125;</span><br><span class="line"><span class="keyword">my</span> ($tblout_file) = @ARGV;</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $minscore)  &#123; $do_minscore  = <span class="number">1</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $maxevalue) &#123; $do_maxevalue = <span class="number">1</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>($do_minscore &amp;&amp; $do_maxevalue) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, -T and -E cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_all_attributes) &amp;&amp; ($do_no_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --all and --none cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_all_attributes) &amp;&amp; ($do_de_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --all and --desc cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_no_attributes) &amp;&amp; ($do_de_attributes)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --none and --desc cannot be used in combination. Pick one.&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>(($do_fmt2) &amp;&amp; (! $do_cmscan)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --fmt2 only makes sense in combination with --cmscan&quot;</span>; </span><br><span class="line">&#125;</span><br><span class="line"><span class="keyword">if</span>((<span class="keyword">defined</span> $opt_source) &amp;&amp; (<span class="keyword">defined</span> $version)) &#123; </span><br><span class="line">  <span class="keyword">die</span> <span class="string">&quot;ERROR, --source and --version are incompatible&quot;</span>;</span><br><span class="line">&#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span>(! -e $tblout_file) &#123; <span class="keyword">die</span> <span class="string">&quot;ERROR tblout file $tblout_file does not exist&quot;</span>; &#125;</span><br><span class="line"><span class="keyword">if</span>(! -s $tblout_file) &#123; <span class="keyword">die</span> <span class="string">&quot;ERROR tblout file $tblout_file is empty&quot;</span>; &#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">my</span> $source = ($do_cmscan) ? <span class="string">&quot;cmscan&quot;</span> : <span class="string">&quot;cmsearch&quot;</span>;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $version)    &#123; $source .= <span class="string">&quot;-&quot;</span> . $version; &#125;</span><br><span class="line"><span class="keyword">if</span>(<span class="keyword">defined</span> $opt_source) &#123; $source = $opt_source; &#125;</span><br><span class="line"></span><br><span class="line"><span class="keyword">open</span>(IN, $tblout_file) || <span class="keyword">die</span> <span class="string">&quot;ERROR unable to open $tblout_file for reading&quot;</span>; </span><br><span class="line"><span class="keyword">my</span> $line;</span><br><span class="line"><span class="keyword">my</span> $i;</span><br><span class="line"><span class="keyword">while</span>($line = &lt;IN&gt;) &#123; </span><br><span class="line">  <span class="keyword">if</span>($line !~ <span class="regexp">m/^\#/</span>) &#123; </span><br><span class="line">    <span class="keyword">chomp</span> $line;</span><br><span class="line">    <span class="keyword">my</span> @el_A = <span class="keyword">split</span>(<span class="regexp">/\s+/</span>, $line);</span><br><span class="line">    <span class="keyword">my</span> $nfields = <span class="keyword">scalar</span>(@el_A);</span><br><span class="line">    <span class="keyword">if</span>((! $do_fmt2) &amp;&amp; ($nfields &lt; <span class="number">18</span>)) &#123; </span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR expected at least 18 space delimited fields in tblout line (fmt 1, default) but got $nfields on line:\n$line\n&quot;</span>; </span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(($do_fmt2) &amp;&amp; ($nfields &lt; <span class="number">27</span>)) &#123; </span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR expected at least 27 space delimited fields in tblout line (fmt 2, --fmt2) but got $nfields on line:\n$line\n&quot;</span>; </span><br><span class="line">    &#125;</span><br><span class="line">    <span class="comment"># ref Infernal 1.1.2 user guide, pages 59-61</span></span><br><span class="line">    <span class="keyword">my</span> $idx     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqname = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqaccn = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlname = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlaccn = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $clan    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdl     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlfrom = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $mdlto   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqfrom = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $seqto   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $strand  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $trunc   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $pass    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $gc      = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $bias    = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $score   = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $evalue  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $inc     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $olp     = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyidx  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyfrct1= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $anyfrct2= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winidx  = <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winfrct1= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $winfrct2= <span class="keyword">undef</span>;</span><br><span class="line">    <span class="keyword">my</span> $desc    = <span class="keyword">undef</span>;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">if</span>($do_fmt2) &#123; <span class="comment"># 27 fields</span></span><br><span class="line">      $idx     = $el_A[<span class="number">0</span>];</span><br><span class="line">      $seqname = ($do_cmscan) ? $el_A[<span class="number">3</span>] : $el_A[<span class="number">1</span>];</span><br><span class="line">      $seqaccn = ($do_cmscan) ? $el_A[<span class="number">4</span>] : $el_A[<span class="number">2</span>];</span><br><span class="line">      $mdlname = ($do_cmscan) ? $el_A[<span class="number">1</span>] : $el_A[<span class="number">3</span>];</span><br><span class="line">      $mdlaccn = ($do_cmscan) ? $el_A[<span class="number">2</span>] : $el_A[<span class="number">4</span>];</span><br><span class="line">      $clan    = $el_A[<span class="number">5</span>];</span><br><span class="line">      $mdl     = $el_A[<span class="number">6</span>];</span><br><span class="line">      $mdlfrom = $el_A[<span class="number">7</span>];</span><br><span class="line">      $mdlto   = $el_A[<span class="number">8</span>];</span><br><span class="line">      $seqfrom = $el_A[<span class="number">9</span>];</span><br><span class="line">      $seqto   = $el_A[<span class="number">10</span>];</span><br><span class="line">      $strand  = $el_A[<span class="number">11</span>];</span><br><span class="line">      $trunc   = $el_A[<span class="number">12</span>];</span><br><span class="line">      $pass    = $el_A[<span class="number">13</span>];</span><br><span class="line">      $gc      = $el_A[<span class="number">14</span>];</span><br><span class="line">      $bias    = $el_A[<span class="number">15</span>];</span><br><span class="line">      $score   = $el_A[<span class="number">16</span>];</span><br><span class="line">      $evalue  = $el_A[<span class="number">17</span>];</span><br><span class="line">      $inc     = $el_A[<span class="number">18</span>];</span><br><span class="line">      $olp     = $el_A[<span class="number">19</span>];</span><br><span class="line">      $anyidx  = $el_A[<span class="number">20</span>];</span><br><span class="line">      $anyfrct1= $el_A[<span class="number">21</span>];</span><br><span class="line">      $anyfrct2= $el_A[<span class="number">22</span>];</span><br><span class="line">      $winidx  = $el_A[<span class="number">23</span>];</span><br><span class="line">      $winfrct1= $el_A[<span class="number">24</span>];</span><br><span class="line">      $winfrct2= $el_A[<span class="number">25</span>];</span><br><span class="line">      $desc    = $el_A[<span class="number">26</span>]; </span><br><span class="line">      <span class="keyword">for</span>($i = <span class="number">27</span>; $i &lt; $nfields; $i++) &#123; $desc .= <span class="string">&quot;_&quot;</span> . $el_A[$i]; &#125;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">else</span> &#123; <span class="comment"># fmt 1, default</span></span><br><span class="line">      $seqname = ($do_cmscan) ? $el_A[<span class="number">2</span>] : $el_A[<span class="number">0</span>];</span><br><span class="line">      $seqaccn = ($do_cmscan) ? $el_A[<span class="number">3</span>] : $el_A[<span class="number">1</span>];</span><br><span class="line">      $mdlname = ($do_cmscan) ? $el_A[<span class="number">0</span>] : $el_A[<span class="number">2</span>];</span><br><span class="line">      $mdlaccn = ($do_cmscan) ? $el_A[<span class="number">1</span>] : $el_A[<span class="number">3</span>];</span><br><span class="line">      $mdl     = $el_A[<span class="number">4</span>];</span><br><span class="line">      $mdlfrom = $el_A[<span class="number">5</span>];</span><br><span class="line">      $mdlto   = $el_A[<span class="number">6</span>];</span><br><span class="line">      $seqfrom = $el_A[<span class="number">7</span>];</span><br><span class="line">      $seqto   = $el_A[<span class="number">8</span>];</span><br><span class="line">      $strand  = $el_A[<span class="number">9</span>];</span><br><span class="line">      $trunc   = $el_A[<span class="number">10</span>];</span><br><span class="line">      $pass    = $el_A[<span class="number">11</span>];</span><br><span class="line">      $gc      = $el_A[<span class="number">12</span>];</span><br><span class="line">      $bias    = $el_A[<span class="number">13</span>];</span><br><span class="line">      $score   = $el_A[<span class="number">14</span>];</span><br><span class="line">      $evalue  = $el_A[<span class="number">15</span>];</span><br><span class="line">      $inc     = $el_A[<span class="number">16</span>];</span><br><span class="line">      $desc    = $el_A[<span class="number">17</span>]; </span><br><span class="line">      <span class="keyword">for</span>($i = <span class="number">18</span>; $i &lt; $nfields; $i++) &#123; $desc .= <span class="string">&quot;_&quot;</span> . $el_A[$i]; &#125;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="comment"># one sanity check, strand should make sense</span></span><br><span class="line">    <span class="keyword">if</span>(($strand <span class="keyword">ne</span> <span class="string">&quot;+&quot;</span>) &amp;&amp; ($strand <span class="keyword">ne</span> <span class="string">&quot;-&quot;</span>)) &#123; </span><br><span class="line">      <span class="keyword">if</span>(($do_fmt2) &amp;&amp; (($seqfrom eq <span class="string">&quot;+&quot;</span>) || ($seqfrom eq <span class="string">&quot;-&quot;</span>))) &#123; </span><br><span class="line">        <span class="keyword">die</span> <span class="string">&quot;ERROR problem parsing, you specified --fmt2 but tblout file appears to have NOT been created with --fmt 2, retry without --fmt2\nproblematic line:\n$line\n&quot;</span>; </span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">if</span>((! $do_fmt2) &amp;&amp; (($pass eq <span class="string">&quot;+&quot;</span>) || ($pass eq <span class="string">&quot;-&quot;</span>))) &#123; </span><br><span class="line">        <span class="keyword">die</span> <span class="string">&quot;ERROR problem parsing, you did not specify --fmt2 but tblout file appears to have been created with --fmt 2, retry with --fmt2\nproblematic line:\n$line\n&quot;</span>; </span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">die</span> <span class="string">&quot;ERROR unable to parse, problematic line:\n$line\n&quot;</span>;</span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">if</span>(($do_minscore) &amp;&amp; ($score &lt; $minscore)) &#123; </span><br><span class="line">      ; <span class="comment"># skip</span></span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">elsif</span>(($do_maxevalue) &amp;&amp; ($evalue &gt; $maxevalue)) &#123; </span><br><span class="line">      ; <span class="comment"># skip</span></span><br><span class="line">    &#125;</span><br><span class="line">    <span class="keyword">else</span> &#123; </span><br><span class="line">      <span class="keyword">my</span> $attributes = <span class="string">&quot;evalue=&quot;</span> . $evalue; <span class="comment"># default to just evalue</span></span><br><span class="line">      <span class="keyword">if</span>($do_all_attributes) &#123; </span><br><span class="line">        <span class="keyword">if</span>($do_fmt2) &#123; </span><br><span class="line">          $attributes .= <span class="keyword">sprintf</span>(<span class="string">&quot;;idx=$idx;seqaccn=$seqaccn;mdlaccn=$mdlaccn;clan=$clan;mdl=$mdl;mdlfrom=$mdlfrom;mdlto=$mdlto;trunc=$trunc;pass=$pass;gc=$gc;bias=$bias;inc=$inc;olp=$olp;anyidx=$anyidx;anyfrct1=$anyfrct1;anyfrct2=$anyfrct2;winidx=$winidx;winfrct1=$winfrct1;winfrct2=$winfrct2;%s$desc&quot;</span>, ($do_hidedesc ? <span class="string">&quot;&quot;</span> : <span class="string">&quot;desc=&quot;</span>));</span><br><span class="line">        &#125;</span><br><span class="line">        <span class="keyword">else</span> &#123; </span><br><span class="line">          $attributes .= <span class="keyword">sprintf</span>(<span class="string">&quot;;seqaccn=$seqaccn;mdlaccn=$mdlaccn;mdl=$mdl;mdlfrom=$mdlfrom;mdlto=$mdlto;trunc=$trunc;pass=$pass;gc=$gc;bias=$bias;inc=$inc;%s$desc&quot;</span>, ($do_hidedesc ? <span class="string">&quot;&quot;</span> : <span class="string">&quot;desc=&quot;</span>));</span><br><span class="line">        &#125;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">elsif</span>($do_no_attributes) &#123; </span><br><span class="line">        $attributes = <span class="string">&quot;-&quot;</span>;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">elsif</span>($do_de_attributes) &#123; </span><br><span class="line">        $attributes = $desc;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">if</span>(<span class="keyword">defined</span> $extra) &#123; </span><br><span class="line">        <span class="keyword">if</span>($attributes eq <span class="string">&quot;-&quot;</span>)       &#123; $attributes = <span class="string">&quot;&quot;</span>; &#125;</span><br><span class="line">        <span class="keyword">elsif</span>($attributes !~ <span class="regexp">m/\;$/</span>) &#123; $attributes .= <span class="string">&quot;;&quot;</span>; &#125;</span><br><span class="line">        $attributes .= $extra . <span class="string">&quot;;&quot;</span>;</span><br><span class="line">      &#125;</span><br><span class="line">      <span class="keyword">printf</span>(<span class="string">&quot;%s\t%s\t%s\t%d\t%d\t%.1f\t%s\t%s\t%s\n&quot;</span>, </span><br><span class="line">             $seqname,                             <span class="comment"># token 1: &#x27;sequence&#x27; (sequence name)</span></span><br><span class="line">             $source,                              <span class="comment"># token 2: &#x27;source&#x27;</span></span><br><span class="line">             $mdlname,                             <span class="comment"># token 3: &#x27;feature&#x27; (model name) you may want to change this to &#x27;ncRNA&#x27;</span></span><br><span class="line">             ($strand eq <span class="string">&quot;+&quot;</span>) ? $seqfrom : $seqto, <span class="comment"># token 4: &#x27;start&#x27; in coordinate space [1..seqlen], must be &lt;= &#x27;end&#x27;</span></span><br><span class="line">             ($strand eq <span class="string">&quot;+&quot;</span>) ? $seqto : $seqfrom, <span class="comment"># token 5: &#x27;end&#x27; in coordinate space [1..seqlen], must be &gt;= &#x27;start&#x27;</span></span><br><span class="line">             $score,                               <span class="comment"># token 6: &#x27;score&#x27; bit score</span></span><br><span class="line">             $strand,                              <span class="comment"># token 7: &#x27;strand&#x27; (&#x27;+&#x27; or &#x27;-&#x27;)</span></span><br><span class="line">             <span class="string">&quot;.&quot;</span>,                                  <span class="comment"># token 8: &#x27;phase&#x27; irrelevant for noncoding RNAs</span></span><br><span class="line">             $attributes);                         <span class="comment"># token 9: attributes, currently only E-value, unless --all, --none or --desc</span></span><br><span class="line">    &#125;</span><br><span class="line">  &#125;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>运行<br><code>perl infernal-tblout2gff.pl --cmscan --fmt2 genome.tblout &gt;genome.infernal.ncRNA.gff3</code></li>
+</ol>
+<p>输入是Infernal的table格式输出文件，输出是gff3格式的ncRNA注释文件。</p>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
 </ul>
 <img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=50% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
@@ -17784,39 +17331,257 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>bootstrap</title>
-    <url>/2018/06/11/web_bootstrap/</url>
-    <content><![CDATA[<h1 id="1-bootstrap简介"><a href="#1-bootstrap简介" class="headerlink" title="1. bootstrap简介"></a>1. bootstrap简介</h1><p>bootstrap是一个用于快速开发web应用和网站的前端框架。<br>它能用于开发响应式布局、移动设备优先的web项目。</p>
-<h1 id="2-bootstrap的安装"><a href="#2-bootstrap的安装" class="headerlink" title="2. bootstrap的安装"></a>2. bootstrap的安装</h1><p>需要在html的link标签引入bootstrap的css插件bootstrap.css，并在script标签中引用bootstrap的js插件bootstrap.js(bootstrap的js插件使用需要先引入jQuery)，有两种方式：</p>
-<h4 id="2-0-0-1-通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。"><a href="#2-0-0-1-通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。" class="headerlink" title="2.0.0.1. 通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。"></a>2.0.0.1. 通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如<a href="http://www.bootcdn.cn/bootstrap/">bootCDN</a>。</h4><h4 id="2-0-0-2-在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。"><a href="#2-0-0-2-在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。" class="headerlink" title="2.0.0.2. 在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。"></a>2.0.0.2. 在<a href="http://v3.bootcss.com/">bootstrap中文网</a>下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。</h4><p>在link标签中引入文件夹的css目录下的bootstrap.min.css文件</p>
-<p><code>&lt;link href=&quot;css/bootstrap.min.css&quot; rel=&quot;stylesheet&quot;&gt;</code></p>
-<p>在script标签中引用文件夹的js目录下的bootstrap.min.js文件(bootstrap的js插件使用需要先引入jQuery)</p>
-<h1 id="3-bootstrap的css"><a href="#3-bootstrap的css" class="headerlink" title="3. bootstrap的css"></a>3. bootstrap的css</h1><ol>
-<li><p>使用<code>&lt;img src=&quot;&quot; class=&quot;img-responsive&quot;&gt;</code>使图像对响应式布局更友好地支持。</p>
-</li>
-<li><p>Bootstrap 排版、链接样式设置了基本的全局样式。分别是：</p>
+    <title>录屏软件</title>
+    <url>/2022/09/22/tools_screen.recorder/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105136&auto=1&height=32"></iframe></div>
+
+<h1 id="1-录屏软件"><a href="#1-录屏软件" class="headerlink" title="1. 录屏软件"></a>1. 录屏软件</h1><p>尝试了多种录屏方式，也遇到一些问题。记录下来，供参考。</p>
+<p>如果在window上简单录制，则优先window+G使用xbox即可。如果是需要录课程等非游戏视频，推荐开源免费的OBS Studio，类似的还有Captura。</p>
+<h1 id="2-Xbox"><a href="#2-Xbox" class="headerlink" title="2. Xbox"></a>2. Xbox</h1><p>Xbox是window10自带的录制游戏的软件，不需下载。</p>
+<p>Xbox只能录制应用程序，不能录制桌面。</p>
+<h2 id="2-1-Xbox使用"><a href="#2-1-Xbox使用" class="headerlink" title="2.1. Xbox使用"></a>2.1. Xbox使用</h2><ol>
+<li>window+G调出Xbox录屏界面</li>
+<li>点击屏幕上方的“捕获”调出捕获小组件</li>
+<li>建议点击调出来的捕获小组件右上角的小图钉，在桌面固定捕获小组件</li>
+<li>简单地点击“录制”和“停止录制”开关按钮即可开始或停止录制</li>
+</ol>
+<h2 id="2-2-Xbox设置"><a href="#2-2-Xbox设置" class="headerlink" title="2.2. Xbox设置"></a>2.2. Xbox设置</h2><ol>
+<li>把需要录制的软件选为游戏。这是为了避免录制时其他软件的干扰。</li>
+</ol>
 <ul>
-<li>为 body 元素设置 <code>background-color: #fff</code>;</li>
-<li>使用 <code>@font-family-base</code>、<code>@font-size-base</code> 和 <code>@line-height-base a</code>变量作为排版的基本参数</li>
-<li>为所有链接设置了基本颜色 <code>@link-color</code> ，并且当链接处于 :hover 状态时才添加下划线</li>
-<li>这些样式都能在 scaffolding.less 文件中找到对应的源码。</li>
+<li>window+G调出Xbox界面</li>
+<li>点击屏幕上方的“设置”调出设置窗口</li>
+<li>在弹出的窗口选择“常规”</li>
+<li>有一个选项：“记住这是一款游戏”，下面会实时显示在最上层的软件，把需要录制的软件放在最上层，选中“记住这是一款游戏”。</li>
 </ul>
-</li>
-<li><p>使用<strong>normalize.css</strong>来建立跨浏览器的一致性。</p>
-</li>
-<li><p>.container 类用于固定宽度并支持响应式布局的容器。.container-fluid 类用于 100% 宽度，占据全部视口（viewport）的容器。</p>
-</li>
+<ol start="2">
+<li>后台录制。这是为了避免录制时其他软件的干扰。</li>
 </ol>
-]]></content>
-      <categories>
-        <category>web</category>
-      </categories>
-      <tags>
-        <tag>bootstrap</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>flex</title>
+<ul>
+<li>“设置”界面选择“正在捕获”</li>
+<li>在“正在录制”的下面选择“当我玩游戏时进行后台录制”</li>
+</ul>
+<ol start="3">
+<li>音频选择</li>
+</ol>
+<ul>
+<li>在“正在捕获”界面的“要录制的音频”下选择录制那些音频：游戏、所有、无。</li>
+<li>我选择的是“所有”，包括游戏和已启用麦克风，系统和其他应用程序。</li>
+</ul>
+<ol start="4">
+<li>麦克风</li>
+</ol>
+<ul>
+<li>在右上角的“查看我的捕获”界面，有个麦克风开关。可以根据需要选择，如果录课则关闭，需要录自己声音则打开。</li>
+</ul>
+<h1 id="3-OBS-Studio"><a href="#3-OBS-Studio" class="headerlink" title="3. OBS Studio"></a>3. OBS Studio</h1><h2 id="3-1-OBS-Studio简介"><a href="#3-1-OBS-Studio简介" class="headerlink" title="3.1. OBS Studio简介"></a>3.1. OBS Studio简介</h2><p>OBS Studio是开源软件，完全免费，适用windows，macOS和Liunx系统，可用于视频录制和推流。</p>
+<p>OBS Studio主页：<a href="https://obsproject.com/">https://obsproject.com/</a></p>
+<h2 id="3-2-OBS-Studio使用"><a href="#3-2-OBS-Studio使用" class="headerlink" title="3.2. OBS Studio使用"></a>3.2. OBS Studio使用</h2><p>以录制腾讯会议为例</p>
+<ol>
+<li>打开软件</li>
+<li>在“来源”中添加需要录制的源</li>
+</ol>
+<ul>
+<li>点击“+”号，有非常多种来源可选择：包括“窗口采集”，“显示器采集”，“浏览器”，“游戏源”，“媒体源”等等。</li>
+<li>这里录制腾讯会议，选择“窗口采集”</li>
+<li>弹出创建源窗口：命名来源，点击确定</li>
+<li>弹出属性窗口：在“窗口”中选择“腾讯会议”，“显示鼠标指针”可以根据需要选择，点击确定。</li>
+</ul>
+<ol start="3">
+<li>使源窗口全屏</li>
+</ol>
+<ul>
+<li>添加的源未必刚好覆盖录制的全屏，如果这样开始录制，视频会包含大量的黑框部分。</li>
+<li>可以直接拉伸添加的源外面的红框，使其覆盖全屏录制区域。</li>
+<li>也可以简单地在“编辑”-“变换”中选择“比例适配屏幕”来最大化源窗口使其适配录制屏幕【推荐】，或者选择“拉伸到全屏”来调整源窗口比例，从而实现完全全屏。</li>
+</ul>
+<ol start="4">
+<li>录制</li>
+</ol>
+<ul>
+<li>在“来源”中出现新建的源，选中这个源，右边的“控件”中使用“开始录制”即可开始，“停止录制”即停止。</li>
+</ul>
+<h2 id="3-3-OBS-Studio设置"><a href="#3-3-OBS-Studio设置" class="headerlink" title="3.3. OBS Studio设置"></a>3.3. OBS Studio设置</h2><h3 id="3-3-1-打开“暂停录制”功能"><a href="#3-3-1-打开“暂停录制”功能" class="headerlink" title="3.3.1. 打开“暂停录制”功能"></a>3.3.1. 打开“暂停录制”功能</h3><p>软件默认设置是不支持“暂停录制”，可通过修改设置来打开“暂停录制”的按钮。</p>
+<ol>
+<li>“文件”-“设置”，弹出设置窗口</li>
+<li>“输出”，下面有显示“当录像质量设为与串流画质相同时，无法暂停录制”。所以需要把“录像”-“录像质量”中选择其他的，我这里选择“高质量”。</li>
+<li>“输出”-“输出模式”中选择“高级”，然后选择“录像”，下面有显示“当录像编码器设为使用推流编码器时，无法暂停录制。”所以在“录制设置”-“编码器”中选择其他的，我这里选择“x264”。</li>
+<li>应用和保存设置。</li>
+<li>这时，如果点击“开始录制”，右边会出现一个暂停按钮，暂停功能就打开了。</li>
+</ol>
+<h3 id="3-3-2-输出设置"><a href="#3-3-2-输出设置" class="headerlink" title="3.3.2. 输出设置"></a>3.3.2. 输出设置</h3><ol>
+<li>“文件”-“设置”，弹出设置窗口</li>
+<li>“输出”-“录像”这里可以设置录像的质量，格式，编码器等。</li>
+</ol>
+<h1 id="4-Captura"><a href="#4-Captura" class="headerlink" title="4. Captura"></a>4. Captura</h1><p>Captura也是开源的录屏软件，网上看到很多推荐，界面简单。</p>
+<p>Captura下载：<a href="https://mathewsachin.github.io/Captura/">https://mathewsachin.github.io/Captura/</a></p>
+<p>但是我自己用下来遇到几个问题。</p>
+<ol>
+<li>首先FFmpeg解码器我用不了也无法自动下载，自己下载导入后也用不了，就没继续折腾了。</li>
+<li>选择窗口录屏，这个窗口一定要一直保持在最上方，如果有其他软件覆盖，就会录到其他软件。</li>
+<li>音频录制的选择，要不就没声音，要不就把麦克风录进去，反正没调对。</li>
+</ol>
+<p>应该是挺不错的软件，可能是和我电脑哪里不兼容，还是我没设置对，反正最后弃了。</p>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>tools</category>
+      </categories>
+      <tags>
+        <tag>Windows</tag>
+        <tag>screen recorder</tag>
+        <tag>video</tag>
+        <tag>Xbox</tag>
+        <tag>OBS Studio</tag>
+        <tag>Captura</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>CSS</title>
+    <url>/2018/06/04/web_CSS/</url>
+    <content><![CDATA[<h1 id="1-CSS基础知识"><a href="#1-CSS基础知识" class="headerlink" title="1. CSS基础知识"></a>1. CSS基础知识</h1><h2 id="1-1-动画的CSS实现"><a href="#1-1-动画的CSS实现" class="headerlink" title="1.1. 动画的CSS实现"></a>1.1. 动画的CSS实现</h2><ol>
+<li><p>transform</p>
+<p>用于元素的2D或3D变化，默认值为none，有六种种变化方式。</p>
+<ul>
+<li>translate(x,y)、translate3d(x,y,z)位移变化，分别为2d和3d变化方式，衍生出translateX(x)、translateY(y)、translateZ(Z)</li>
+<li>scale(x,y)、scale3d(x,y,z)缩放变化，同样衍生三轴的单独变化方式。</li>
+<li>rotate(angle)、rotate3d(x,y,z,angle)旋转变化，同样衍生三轴的单独变化方式。rotate旋转中心点默认是元素中心，可以通过设置<code>tranform-origin：x-axis y-axis z-axis</code>来改变旋转中心点的位置。</li>
+<li>skew(x-angle,y-angle)沿着x和y轴的2d倾斜变化，衍生两轴单独变化方式。</li>
+<li>matrix(n,n,n,n,n,n)、matrix3d(n,n,n,n,n,n,n,n,n,n,n,n,n,n,n,n)分别定义2d和3d转换，使用6个值或16个值（4*4）的矩阵。</li>
+<li>perspective(n)为3d元素定义透视视图。</li>
+</ul>
+<p><code>transform-style:flat(default)/preserve-3d</code>指定嵌套元素怎样在三维空间呈现，flat表示所有子元素在2d平面呈现，preserve-3d表示所有子元素在3d空间呈现。</p>
+</li>
+<li><p>transition</p>
+<p><code>transition</code>是<code>transition-property</code>、<code>transition-duration</code>、<code>transition-timing-function</code>和<code>transition-delay</code>四个属性的简写，默认值<code>all 0 ease 0</code>，是指定元素属性发生渐变的一种效果，一定要事件触发。</p>
+<ul>
+<li><code>transition-property:width</code>指定需要渐变变化的css属性，比如宽度的渐变。</li>
+<li><code>transition-duration:xs</code>指定渐变效果的时长为x秒，必须指定，否则为0，等同于没有渐变效果。</li>
+<li><code>transition-timing-function</code>指定transition效果的转速曲线，可取以下值：<br>* linear 规定以相同速度开始至结束的过渡效果（等于 cubic-bezier(0,0,1,1)）。<br>* ease 规定慢速开始，然后变快，然后慢速结束的过渡效果（cubic-bezier(0.25,0.1,0.25,1)）。<br>* ease-in 规定以慢速开始的过渡效果（等于 cubic-bezier(0.42,0,1,1)）。<br>* ease-out 规定以慢速结束的过渡效果（等于 cubic-bezier(0,0,0.58,1)）。<br>* ease-in-out 规定以慢速开始和结束的过渡效果（等于 cubic-bezier(0.42,0,0.58,1)）。<br>* cubic-bezier(n,n,n,n) 在 cubic-bezier 函数中定义自己的值。可能的值是 0 至 1 之间的数值。</li>
+<li><code>transition-delay:xs</code>指定transition效果需要等待x秒才开始。</li>
+</ul>
+</li>
+<li><p>animation</p>
+<p>通过@keyframes定义动画的每一帧，然后使用animation属性调用动画，animation默认值是<code>none 0 ease 0 1 normal</code>，是多个属性的简写，属性依次如下：</p>
+<ul>
+<li><code>animation-name</code>在@keyframes中定义的动画名。</li>
+<li><code>animation-duration</code>指定动画的时长。</li>
+<li><code>animation-timing-function</code>指定动画的转速曲线，同<code>transition</code>。</li>
+<li><code>animation-delay</code>指定动画在启动前的延迟间隔，可为负值，负值时直接跳过动画的制定时间开始播放。</li>
+<li><code>animation-iteration-count</code>指定动画播放次数，值为<code>infinite</code>时无限多次播放。</li>
+<li><code>animation-direction</code>指定是否轮流反向播放动画。</li>
+<li><code>animation-fill-mode</code>规定当动画不播放时（当动画完成时，或当动画有一个延迟未开始播放时），要应用到元素的样式。</li>
+<li><code>animation-play-state</code>指定动画是否正在运行或已暂停。</li>
+</ul>
+</li>
+</ol>
+<p><strong>@keyframes语法</strong></p>
+<p>@keyframes myanimation{}</p>
+<ol>
+<li>tips</li>
+</ol>
+<p>配合hover等事件来实现动画效果。</p>
+<h3 id="1-1-1-几个要点"><a href="#1-1-1-几个要点" class="headerlink" title="1.1.1. 几个要点"></a>1.1.1. 几个要点</h3><ol>
+<li>class属性</li>
+</ol>
+<ul>
+<li>元素在设置样式时，首先考虑是否有多个相同元素具有同一样式和是否复用的问题，如果单个元素使用的样式或者不会复用，则优先使用内联样式，在html中就实现。</li>
+<li>如果需要在css文件中实现的样式，则尽量使用class属性，使得具有同样样式的元素使用同一class属性，减少css的冗余。</li>
+<li>对class属性的命明尽量语意化。</li>
+</ul>
+<ol start="2">
+<li>初始化</li>
+</ol>
+<ul>
+<li>使用<strong>reset.css</strong>或者<strong>normalize.css</strong>来初始化，使得css设置更轻松。或者在css文档开头，简单地用<code>*&#123;margin:0;padding:0&#125;</code>来初始化所有元素的边距，因为某些元素（例如h）自带一些边距。</li>
+<li>使用<strong>felxible.js</strong>来在css中可以使用<strong>rem</strong>（为显示设备宽度的十分之一）来作为长度单位，更多地应用在移动端。</li>
+</ul>
+<ol start="3">
+<li>border-width</li>
+</ol>
+<p>参考<a href="https://www.jianshu.com/p/63fdbd7fdc9b">简书</a>的这个总结和<a href="https://www.imooc.com/video/6858">慕课网</a>的这个使用border-width制作折角效果的视频。border-width实现三角形等图形很方便。</p>
+<p>当content区为空（width和height都为0）时，四个方向的border分别为三角形，当四个方向的border-width都不为0时，组成一个矩形。</p>
+<p>没有定义基本border时，未被定义的边那个方向的矩形的一半不会显示，并影响相邻边的显示。所以，若只定义了对边的边框，则相当于四个边框都未被定义。<br>当定义了基本border，即使是白色或透明，在此基础上再定义边框时，则会安装定义的边框的三角形的组合来显示。</p>
+<h2 id="1-2-问题笔记"><a href="#1-2-问题笔记" class="headerlink" title="1.2. 问题笔记"></a>1.2. 问题笔记</h2><ol>
+<li>任意数量图片从左到右按顺序排列，每行预期排五个，图片间有相同间距的样式的实现。<br>Q:解决分散排列，并适用于不同数量图片的问题。<br>如果直接定义父容器的<code>display:flex</code>和<code>justify-content:space-between</code>可以实现图片分散排列，但是当图片数量不是5时（比如为3时），也会展示分散排列的效果，而非占据左边2&#x2F;5的位置。<br>A：定义父容器的<code>display:flex</code>和子元素图片<code>width:20%</code>即可实现。<br>Q:解决图片排列紧凑的问题。<br>A:定义子元素图片<code>box-sizing:border-box;padding:1%;</code>来实现图片间隙。</li>
+<li>实现任意元素单行排列，不换行，超出省略并显示省略号。<br>Q：强制不换行<br>A：<code>white-space:nowrap;</code><br>Q:超出省略<br>A：<code>overflow: hidden;</code><br>Q：超出显示省略号<br>A：<code>text-overflow: ellipsis;</code></li>
+</ol>
+<h2 id="1-3-tips"><a href="#1-3-tips" class="headerlink" title="1.3. tips"></a>1.3. tips</h2><ul>
+<li>公司项目少有使用<strong>响应式</strong>CSS，而是移动端和web端各一套CSS，以使得网页在不同设备的展示更和谐，主要是长度设置、移动端键盘弹出遮盖内容区、移动端触摸屏更多操作等问题。</li>
+<li>尽量不要设定元素的<code>height</code>，使其保持auto。</li>
+<li>一些icon、line和矩形可以使用<code>div</code>或者<code>:before :after</code>来实现。</li>
+<li>使用<code>border-randius</code>属性来控制矩形或者圆形。</li>
+<li><code>white-space</code>属性指定元素内的空白怎么处理，文本元素取值<code>nowrap</code>时，文本强制不换行，直到遇到<br>元素。</li>
+<li>使用图片作为背景时，一般设置<code>background-position:center</code>来使图片居中展示，当图片与背景尺寸不匹配时，图片仍然居中。</li>
+</ul>
+]]></content>
+      <categories>
+        <category>web</category>
+      </categories>
+      <tags>
+        <tag>CSS</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>ES6</title>
+    <url>/2018/06/12/web_ES6/</url>
+    <content><![CDATA[<h2 id="常量"><a href="#常量" class="headerlink" title="常量"></a>常量</h2><p>const pi &#x3D; 3.1415926<br>常量是只读的，不可修改。</p>
+<h2 id="作用域"><a href="#作用域" class="headerlink" title="作用域"></a>作用域</h2><p>ES6中有块级作用域<br>const、let，在每次循环内都有自己的闭包和作用域。<br>{}内就是块级作用域。</p>
+<h2 id="箭头函数"><a href="#箭头函数" class="headerlink" title="箭头函数"></a>箭头函数</h2><ol>
+<li>语法：(参数)&#x3D;&gt;{函数体}<br>当只有一个参数时，可省略()；当函数体只有return something时，可以省略{}。eg：i&#x3D;&gt;i+1</li>
+<li>箭头函数的this是定义该名称时this的指向</li>
+</ol>
+<h2 id="默认参数"><a href="#默认参数" class="headerlink" title="默认参数"></a>默认参数</h2><p>function func(x&#x3D;1,y&#x3D;5,z&#x3D;20){<br>return x+y+z<br>}</p>
+<h3 id="可变参数"><a href="#可变参数" class="headerlink" title="可变参数"></a>可变参数</h3><p>不知数量参数的求和<br>function func(…a){<br>a.forEach(item&#x3D;&gt;{<br>sum+&#x3D;item*1<br>});<br>return sum<br>}</p>
+<h3 id="利用扩展运算符合并数组"><a href="#利用扩展运算符合并数组" class="headerlink" title="利用扩展运算符合并数组"></a>利用扩展运算符合并数组</h3><p>var params &#x3D; [‘hello’,true,1,23]<br>var concatlist &#x3D; [1,2,’world’,…params]<br>相当于concatlist为[1,2,’world’,’hello’,true,1,23]</p>
+<h2 id="对象代理"><a href="#对象代理" class="headerlink" title="对象代理"></a>对象代理</h2><p>私有属性<br>let Person &#x3D; {<br>name: ‘karen’,<br>sex: ‘female’,<br>age: 18<br>}</p>
+<p>let person &#x3D; new Proxy(Person,{<br>get(target,key){<br>return target[key]<br>},<br>set(target,key,value){<br>if(key!&#x3D;&#x3D;’sex’){<br>target[key]&#x3D;value;<br>}<br>}<br>})<br>通过Proxy对象代理实现保护Person的sex属性，使其不能修改。</p>
+]]></content>
+      <categories>
+        <category>web</category>
+      </categories>
+      <tags>
+        <tag>ES6</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>bootstrap</title>
+    <url>/2018/06/11/web_bootstrap/</url>
+    <content><![CDATA[<h1 id="1-bootstrap简介"><a href="#1-bootstrap简介" class="headerlink" title="1. bootstrap简介"></a>1. bootstrap简介</h1><p>bootstrap是一个用于快速开发web应用和网站的前端框架。<br>它能用于开发响应式布局、移动设备优先的web项目。</p>
+<h1 id="2-bootstrap的安装"><a href="#2-bootstrap的安装" class="headerlink" title="2. bootstrap的安装"></a>2. bootstrap的安装</h1><p>需要在html的link标签引入bootstrap的css插件bootstrap.css，并在script标签中引用bootstrap的js插件bootstrap.js(bootstrap的js插件使用需要先引入jQuery)，有两种方式：</p>
+<h4 id="2-0-0-1-通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。"><a href="#2-0-0-1-通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。" class="headerlink" title="2.0.0.1. 通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如bootCDN。"></a>2.0.0.1. 通过CDN（内容分发网络）引用bootstrap，多个CDN可以使用。比如<a href="http://www.bootcdn.cn/bootstrap/">bootCDN</a>。</h4><h4 id="2-0-0-2-在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。"><a href="#2-0-0-2-在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。" class="headerlink" title="2.0.0.2. 在bootstrap中文网下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。"></a>2.0.0.2. 在<a href="http://v3.bootcss.com/">bootstrap中文网</a>下载bootstrap，是一个文件夹，下有css、js、fonts三个目录。</h4><p>在link标签中引入文件夹的css目录下的bootstrap.min.css文件</p>
+<p><code>&lt;link href=&quot;css/bootstrap.min.css&quot; rel=&quot;stylesheet&quot;&gt;</code></p>
+<p>在script标签中引用文件夹的js目录下的bootstrap.min.js文件(bootstrap的js插件使用需要先引入jQuery)</p>
+<h1 id="3-bootstrap的css"><a href="#3-bootstrap的css" class="headerlink" title="3. bootstrap的css"></a>3. bootstrap的css</h1><ol>
+<li><p>使用<code>&lt;img src=&quot;&quot; class=&quot;img-responsive&quot;&gt;</code>使图像对响应式布局更友好地支持。</p>
+</li>
+<li><p>Bootstrap 排版、链接样式设置了基本的全局样式。分别是：</p>
+<ul>
+<li>为 body 元素设置 <code>background-color: #fff</code>;</li>
+<li>使用 <code>@font-family-base</code>、<code>@font-size-base</code> 和 <code>@line-height-base a</code>变量作为排版的基本参数</li>
+<li>为所有链接设置了基本颜色 <code>@link-color</code> ，并且当链接处于 :hover 状态时才添加下划线</li>
+<li>这些样式都能在 scaffolding.less 文件中找到对应的源码。</li>
+</ul>
+</li>
+<li><p>使用<strong>normalize.css</strong>来建立跨浏览器的一致性。</p>
+</li>
+<li><p>.container 类用于固定宽度并支持响应式布局的容器。.container-fluid 类用于 100% 宽度，占据全部视口（viewport）的容器。</p>
+</li>
+</ol>
+]]></content>
+      <categories>
+        <category>web</category>
+      </categories>
+      <tags>
+        <tag>bootstrap</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>flex</title>
     <url>/2018/06/12/web_flex/</url>
     <content><![CDATA[<h1 id="1-flex基础知识"><a href="#1-flex基础知识" class="headerlink" title="1. flex基础知识"></a>1. flex基础知识</h1><p>以下是学习<a href="http://www.ruanyifeng.com/blog/2015/07/flex-grammar.html">阮一峰老师教程</a>后的笔记。<br>传统布局解决方案基于<strong>盒模型</strong>，常使用<code>display</code>、<code>position</code>和<code>float</code>属性来实现，但对某些特殊布局非常不方便，比如垂直居中。<br>使用**<code>display:flex</code>**可以方便地实现多个元素的优雅排版，居中对齐也很方便。</p>
 <h2 id="1-1-flex基本概念"><a href="#1-1-flex基本概念" class="headerlink" title="1.1. flex基本概念"></a>1.1. flex基本概念</h2><ol>
@@ -17955,125 +17720,15 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>录屏软件</title>
-    <url>/2022/09/22/tools_screen.recorder/</url>
-    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=105136&auto=1&height=32"></iframe></div>
-
-<h1 id="1-录屏软件"><a href="#1-录屏软件" class="headerlink" title="1. 录屏软件"></a>1. 录屏软件</h1><p>尝试了多种录屏方式，也遇到一些问题。记录下来，供参考。</p>
-<p>如果在window上简单录制，则优先window+G使用xbox即可。如果是需要录课程等非游戏视频，推荐开源免费的OBS Studio，类似的还有Captura。</p>
-<h1 id="2-Xbox"><a href="#2-Xbox" class="headerlink" title="2. Xbox"></a>2. Xbox</h1><p>Xbox是window10自带的录制游戏的软件，不需下载。</p>
-<p>Xbox只能录制应用程序，不能录制桌面。</p>
-<h2 id="2-1-Xbox使用"><a href="#2-1-Xbox使用" class="headerlink" title="2.1. Xbox使用"></a>2.1. Xbox使用</h2><ol>
-<li>window+G调出Xbox录屏界面</li>
-<li>点击屏幕上方的“捕获”调出捕获小组件</li>
-<li>建议点击调出来的捕获小组件右上角的小图钉，在桌面固定捕获小组件</li>
-<li>简单地点击“录制”和“停止录制”开关按钮即可开始或停止录制</li>
-</ol>
-<h2 id="2-2-Xbox设置"><a href="#2-2-Xbox设置" class="headerlink" title="2.2. Xbox设置"></a>2.2. Xbox设置</h2><ol>
-<li>把需要录制的软件选为游戏。这是为了避免录制时其他软件的干扰。</li>
-</ol>
+    <title>jquery</title>
+    <url>/2018/06/05/web_jquery/</url>
+    <content><![CDATA[<h1 id="1-jQuery基础知识"><a href="#1-jQuery基础知识" class="headerlink" title="1. jQuery基础知识"></a>1. jQuery基础知识</h1><h2 id="1-1-jquery的安装"><a href="#1-1-jquery的安装" class="headerlink" title="1.1. jquery的安装"></a>1.1. jquery的安装</h2><p>只需要在html的script标签中引用jquery，两种方式：</p>
 <ul>
-<li>window+G调出Xbox界面</li>
-<li>点击屏幕上方的“设置”调出设置窗口</li>
-<li>在弹出的窗口选择“常规”</li>
-<li>有一个选项：“记住这是一款游戏”，下面会实时显示在最上层的软件，把需要录制的软件放在最上层，选中“记住这是一款游戏”。</li>
-</ul>
-<ol start="2">
-<li>后台录制。这是为了避免录制时其他软件的干扰。</li>
-</ol>
-<ul>
-<li>“设置”界面选择“正在捕获”</li>
-<li>在“正在录制”的下面选择“当我玩游戏时进行后台录制”</li>
-</ul>
-<ol start="3">
-<li>音频选择</li>
-</ol>
-<ul>
-<li>在“正在捕获”界面的“要录制的音频”下选择录制那些音频：游戏、所有、无。</li>
-<li>我选择的是“所有”，包括游戏和已启用麦克风，系统和其他应用程序。</li>
-</ul>
-<ol start="4">
-<li>麦克风</li>
-</ol>
-<ul>
-<li>在右上角的“查看我的捕获”界面，有个麦克风开关。可以根据需要选择，如果录课则关闭，需要录自己声音则打开。</li>
-</ul>
-<h1 id="3-OBS-Studio"><a href="#3-OBS-Studio" class="headerlink" title="3. OBS Studio"></a>3. OBS Studio</h1><h2 id="3-1-OBS-Studio简介"><a href="#3-1-OBS-Studio简介" class="headerlink" title="3.1. OBS Studio简介"></a>3.1. OBS Studio简介</h2><p>OBS Studio是开源软件，完全免费，适用windows，macOS和Liunx系统，可用于视频录制和推流。</p>
-<p>OBS Studio主页：<a href="https://obsproject.com/">https://obsproject.com/</a></p>
-<h2 id="3-2-OBS-Studio使用"><a href="#3-2-OBS-Studio使用" class="headerlink" title="3.2. OBS Studio使用"></a>3.2. OBS Studio使用</h2><p>以录制腾讯会议为例</p>
-<ol>
-<li>打开软件</li>
-<li>在“来源”中添加需要录制的源</li>
-</ol>
-<ul>
-<li>点击“+”号，有非常多种来源可选择：包括“窗口采集”，“显示器采集”，“浏览器”，“游戏源”，“媒体源”等等。</li>
-<li>这里录制腾讯会议，选择“窗口采集”</li>
-<li>弹出创建源窗口：命名来源，点击确定</li>
-<li>弹出属性窗口：在“窗口”中选择“腾讯会议”，“显示鼠标指针”可以根据需要选择，点击确定。</li>
-</ul>
-<ol start="3">
-<li>使源窗口全屏</li>
-</ol>
-<ul>
-<li>添加的源未必刚好覆盖录制的全屏，如果这样开始录制，视频会包含大量的黑框部分。</li>
-<li>可以直接拉伸添加的源外面的红框，使其覆盖全屏录制区域。</li>
-<li>也可以简单地在“编辑”-“变换”中选择“比例适配屏幕”来最大化源窗口使其适配录制屏幕【推荐】，或者选择“拉伸到全屏”来调整源窗口比例，从而实现完全全屏。</li>
-</ul>
-<ol start="4">
-<li>录制</li>
-</ol>
-<ul>
-<li>在“来源”中出现新建的源，选中这个源，右边的“控件”中使用“开始录制”即可开始，“停止录制”即停止。</li>
-</ul>
-<h2 id="3-3-OBS-Studio设置"><a href="#3-3-OBS-Studio设置" class="headerlink" title="3.3. OBS Studio设置"></a>3.3. OBS Studio设置</h2><h3 id="3-3-1-打开“暂停录制”功能"><a href="#3-3-1-打开“暂停录制”功能" class="headerlink" title="3.3.1. 打开“暂停录制”功能"></a>3.3.1. 打开“暂停录制”功能</h3><p>软件默认设置是不支持“暂停录制”，可通过修改设置来打开“暂停录制”的按钮。</p>
-<ol>
-<li>“文件”-“设置”，弹出设置窗口</li>
-<li>“输出”，下面有显示“当录像质量设为与串流画质相同时，无法暂停录制”。所以需要把“录像”-“录像质量”中选择其他的，我这里选择“高质量”。</li>
-<li>“输出”-“输出模式”中选择“高级”，然后选择“录像”，下面有显示“当录像编码器设为使用推流编码器时，无法暂停录制。”所以在“录制设置”-“编码器”中选择其他的，我这里选择“x264”。</li>
-<li>应用和保存设置。</li>
-<li>这时，如果点击“开始录制”，右边会出现一个暂停按钮，暂停功能就打开了。</li>
-</ol>
-<h3 id="3-3-2-输出设置"><a href="#3-3-2-输出设置" class="headerlink" title="3.3.2. 输出设置"></a>3.3.2. 输出设置</h3><ol>
-<li>“文件”-“设置”，弹出设置窗口</li>
-<li>“输出”-“录像”这里可以设置录像的质量，格式，编码器等。</li>
-</ol>
-<h1 id="4-Captura"><a href="#4-Captura" class="headerlink" title="4. Captura"></a>4. Captura</h1><p>Captura也是开源的录屏软件，网上看到很多推荐，界面简单。</p>
-<p>Captura下载：<a href="https://mathewsachin.github.io/Captura/">https://mathewsachin.github.io/Captura/</a></p>
-<p>但是我自己用下来遇到几个问题。</p>
-<ol>
-<li>首先FFmpeg解码器我用不了也无法自动下载，自己下载导入后也用不了，就没继续折腾了。</li>
-<li>选择窗口录屏，这个窗口一定要一直保持在最上方，如果有其他软件覆盖，就会录到其他软件。</li>
-<li>音频录制的选择，要不就没声音，要不就把麦克风录进去，反正没调对。</li>
-</ol>
-<p>应该是挺不错的软件，可能是和我电脑哪里不兼容，还是我没设置对，反正最后弃了。</p>
-<hr>
-<ul>
-<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
-<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
-</ul>
-<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
-      <categories>
-        <category>tools</category>
-      </categories>
-      <tags>
-        <tag>Windows</tag>
-        <tag>screen recorder</tag>
-        <tag>video</tag>
-        <tag>Xbox</tag>
-        <tag>OBS Studio</tag>
-        <tag>Captura</tag>
-      </tags>
-  </entry>
-  <entry>
-    <title>jquery</title>
-    <url>/2018/06/05/web_jquery/</url>
-    <content><![CDATA[<h1 id="1-jQuery基础知识"><a href="#1-jQuery基础知识" class="headerlink" title="1. jQuery基础知识"></a>1. jQuery基础知识</h1><h2 id="1-1-jquery的安装"><a href="#1-1-jquery的安装" class="headerlink" title="1.1. jquery的安装"></a>1.1. jquery的安装</h2><p>只需要在html的script标签中引用jquery，两种方式：</p>
-<ul>
-<li><p>通过CDN（内容分发网络）引用jquery，多个CDN可以使用。比如百度CDN，在html中按如下语法引用。</p>
-<p><code>&lt;script src=&quot;https://apps.bdimg.com/libs/jquery/2.1.4/jquery.min.js&quot;&gt;&lt;/script&gt;</code></p>
-</li>
-<li><p>在<a href="http://jquery.com/download/">jquery.com</a>中下载jquery文本，它是一个js文件，在script标签中引用这个文件即可。<code>&lt;script type=&quot;text/javascript&quot; src=&quot;flexible.js&quot;&gt;&lt;/script&gt;</code></p>
-</li>
+<li><p>通过CDN（内容分发网络）引用jquery，多个CDN可以使用。比如百度CDN，在html中按如下语法引用。</p>
+<p><code>&lt;script src=&quot;https://apps.bdimg.com/libs/jquery/2.1.4/jquery.min.js&quot;&gt;&lt;/script&gt;</code></p>
+</li>
+<li><p>在<a href="http://jquery.com/download/">jquery.com</a>中下载jquery文本，它是一个js文件，在script标签中引用这个文件即可。<code>&lt;script type=&quot;text/javascript&quot; src=&quot;flexible.js&quot;&gt;&lt;/script&gt;</code></p>
+</li>
 </ul>
 <h2 id="1-2-jquery语法"><a href="#1-2-jquery语法" class="headerlink" title="1.2. jquery语法"></a>1.2. jquery语法</h2><h3 id="1-2-1-基础语法"><a href="#1-2-1-基础语法" class="headerlink" title="1.2.1. 基础语法"></a>1.2.1. 基础语法</h3><p><code>$(selector).action()</code></p>
 <ul>
@@ -18250,111 +17905,456 @@ BiocManager的版本与R版本一一对应，安装时如果版本不对会有
       </tags>
   </entry>
   <entry>
-    <title>CSS</title>
-    <url>/2018/06/04/web_CSS/</url>
-    <content><![CDATA[<h1 id="1-CSS基础知识"><a href="#1-CSS基础知识" class="headerlink" title="1. CSS基础知识"></a>1. CSS基础知识</h1><h2 id="1-1-动画的CSS实现"><a href="#1-1-动画的CSS实现" class="headerlink" title="1.1. 动画的CSS实现"></a>1.1. 动画的CSS实现</h2><ol>
-<li><p>transform</p>
-<p>用于元素的2D或3D变化，默认值为none，有六种种变化方式。</p>
+    <title>基因组质量评估：（五）mapping法：7. 用软件mosdepth统计BAM文件的深度</title>
+    <url>/2022/07/31/omics_genome_quality.assessment_mapping_mosdepth/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=1514742&auto=1&height=32"></iframe></div>
+
+<h1 id="1-mosdepth简介"><a href="#1-mosdepth简介" class="headerlink" title="1. mosdepth简介"></a>1. mosdepth简介</h1><p>mosdepth（<a href="https://github.com/brentp/mosdepth%EF%BC%89%E6%98%AF%E7%94%A8%E4%BA%8EWGS%EF%BC%8Cexome%EF%BC%8Ctargeted">https://github.com/brentp/mosdepth）是用于WGS，exome，targeted</a> sequencing的BAM&#x2F;CRAM文件的测序深度计算的软件，主要是用Nim语言写的（第一次听说这种语言）。</p>
+<h1 id="2-mosdepth可以得到的数据包括"><a href="#2-mosdepth可以得到的数据包括" class="headerlink" title="2. mosdepth可以得到的数据包括"></a>2. mosdepth可以得到的数据包括</h1><ol>
+<li>每个碱基深度的计算速度是samtools depth的约2倍。——对于 30X 基因组，大约需要 25 分钟的 CPU 时间。</li>
+<li>给定窗口大小的平均每个窗口深度，可用于 CNV calling。</li>
+<li>给定区域的 BED 文件的每个区域的平均值。</li>
+<li>给定窗口大小的每个区域累积覆盖率（cumulative coverage）直方图的平均值或中值。</li>
+<li>对于每个染色体和全基因组，在给定阈值或以上覆盖的碱基比例分布。</li>
+<li>合并相邻碱基的量化输出，只要它们落在相同的覆盖范围内，例如（10-20）。</li>
+<li>阈值输出以指示在给定阈值下每个区域中有多少个碱基被覆盖。</li>
+<li>每条染色体和每条染色体指定区域内的平均深度的总结。</li>
+<li>一个d4文件（比bigwig好）。</li>
+</ol>
+<h1 id="3-下载安装"><a href="#3-下载安装" class="headerlink" title="3. 下载安装"></a>3. 下载安装</h1><p>直接下载已编译文件，修改权限即可使用。</p>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">wget https://github.com/brentp/mosdepth/releases/download/v0.3.3/mosdepth</span><br><span class="line">chmod +x mosdepth</span><br><span class="line">./mosdepth -h</span><br></pre></td></tr></table></figure>
+
+<h1 id="4-使用"><a href="#4-使用" class="headerlink" title="4. 使用"></a>4. 使用</h1><ol>
+<li>准备</li>
+</ol>
 <ul>
-<li>translate(x,y)、translate3d(x,y,z)位移变化，分别为2d和3d变化方式，衍生出translateX(x)、translateY(y)、translateZ(Z)</li>
-<li>scale(x,y)、scale3d(x,y,z)缩放变化，同样衍生三轴的单独变化方式。</li>
-<li>rotate(angle)、rotate3d(x,y,z,angle)旋转变化，同样衍生三轴的单独变化方式。rotate旋转中心点默认是元素中心，可以通过设置<code>tranform-origin：x-axis y-axis z-axis</code>来改变旋转中心点的位置。</li>
-<li>skew(x-angle,y-angle)沿着x和y轴的2d倾斜变化，衍生两轴单独变化方式。</li>
-<li>matrix(n,n,n,n,n,n)、matrix3d(n,n,n,n,n,n,n,n,n,n,n,n,n,n,n,n)分别定义2d和3d转换，使用6个值或16个值（4*4）的矩阵。</li>
-<li>perspective(n)为3d元素定义透视视图。</li>
+<li><code>samtools index sample.bam</code> # 生成bam文件的索引文件sample.bam.bai</li>
 </ul>
-<p><code>transform-style:flat(default)/preserve-3d</code>指定嵌套元素怎样在三维空间呈现，flat表示所有子元素在2d平面呈现，preserve-3d表示所有子元素在3d空间呈现。</p>
-</li>
-<li><p>transition</p>
-<p><code>transition</code>是<code>transition-property</code>、<code>transition-duration</code>、<code>transition-timing-function</code>和<code>transition-delay</code>四个属性的简写，默认值<code>all 0 ease 0</code>，是指定元素属性发生渐变的一种效果，一定要事件触发。</p>
+<ol start="2">
+<li>计算深度</li>
+</ol>
 <ul>
-<li><code>transition-property:width</code>指定需要渐变变化的css属性，比如宽度的渐变。</li>
-<li><code>transition-duration:xs</code>指定渐变效果的时长为x秒，必须指定，否则为0，等同于没有渐变效果。</li>
-<li><code>transition-timing-function</code>指定transition效果的转速曲线，可取以下值：<br>* linear 规定以相同速度开始至结束的过渡效果（等于 cubic-bezier(0,0,1,1)）。<br>* ease 规定慢速开始，然后变快，然后慢速结束的过渡效果（cubic-bezier(0.25,0.1,0.25,1)）。<br>* ease-in 规定以慢速开始的过渡效果（等于 cubic-bezier(0.42,0,1,1)）。<br>* ease-out 规定以慢速结束的过渡效果（等于 cubic-bezier(0,0,0.58,1)）。<br>* ease-in-out 规定以慢速开始和结束的过渡效果（等于 cubic-bezier(0.42,0,0.58,1)）。<br>* cubic-bezier(n,n,n,n) 在 cubic-bezier 函数中定义自己的值。可能的值是 0 至 1 之间的数值。</li>
-<li><code>transition-delay:xs</code>指定transition效果需要等待x秒才开始。</li>
+<li><code>mosdepth -t 4 out sample.bam</code></li>
 </ul>
-</li>
-<li><p>animation</p>
-<p>通过@keyframes定义动画的每一帧，然后使用animation属性调用动画，animation默认值是<code>none 0 ease 0 1 normal</code>，是多个属性的简写，属性依次如下：</p>
+<ol start="3">
+<li>参数</li>
+</ol>
 <ul>
-<li><code>animation-name</code>在@keyframes中定义的动画名。</li>
-<li><code>animation-duration</code>指定动画的时长。</li>
-<li><code>animation-timing-function</code>指定动画的转速曲线，同<code>transition</code>。</li>
-<li><code>animation-delay</code>指定动画在启动前的延迟间隔，可为负值，负值时直接跳过动画的制定时间开始播放。</li>
-<li><code>animation-iteration-count</code>指定动画播放次数，值为<code>infinite</code>时无限多次播放。</li>
-<li><code>animation-direction</code>指定是否轮流反向播放动画。</li>
-<li><code>animation-fill-mode</code>规定当动画不播放时（当动画完成时，或当动画有一个延迟未开始播放时），要应用到元素的样式。</li>
-<li><code>animation-play-state</code>指定动画是否正在运行或已暂停。</li>
+<li>-t 4：线程，需要&lt;&#x3D;4</li>
+<li>out：输出文件的前缀</li>
+<li>sample.bam：待分析的bam文件</li>
+<li>–by sample.bed：指定区域的bed文件，我分析整个基因组，没加这个参数。</li>
 </ul>
-</li>
-</ol>
-<p><strong>@keyframes语法</strong></p>
-<p>@keyframes myanimation{}</p>
-<ol>
-<li>tips</li>
-</ol>
-<p>配合hover等事件来实现动画效果。</p>
-<h3 id="1-1-1-几个要点"><a href="#1-1-1-几个要点" class="headerlink" title="1.1.1. 几个要点"></a>1.1.1. 几个要点</h3><ol>
-<li>class属性</li>
+<p>还有许多参数等着探索…</p>
+<h1 id="5-结果"><a href="#5-结果" class="headerlink" title="5. 结果"></a>5. 结果</h1><ol>
+<li>out.mosdepth.summary.txt</li>
 </ol>
 <ul>
-<li>元素在设置样式时，首先考虑是否有多个相同元素具有同一样式和是否复用的问题，如果单个元素使用的样式或者不会复用，则优先使用内联样式，在html中就实现。</li>
-<li>如果需要在css文件中实现的样式，则尽量使用class属性，使得具有同样样式的元素使用同一class属性，减少css的冗余。</li>
-<li>对class属性的命明尽量语意化。</li>
+<li>文件包含每条染色体和整个基因组的信息，长度，mapped 碱基数量，平均深度，最小深度和最大深度。示例：</li>
 </ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">chrom	length	bases	mean	min	max</span><br><span class="line">MCscaf001	12541575	440858440	35.15	0	3561</span><br><span class="line">MCscaf002	20211832	749371193	37.08	0	8181</span><br><span class="line">... ...</span><br><span class="line">MCscaf265	25000	897138	35.89	0	84</span><br><span class="line">MCscaf266	25000	913514	36.54	0	82</span><br><span class="line">total	256218469	10404013912	40.61	0	92318</span><br></pre></td></tr></table></figure>
+
 <ol start="2">
-<li>初始化</li>
+<li>out.mosdepth.global.dist.txt</li>
 </ol>
 <ul>
-<li>使用<strong>reset.css</strong>或者<strong>normalize.css</strong>来初始化，使得css设置更轻松。或者在css文档开头，简单地用<code>*&#123;margin:0;padding:0&#125;</code>来初始化所有元素的边距，因为某些元素（例如h）自带一些边距。</li>
-<li>使用<strong>felxible.js</strong>来在css中可以使用<strong>rem</strong>（为显示设备宽度的十分之一）来作为长度单位，更多地应用在移动端。</li>
+<li>文件包含累积分布，指示给定覆盖率阈值下覆盖的总碱基的比例。包含三列：染色体&#x2F;total，覆盖水平，该级别覆盖的碱基比例。示例：</li>
 </ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">MCscaf001	1961	0.00</span><br><span class="line">MCscaf001	1960	0.00</span><br><span class="line">MCscaf001	1957	0.00</span><br><span class="line">... ...</span><br><span class="line">total	2	0.99</span><br><span class="line">total	1	1.00</span><br><span class="line">total	0	1.00</span><br></pre></td></tr></table></figure>
+
+<p>还可以用脚本<code>python scripts/plot-dist.py \*global.dist.txt</code>画图，输出<code>dist.html</code>，可以看出整个基因组的覆盖度的分布。</p>
 <ol start="3">
-<li>border-width</li>
+<li>out.per-base.bed.gz</li>
 </ol>
-<p>参考<a href="https://www.jianshu.com/p/63fdbd7fdc9b">简书</a>的这个总结和<a href="https://www.imooc.com/video/6858">慕课网</a>的这个使用border-width制作折角效果的视频。border-width实现三角形等图形很方便。</p>
-<p>当content区为空（width和height都为0）时，四个方向的border分别为三角形，当四个方向的border-width都不为0时，组成一个矩形。</p>
-<p>没有定义基本border时，未被定义的边那个方向的矩形的一半不会显示，并影响相邻边的显示。所以，若只定义了对边的边框，则相当于四个边框都未被定义。<br>当定义了基本border，即使是白色或透明，在此基础上再定义边框时，则会安装定义的边框的三角形的组合来显示。</p>
-<h2 id="1-2-问题笔记"><a href="#1-2-问题笔记" class="headerlink" title="1.2. 问题笔记"></a>1.2. 问题笔记</h2><ol>
-<li>任意数量图片从左到右按顺序排列，每行预期排五个，图片间有相同间距的样式的实现。<br>Q:解决分散排列，并适用于不同数量图片的问题。<br>如果直接定义父容器的<code>display:flex</code>和<code>justify-content:space-between</code>可以实现图片分散排列，但是当图片数量不是5时（比如为3时），也会展示分散排列的效果，而非占据左边2&#x2F;5的位置。<br>A：定义父容器的<code>display:flex</code>和子元素图片<code>width:20%</code>即可实现。<br>Q:解决图片排列紧凑的问题。<br>A:定义子元素图片<code>box-sizing:border-box;padding:1%;</code>来实现图片间隙。</li>
-<li>实现任意元素单行排列，不换行，超出省略并显示省略号。<br>Q：强制不换行<br>A：<code>white-space:nowrap;</code><br>Q:超出省略<br>A：<code>overflow: hidden;</code><br>Q：超出显示省略号<br>A：<code>text-overflow: ellipsis;</code></li>
+<ul>
+<li>每个碱基的输出数据。</li>
+</ul>
+<ol start="4">
+<li>out.per-base.bed.gz.csi</li>
 </ol>
-<h2 id="1-3-tips"><a href="#1-3-tips" class="headerlink" title="1.3. tips"></a>1.3. tips</h2><ul>
-<li>公司项目少有使用<strong>响应式</strong>CSS，而是移动端和web端各一套CSS，以使得网页在不同设备的展示更和谐，主要是长度设置、移动端键盘弹出遮盖内容区、移动端触摸屏更多操作等问题。</li>
-<li>尽量不要设定元素的<code>height</code>，使其保持auto。</li>
-<li>一些icon、line和矩形可以使用<code>div</code>或者<code>:before :after</code>来实现。</li>
-<li>使用<code>border-randius</code>属性来控制矩形或者圆形。</li>
-<li><code>white-space</code>属性指定元素内的空白怎么处理，文本元素取值<code>nowrap</code>时，文本强制不换行，直到遇到<br>元素。</li>
-<li>使用图片作为背景时，一般设置<code>background-position:center</code>来使图片居中展示，当图片与背景尺寸不匹配时，图片仍然居中。</li>
+<h1 id="6-references"><a href="#6-references" class="headerlink" title="6. references"></a>6. references</h1><ol>
+<li><a href="https://github.com/brentp/mosdepth">https://github.com/brentp/mosdepth</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
 </ul>
-]]></content>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
       <categories>
-        <category>web</category>
+        <category>omics</category>
+        <category>genome</category>
+        <category>quality assessment</category>
       </categories>
       <tags>
-        <tag>CSS</tag>
+        <tag>biosoft</tag>
+        <tag>genome</tag>
+        <tag>quality assessment</tag>
+        <tag>mapping</tag>
+        <tag>sam</tag>
+        <tag>bam</tag>
+        <tag>depth</tag>
+        <tag>mosdepth</tag>
       </tags>
   </entry>
   <entry>
-    <title>ES6</title>
-    <url>/2018/06/12/web_ES6/</url>
-    <content><![CDATA[<h2 id="常量"><a href="#常量" class="headerlink" title="常量"></a>常量</h2><p>const pi &#x3D; 3.1415926<br>常量是只读的，不可修改。</p>
-<h2 id="作用域"><a href="#作用域" class="headerlink" title="作用域"></a>作用域</h2><p>ES6中有块级作用域<br>const、let，在每次循环内都有自己的闭包和作用域。<br>{}内就是块级作用域。</p>
-<h2 id="箭头函数"><a href="#箭头函数" class="headerlink" title="箭头函数"></a>箭头函数</h2><ol>
-<li>语法：(参数)&#x3D;&gt;{函数体}<br>当只有一个参数时，可省略()；当函数体只有return something时，可以省略{}。eg：i&#x3D;&gt;i+1</li>
-<li>箭头函数的this是定义该名称时this的指向</li>
+    <title>用miniprot做蛋白序列和基因组序列间的比对</title>
+    <url>/2024/09/30/bioinfo_alignment_prot_miniprot/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=397952&auto=1&height=32"></iframe></div>
+
+<h1 id="1-miniprot介绍"><a href="#1-miniprot介绍" class="headerlink" title="1. miniprot介绍"></a>1. miniprot介绍</h1><p>需要进行蛋白序列与基因组序列的比对，可以用李恒团队开发的miniprot。</p>
+<h1 id="2-miniprot安装"><a href="#2-miniprot安装" class="headerlink" title="2. miniprot安装"></a>2. miniprot安装</h1><ol>
+<li>conda安装：<code>conda install miniprot</code></li>
+<li>下载安装：仅依赖zlib</li>
 </ol>
-<h2 id="默认参数"><a href="#默认参数" class="headerlink" title="默认参数"></a>默认参数</h2><p>function func(x&#x3D;1,y&#x3D;5,z&#x3D;20){<br>return x+y+z<br>}</p>
-<h3 id="可变参数"><a href="#可变参数" class="headerlink" title="可变参数"></a>可变参数</h3><p>不知数量参数的求和<br>function func(…a){<br>a.forEach(item&#x3D;&gt;{<br>sum+&#x3D;item*1<br>});<br>return sum<br>}</p>
-<h3 id="利用扩展运算符合并数组"><a href="#利用扩展运算符合并数组" class="headerlink" title="利用扩展运算符合并数组"></a>利用扩展运算符合并数组</h3><p>var params &#x3D; [‘hello’,true,1,23]<br>var concatlist &#x3D; [1,2,’world’,…params]<br>相当于concatlist为[1,2,’world’,’hello’,true,1,23]</p>
-<h2 id="对象代理"><a href="#对象代理" class="headerlink" title="对象代理"></a>对象代理</h2><p>私有属性<br>let Person &#x3D; {<br>name: ‘karen’,<br>sex: ‘female’,<br>age: 18<br>}</p>
-<p>let person &#x3D; new Proxy(Person,{<br>get(target,key){<br>return target[key]<br>},<br>set(target,key,value){<br>if(key!&#x3D;&#x3D;’sex’){<br>target[key]&#x3D;value;<br>}<br>}<br>})<br>通过Proxy对象代理实现保护Person的sex属性，使其不能修改。</p>
-]]></content>
-      <categories>
-        <category>web</category>
-      </categories>
-      <tags>
-        <tag>ES6</tag>
+<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">git clone https://github.com/lh3/miniprot</span><br><span class="line">cd miniprot &amp;&amp; make</span><br></pre></td></tr></table></figure>
+
+<h1 id="3-miniprot使用"><a href="#3-miniprot使用" class="headerlink" title="3. miniprot使用"></a>3. miniprot使用</h1><p>基因组genome.fna和蛋白序列protein.faa进行比对</p>
+<h2 id="3-1-先建索引，再比对【推荐】"><a href="#3-1-先建索引，再比对【推荐】" class="headerlink" title="3.1. 先建索引，再比对【推荐】"></a>3.1. 先建索引，再比对【推荐】</h2><ol>
+<li>建立索引：会将基因组genome.fna翻译成氨基酸序列，费时。</li>
+</ol>
+<ul>
+<li><code>miniprot -d genome.mpi genome.fna</code></li>
+</ul>
+<ol start="2">
+<li>比对：翻译的基因组氨基酸序列与指定的蛋白序列进行比对</li>
+</ol>
+<p>-<code>miniprot --gff -It16 --gff genome.mpi protein.faa &gt; aln.gff</code></p>
+<h2 id="3-2-建索引和比对一步完成"><a href="#3-2-建索引和比对一步完成" class="headerlink" title="3.2. 建索引和比对一步完成"></a>3.2. 建索引和比对一步完成</h2><ul>
+<li><code>miniprot -Iut16 --gff genome.fna protein.faa &gt; aln.gff</code></li>
+</ul>
+<h1 id="references"><a href="#references" class="headerlink" title="references"></a>references</h1><ol>
+<li>github：<a href="https://github.com/lh3/miniprot">https://github.com/lh3/miniprot</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>alignment</category>
+        <category>prot</category>
+      </categories>
+      <tags>
+        <tag>miniprot</tag>
+        <tag>align</tag>
+      </tags>
+  </entry>
+  <entry>
+    <title>用PseudogenePipeline鉴定假基因及后续分析</title>
+    <url>/2022/09/07/bioinfo_pseudogene_PseudogenePipeline/</url>
+    <content><![CDATA[<div align="middle"><iframe frameborder="no" border="0" marginwidth="0" marginheight="0" width=298 height=52 src="//music.163.com/outchain/player?type=2&id=95370&auto=1&height=32"></iframe></div>
+
+<h1 id="1-假基因"><a href="#1-假基因" class="headerlink" title="1. 假基因"></a>1. 假基因</h1><p>假基因(pseudogene)是类似与功能基因的非功能性DNA片段。大多数是功能基因的多余拷贝，通常是由于移码突变或提前的终止密码子被鉴定为假基因。</p>
+<p>由于可能含有启动子元件，假基因有可能产生低水平的转录生成RNA，但通常无法产生功能性的最终蛋白产物。</p>
+<h1 id="2-PseudogenePipeline-简介"><a href="#2-PseudogenePipeline-简介" class="headerlink" title="2. PseudogenePipeline 简介"></a>2. PseudogenePipeline 简介</h1><p>PseudogenePipeline是Shiulab开发的一个鉴定假基因的流程包，v1.0.0版本基于python2，v2.0.0版本基于python3。</p>
+<h1 id="3-PseudogenePipeline-原理"><a href="#3-PseudogenePipeline-原理" class="headerlink" title="3. PseudogenePipeline 原理"></a>3. PseudogenePipeline 原理</h1><p>在基因组未被注释到基因的区域（基因间隔区）查找与基因相似的片段，大多是没有完整ORF区域或者基因的片段。使用tblastn进行基因转录翻译生成的氨基酸序列和基因组上基因间隔区的比对。</p>
+<h1 id="4-PseudogenePipeline-安装"><a href="#4-PseudogenePipeline-安装" class="headerlink" title="4. PseudogenePipeline 安装"></a>4. PseudogenePipeline 安装</h1><h2 id="4-1-依赖-requirements"><a href="#4-1-依赖-requirements" class="headerlink" title="4.1. 依赖-requirements"></a>4.1. 依赖-requirements</h2><ol>
+<li>python</li>
+<li>RepeatMasker</li>
+<li>tfasty：是fasta36包的一部分，version 36及以上 （<a href="https://faculty.virginia.edu/wrpearson/fasta/%EF%BC%89%EF%BC%8C%E5%AE%9E%E6%B5%8B%E7%94%A8%E7%9A%84fasta-36.3.8h%E7%89%88%E6%9C%AC%E3%80%82%E4%B8%8B%E8%BD%BD%E8%A7%A3%E5%8E%8B%E5%8D%B3%E5%8F%AF%E3%80%82">https://faculty.virginia.edu/wrpearson/fasta/），实测用的fasta-36.3.8h版本。下载解压即可。</a></li>
+</ol>
+<h2 id="4-2-下载"><a href="#4-2-下载" class="headerlink" title="4.2. 下载"></a>4.2. 下载</h2><p>在 <a href="https://github.com/ShiuLab/PseudogenePipeline">https://github.com/ShiuLab/PseudogenePipeline</a> 直接下载PseudogenePipeline，是python脚本包，无需安装即可使用。</p>
+<h1 id="5-PseudogenePipeline-使用"><a href="#5-PseudogenePipeline-使用" class="headerlink" title="5. PseudogenePipeline 使用"></a>5. PseudogenePipeline 使用</h1><h2 id="5-1-文件准备"><a href="#5-1-文件准备" class="headerlink" title="5.1. 文件准备"></a>5.1. 文件准备</h2><p>文件包括：</p>
+<ol>
+<li>genome.fa：基因组文件，建议用repeats.gff为基因组的重复序列做mask后使用genome.repeatmask.fa，以减少计算量</li>
+<li>sample.protein.fa：注释的基因对应的蛋白组</li>
+<li>tblastn.out：genome.repeatmask.fa和sample.protein.fa进行tblastn获得的输出文件</li>
+<li>sample.gff3：基因组的蛋白编码基因注释文件，有具体的格式要求</li>
+</ol>
+<h3 id="5-1-1-genome-mask文件"><a href="#5-1-1-genome-mask文件" class="headerlink" title="5.1.1. genome mask文件"></a>5.1.1. genome mask文件</h3><p>用注释的重复序列repeats.gff为基因组做genome mask，把重复序列所在位置的序列替换成N，减少假基因注释的计算量。获得masked的基因组文件genome.repeatmask.fa。</p>
+<h3 id="5-1-2-tblastn-out文件"><a href="#5-1-2-tblastn-out文件" class="headerlink" title="5.1.2. tblastn.out文件"></a>5.1.2. tblastn.out文件</h3><p>用<code>tblastn</code>为mask的基因组和注释的基因组蛋白序列做比对，比对结果用作PseudogenePipeline的输入。</p>
+<ol>
+<li>运行</li>
+</ol>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">makeblastdb -in genome.repeatmask.fa -dbtype nucl -out sample # 建库</span><br><span class="line">tblastn -db dample -query sample.proteins.fa -num_threads 4 -out tblastn.out -outfmt 6 # tblastn比对，输出tblastn.out</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>notes</li>
+</ol>
+<ul>
+<li>可能由于基因组大(6G)，多线程会中断，只能单线程，可以把蛋白文件sample.proteins.fa切分成多份，分别单线程tblastn，然后把结果合并。</li>
+<li>注意蛋白文件和基因组文件的序列id不能有空格，否则自动去除空格和空格后内容。</li>
+</ul>
+<h3 id="5-1-3-sample-gff3文件"><a href="#5-1-3-sample-gff3文件" class="headerlink" title="5.1.3. sample.gff3文件"></a>5.1.3. sample.gff3文件</h3><p>gff3文件格式类似普通的gff3格式，共九列。但有很具体的要求，主要是对第九列内容的要求，可参照软件给的例子进行增添修改以符合要求。</p>
+<p>具体格式要求：</p>
+<ul>
+<li>gene行，第九列包含<code>Name=信息</code>，值可与<code>ID=信息</code>一致</li>
+<li>mRNA行，第九列包含<code>longest=1</code>或者<code>longest=0</code>信息，用于可变剪切筛选，1代表最长可变剪切，0代表非最长可变剪切。</li>
+<li>mRNA,exon,CDS,UTR行，第九列都包含<code>pacid=信息</code>，值可与<code>Parent=信息</code>一致，一个gene内部的所有pacid值应相同。</li>
+</ul>
+<h2 id="5-2-用PseudogenePipeline鉴定假基因"><a href="#5-2-用PseudogenePipeline鉴定假基因" class="headerlink" title="5.2. 用PseudogenePipeline鉴定假基因"></a>5.2. 用PseudogenePipeline鉴定假基因</h2><ol>
+<li>参数文件parameter_file示例</li>
+</ol>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">b_out=/path/to/tblastn.out # 前面准备的tblastn.out文件，&lt;tblastn_output_file&gt;</span><br><span class="line">p_seq=/path/to/sample.proteins.fa # 蛋白序列，&lt;protein_sequence_FASTA_file&gt;</span><br><span class="line">g_seq=/genome.repeatmask.fa # 基因组序列（最好是mask过的） &lt;genome_sequence_FASTA_File&gt;</span><br><span class="line">b_filter=y # 是否做blastout的filter，&lt;filter blastouput? y/n&gt;</span><br><span class="line">b_data=tabular # &lt;define type of blastdata: tabular, blastall, blastplus&gt;</span><br><span class="line">p_codes=/path/to/PseudogenePipeline-master/_pipeline_scripts #&lt;path to ../_pipeline_scripts&gt;</span><br><span class="line">o_codes=/path/to/PseudogenePipeline-master/_pipeline_scripts #&lt;path to ../_pipeline_scripts&gt;</span><br><span class="line">f_dir=/path/to/fasta-36.3.8h/bin #&lt;path to FASTA package install&gt;</span><br><span class="line">f_prog=tfasty36 #&lt;tfasty program name&gt;</span><br><span class="line">blosum=/path/to/PseudogenePipeline-master/_example_files/blosum50.matrix #&lt;path to blosum matrilx file&gt;</span><br><span class="line">gff=/path/to/sample.gff3 # gff3注释文件 &lt;gff_file&gt;</span><br><span class="line">repCut=300 #&lt;RepeatMasker cutoff; integer, recommended 300&gt;</span><br><span class="line">repDiv=30 #&lt;RepeatMakser divergence; integer, recommended 30&gt;</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>运行</li>
+</ol>
+<ul>
+<li><code>python2 PseudogenePipeline-master/_wrapper_scripts/CombinedPseudoWrapper.py parameter_file</code></li>
+</ul>
+<h2 id="5-3-PseudogenePipeline运行结果"><a href="#5-3-PseudogenePipeline运行结果" class="headerlink" title="5.3. PseudogenePipeline运行结果"></a>5.3. PseudogenePipeline运行结果</h2><h3 id="5-3-1-结果文件"><a href="#5-3-1-结果文件" class="headerlink" title="5.3.1. 结果文件"></a>5.3.1. 结果文件</h3><p>在生成的结果目录result下，保存了结果文件，包括三个文件夹。result&#x2F;_intermediate保存着中间文件，result&#x2F;_logs保存着log文件，result&#x2F;_results保存着高置信并通过RepeatMasker过滤的鉴定出来的假基因结果文件。</p>
+<h4 id="5-3-1-1-results文件夹中的文件"><a href="#5-3-1-1-results文件夹中的文件" class="headerlink" title="5.3.1.1. _results文件夹中的文件"></a>5.3.1.1. _results文件夹中的文件</h4><p>result&#x2F;_results中的结果文件包括以下文件，每个文件第一行都是以<code>#python</code>开始的生成此文件的命令，其余行才保存了假基因的信息：</p>
+<ol>
+<li>sample.4col.true.RMfilt.hiConf</li>
+</ol>
+<ul>
+<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
+<li>其余行是假基因和位置信息，共四列。第一列是假基因对应的真基因的ID（gene38168）加上假基因位置信息（ctg947|625093-625785）加上其他比对信息，分号分隔；第二列是染色体ID；第三第四列是start和end的坐标。</li>
+<li><code>gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0</code>表示在染色体<code>ctg947</code>的<code>625093-625785</code>位置有一个假基因pseudogene。</li>
+<li>对应的真基因是<code>gene38168</code>；<code>336-566</code>和<code>693-4</code>分别是指真基因序列和假基因序列比对上的区域；比对的信息是从tblastn的比对结果中获取的。</li>
+<li>真基因<code>gene38168</code>的<strong>氨基酸</strong>序列的<code>336-566</code>位置。</li>
+<li>假基因的<code>693-4</code>位置是指在染色体<code>ctg947</code>的<code>625093-625785</code>位置的假基因核酸序列上的第4到693个碱基，由于起始位置比终止位置大，所以是4-693位置的反向互补序列。</li>
+<li>下面是文件的部分内容。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0	ctg947	625785	625096</span><br><span class="line">gene04393;ctg8706|272998-273192;228-290:190-4;2|0|1|0	ctg8706	273187	273001</span><br><span class="line">gene10546;ctg5903|105682-105807;115-156:126-4;1|0|0|0	ctg5903	105807	105685</span><br></pre></td></tr></table></figure>
+
+<ol start="2">
+<li>sample.4col.true.RMfilt.hiConf.cdnm</li>
+</ol>
+<ul>
+<li>同文件sample.4col.true.RMfilt.hiConf的内容基本一致，区别在于第一列的内容，这里是假基因ID（Ps000001）和对应的真基因ID（gene38168）。</li>
+<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
+<li>其余行是假基因和位置信息，共四列。</li>
+<li>下面是文件的部分内容。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Ps000001_gene38168	ctg947	625785	625096</span><br><span class="line">Ps000003_gene04393	ctg8706	273187	273001</span><br><span class="line">Ps000004_gene10546	ctg5903	105807	105685</span><br></pre></td></tr></table></figure>
+
+<ol start="3">
+<li>sample.4col.true.RMfilt.hiConf.cdnm.gff</li>
+</ol>
+<ul>
+<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
+<li>其余行是假基因的注释信息，gff格式，共九列。</li>
+<li>下面是文件的部分内容。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">ctg947	ShiuLab	pseudogene	625096	625785	.	-	.	ID=Ps000001;Name=Ps000001_gene38168;Derives_from=gene38168;Note=pseudogene_evidence_code_4,0,0,0</span><br><span class="line">ctg8706	ShiuLab	pseudogene	273001	273187	.	-	.	ID=Ps000003;Name=Ps000003_gene04393;Derives_from=gene04393;Note=pseudogene_evidence_code_2,0,1,0</span><br><span class="line">ctg5903	ShiuLab	pseudogene	105685	105807	.	-	.	ID=Ps000004;Name=Ps000004_evm.model.ctg10546.26;Derives_from=gene10546;Note=pseudogene_evidence_code_1,0,0,0</span><br></pre></td></tr></table></figure>
+
+<ol start="4">
+<li>sample.4col.true.fa.RMfilt.hiConf</li>
+</ol>
+<ul>
+<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
+<li>其余行是假基因的序列信息，fasta格式，序列ID是假基因对应的真基因的ID（gene38168）加上假基因位置信息（ctg947|625093-625785）加上其他比对信息，分号分隔。序列ID同文件sample.4col.true.RMfilt.hiConf的第一列内容一致。</li>
+<li>下面是文件的部分内容。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&gt;gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0</span><br><span class="line">TTGCAGGTTGCAATTTTGATGTCCAAGGACCAAGCACTATATTACGCATGGTTGTTCTCCCTAATAATTTATGAATTACTTAGAAAACCTACCTTGCGCAATGTAGAAGCGTTAGTGCAAACATGGCTCCGTGTCACACAATGCATGTCACGGTCAATTAACTTTAAGAAGGTAGCAAATCGTAAAAGGAGTTGGGAGCGTAGGAGAGCATGAGCTTATCCATGACCCCAGAGGTTTCTTGAGTGCTTTTTGTTGAGTGCATATAATGCAAACATGTTCAAATGTCGCTTGTGAGTGAGTAGAGAAACCTTCCAATACGTATGTGGCAAGGTCTCTCTTGCAATGAAGAAGGTGAGCACAAACATGCGTGTTCCACTTAGTATGGAAACAAAACTGGCAATAGCTCTCAGTAGGCTAGCTTTAGGTGCAGGGCTTGAGATGTTGGGTGGTTTGCATGGCTGTGCAAAGAGCACAAGTTGTAAAGTAGTGATTGATTTTTGTAAGGCAATTGTTACATCAGGTCTTAGGGATTTGTACATTAGGTGGCCTGCCCTCTCATGGCTAGAGACACTAGCAAGTGAGTTTCAAGCTTCAAGATGCATCCCCTTTGTTGCTAGAGCCATAGACAGTTCTCACATCCCTATCATCGCCCCAAGAGACAATCACGTAGATTATTTCAATTGAAAAGGG</span><br><span class="line">&gt;gene04393;ctg8706|272998-273192;228-290:190-4;2|0|1|0</span><br><span class="line">TATATGTCGTGATATCACAGCAATCAATCTATCATTAGAGAACTTAGATGATTTGTATAAGGTGTGTGGGTTCATACATGGCTTGGACATGAAGTACAAGCACAACATACGTACTCAAAACCCCAAAGACCCCATAGATGCTATTTAAGGTTCTCAAATCTATAATGACTCTCTTGATAAGAGGAGC</span><br><span class="line">&gt;gene10546;ctg5903|105682-105807;115-156:126-4;1|0|0|0</span><br><span class="line">GGCATGGATGATGACCAATTTCTTTCTTTCTTGGACCTCAAGCAGAGGAGGTGGTATGAGGACCGCCATTGCTCCAAAGCATAGTCACGTTGTTCCACTCCTTCAGAGTCTCCAACTGCTTTG</span><br></pre></td></tr></table></figure>
+
+<ol start="5">
+<li>sample.4col.true.fa.RMfilt.hiConf.cdnm</li>
+</ol>
+<ul>
+<li>第一行<code>#python</code>开头的是生成此文件运行的命令。</li>
+<li>其余行是假基因的序列信息，fasta格式，序列ID是假基因ID（Ps000001）和对应的真基因ID（gene38168）。序列ID同文件sample.4col.true.RMfilt.hiConf.cdnm的第一列内容一致。</li>
+<li>下面是文件的部分内容。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">&gt;Ps000001_gene38168</span><br><span class="line">TTGCAGGTTGCAATTTTGATGTCCAAGGACCAAGCACTATATTACGCATGGTTGTTCTCCCTAATAATTTATGAATTACTTAGAAAACCTACCTTGCGCAATGTAGAAGCGTTAGTGCAAACATGGCTCCGTGTCACACAATGCATGTCACGGTCAATTAACTTTAAGAAGGTAGCAAATCGTAAAAGGAGTTGGGAGCGTAGGAGAGCATGAGCTTATCCATGACCCCAGAGGTTTCTTGAGTGCTTTTTGTTGAGTGCATATAATGCAAACATGTTCAAATGTCGCTTGTGAGTGAGTAGAGAAACCTTCCAATACGTATGTGGCAAGGTCTCTCTTGCAATGAAGAAGGTGAGCACAAACATGCGTGTTCCACTTAGTATGGAAACAAAACTGGCAATAGCTCTCAGTAGGCTAGCTTTAGGTGCAGGGCTTGAGATGTTGGGTGGTTTGCATGGCTGTGCAAAGAGCACAAGTTGTAAAGTAGTGATTGATTTTTGTAAGGCAATTGTTACATCAGGTCTTAGGGATTTGTACATTAGGTGGCCTGCCCTCTCATGGCTAGAGACACTAGCAAGTGAGTTTCAAGCTTCAAGATGCATCCCCTTTGTTGCTAGAGCCATAGACAGTTCTCACATCCCTATCATCGCCCCAAGAGACAATCACGTAGATTATTTCAATTGAAAAGGG</span><br><span class="line">&gt;Ps000003_gene04393</span><br><span class="line">TATATGTCGTGATATCACAGCAATCAATCTATCATTAGAGAACTTAGATGATTTGTATAAGGTGTGTGGGTTCATACATGGCTTGGACATGAAGTACAAGCACAACATACGTACTCAAAACCCCAAAGACCCCATAGATGCTATTTAAGGTTCTCAAATCTATAATGACTCTCTTGATAAGAGGAGC</span><br><span class="line">&gt;Ps000004_gene10546</span><br><span class="line">GGCATGGATGATGACCAATTTCTTTCTTTCTTGGACCTCAAGCAGAGGAGGTGGTATGAGGACCGCCATTGCTCCAAAGCATAGTCACGTTGTTCCACTCCTTCAGAGTCTCCAACTGCTTTG</span><br></pre></td></tr></table></figure>
+
+<h4 id="5-3-1-2-intermediate文件夹中的文件"><a href="#5-3-1-2-intermediate文件夹中的文件" class="headerlink" title="5.3.1.2. _intermediate文件夹中的文件"></a>5.3.1.2. _intermediate文件夹中的文件</h4><p><code>result/_intermediate</code>中的结果文件包括以下文件，许多文件第一行都是以<code>#python</code>开始的生成此文件的命令，其余行才保存了信息：</p>
+<ol>
+<li>sample.fullyFiltered.disable_count</li>
+</ol>
+<ul>
+<li>第一行都是以<code>#python</code>开始的生成此文件的命令</li>
+<li>保存着假基因和对应真基因的氨基酸的比对好的序列</li>
+</ul>
+<ol start="2">
+<li>sample.fullyFiltered.disable_count.RMfilt</li>
+</ol>
+<ul>
+<li>前两行都是以<code>#python</code>开始的生成此文件的命令</li>
+<li>保存着假基因和对应真基因的氨基酸的比对好的序列</li>
+</ul>
+<h3 id="5-3-2-notes"><a href="#5-3-2-notes" class="headerlink" title="5.3.2. notes"></a>5.3.2. notes</h3><ul>
+<li>注释到的假基因之间是没有重叠的，一个位置的序列只能被注释为一个假基因。</li>
+<li>建议做一个长度的过滤，比如长度大于300bp的假基因才被鉴定为假基因，进入到下一步的分析流程。</li>
+<li>result&#x2F;_intermediate文件夹中保存着额外的文件，比如sample.fullyFiltered.disable_count.RMfilt文件保存着假基因和对应真基因比对好的蛋白质序列。</li>
+</ul>
+<h2 id="5-4-PseudogenePipeline运行的一个例子"><a href="#5-4-PseudogenePipeline运行的一个例子" class="headerlink" title="5.4. PseudogenePipeline运行的一个例子"></a>5.4. PseudogenePipeline运行的一个例子</h2><p>运行相关数据：</p>
+<ul>
+<li>6Gb大小的蕨类基因组，跑了70h。</li>
+<li>70000个注释的真基因，软件结果中有222,372个假基因，其中长度大于300bp的为93594条。</li>
+</ul>
+<h1 id="6-假基因后续分析"><a href="#6-假基因后续分析" class="headerlink" title="6. 假基因后续分析"></a>6. 假基因后续分析</h1><p>鉴定假基因后，可以通过进一步的分析来获取更多认识。</p>
+<h2 id="6-1-批量计算pseudogene和功能性gene之间的Ks"><a href="#6-1-批量计算pseudogene和功能性gene之间的Ks" class="headerlink" title="6.1. 批量计算pseudogene和功能性gene之间的Ks"></a>6.1. 批量计算pseudogene和功能性gene之间的Ks</h2><p>通过计算pseudogene和功能性基因（真基因）间的Ks，可以判断是否与其他进化事件的Ks重合（比如WGD），也可以通过Ks计算假基因产生的时间的分布。</p>
+<h3 id="6-1-1-背景"><a href="#6-1-1-背景" class="headerlink" title="6.1.1. 背景"></a>6.1.1. 背景</h3><ol>
+<li>Ks只能计算ORF区域的（即完整密码子，3的倍数的碱基），并遇到stop codon停止。</li>
+<li>根据假基因的注释信息，找到pseudogene和对应真基因的序列，并把pseudogene和对应真基因的ORF区域提取出来。</li>
+<li>若pseudogene比对到了intron区域，删除这部分，并从真基因密码子首位开始匹配pseudogene，提取完整密码子（3的倍数个碱基）。</li>
+<li>只需要比对上的密码子区域，并要求3的倍数的碱基；而假基因常常包含许多中间的终止密码子，所以这些中间终止密码子和对应位置的真基因的三个碱基也需要删除。（还有一种策略是从第一个终止密码子处截断，之后的序列全都不要，但这种策略常常会使得提取出来的序列太短，造成更大误差，所以不推荐。）</li>
+</ol>
+<p>准备以下三个输入文件，就可以根据 <strong>博客：批量计算Ka和Ks：<a href="https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks">https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks</a></strong> 进行Ks的批量计算。</p>
+<h3 id="6-1-2-准备sample-pairs文件"><a href="#6-1-2-准备sample-pairs文件" class="headerlink" title="6.1.2. 准备sample.pairs文件"></a>6.1.2. 准备sample.pairs文件</h3><p>sample.pairs保存着两列数据，tab分隔，第一列是假基因的ID，第二列是对应的真基因的ID。</p>
+<p>可以用下面的命令从结果文件中提取假基因和对应的真基因的ID，获得sample.pairs。</p>
+<p><code>cat ./_results/sample.4col.true.RMfilt.hiConf.cdnm  |grep -v &quot;#&quot;|sed -e &quot;s/\t.*//g&quot; -e &quot;s/_/\t/&quot; &gt;sample.pairs </code></p>
+<h3 id="6-1-3-准备用于计算Ks的CDS序列文件-all-cds-和氨基酸序列文件-all-pep"><a href="#6-1-3-准备用于计算Ks的CDS序列文件-all-cds-和氨基酸序列文件-all-pep" class="headerlink" title="6.1.3. 准备用于计算Ks的CDS序列文件(all.cds)和氨基酸序列文件(all.pep)"></a>6.1.3. 准备用于计算Ks的CDS序列文件(all.cds)和氨基酸序列文件(all.pep)</h3><p>用于计算Ks的CDS序列文件需要包括所有假基因和对应真基因的CDS序列，用于计算Ks的氨基酸序列文件需要包括所有假基因和对应真基因的氨基酸序列。</p>
+<p>注意：all.cds和all.pep可以包含多余的序列但不能少。因为是根据sample.pairs文件的指定序列对计算Ks的。</p>
+<ol>
+<li>提取cds序列</li>
+</ol>
+<ul>
+<li>用cufflinks的gffread工具从gff文件提取CDS序列: <code>gffread sample.4col.true.RMfilt.hiConf.cdnm.gff -g genome.fa -x pseudo.cds</code></li>
+<li>另一种方法直接从结果文件中提取cds序列：<code>cat sample.4col.true.fa.RMfilt.hiConf.cdnm |grep -v &quot;#&quot; &gt;pseudo.cds</code></li>
+</ul>
+<ol start="2">
+<li>合并假基因CDS序列和基因组所有真基因的CDS序列（sample.cds）到一个文件all.cds</li>
+</ol>
+<ul>
+<li><code>cat pseudo.cds sample.cds &gt; all.cds</code></li>
+</ul>
+<ol start="3">
+<li>用cufflinks的gffread工具从gff文件提取pep序列</li>
+</ol>
+<ul>
+<li><code>gffread sample.4col.true.RMfilt.hiConf.cdnm.gff -g genome.fa -y pseudo.pep</code></li>
+</ul>
+<ol start="4">
+<li>合并假基因氨基酸序列和基因组所有真基因的氨基酸序列（sample.pep）到一个文件all.pep</li>
+</ol>
+<ul>
+<li><code>cat pseudo.pep sample.pep &gt; all.pep</code></li>
+</ul>
+<h3 id="6-1-4-计算Ks"><a href="#6-1-4-计算Ks" class="headerlink" title="6.1.4. 计算Ks"></a>6.1.4. 计算Ks</h3><p>准备好以上三个文件：sample.pair, all.cds, all.pep 即可用ParaAT.pl和KaKs_Calculator2.0两个软件，参考<strong>博客：批量计算Ka和Ks：<a href="https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks">https://yanzhongsino.github.io/2022/09/07/bioinfo_Ks_batch.calculation.Ks</a></strong>的步骤计算假基因和对应真基因间的Ks，从而获知遗传上的分化及分布。</p>
+<h2 id="6-2-检测假基因和共线性区块的关系"><a href="#6-2-检测假基因和共线性区块的关系" class="headerlink" title="6.2. 检测假基因和共线性区块的关系"></a>6.2. 检测假基因和共线性区块的关系</h2><p>在2022年08月在GBE上发表的荷叶铁线蕨基因组的文章（<a href="https://academic.oup.com/gbe/article/14/8/evac127/6659224%EF%BC%89%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E5%BA%94%E7%94%A8%EF%BC%8C%E5%88%A4%E6%96%AD%E6%98%AF%E5%90%A6%E6%98%AFpseudogene%E9%80%A0%E6%88%90%E5%85%B1%E7%BA%BF%E6%80%A7blocks%E5%8F%98%E7%9F%AD%EF%BC%8C%E7%94%A8%E5%88%B0%E7%9A%84%E6%96%B9%E6%B3%95%E7%BB%86%E8%8A%82%E8%AE%B0%E5%BD%95%E5%9C%A8%E6%AD%A4%E3%80%82">https://academic.oup.com/gbe/article/14/8/evac127/6659224）中的一个应用，判断是否是pseudogene造成共线性blocks变短，用到的方法细节记录在此。</a></p>
+<ol>
+<li>分析MCScanX结果中找到的共线性blocks，看blocks是否有成对出现在同一个contig上的，两对出现在同一contig上的blocks是否分别有共线性对应。</li>
+</ol>
+<ul>
+<li>结果是不存在符合条件的两对blocks。</li>
+<li>原因是contig的长度短造成的，contig长度在1-2Mbp，一个block含5个及以上基因，就占据了一条contig的全长，所以同一个contig上有多个block出现的概率变小。</li>
+</ul>
+<ol start="2">
+<li>在最长的blocks里找pseudogene造成blocks内部gene无共线性的例子。即block内部无共线性的gene对应block上对面位置是否有这个gene的pseudogene存在。</li>
+</ol>
+<ul>
+<li>结果是在超过7个gene的blocks里找到5个blocks存在这种情况。</li>
+</ul>
+<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">Alignment 288: ctg4124-ctg7158：Ane23910-Ane23911之间有Ane41918的pseudogene(ctg4124 6381349 6381618)；Ane23909-Ane23910之间有Ane41900的pseudogene（重排）；</span><br><span class="line">Alignment 102： ctg11329-ctg6741：Ane66914-Ane66915之间有Ane38759的pseudogene；Ane66920-Ane66921之间有Ane38765的pseudogene(ctg11329        2818519 2817964)；</span><br><span class="line">Alignment 139: ctg1252-ctg4677：Ane27375-Ane27376之间有Ane07597的pseudogene；</span><br><span class="line">Alignment 422: ctg7700-ctg9700：Ane46157-Ane461158之间有Ane57929的pseudogene（重排）(ctg7700 2107090 2106980)；</span><br><span class="line">Alignment 396: ctg6709-ctg8004：Ane38599-Ane38600之间有Ane47496的pseudogene（重排）(ctg6709 1117953 1117655)；</span><br></pre></td></tr></table></figure>
+
+<h2 id="6-3-非批量计算Ks"><a href="#6-3-非批量计算Ks" class="headerlink" title="6.3. 非批量计算Ks"></a>6.3. 非批量计算Ks</h2><p>如果只有少量样本对（关注的假基因和对应真基因）需要计算Ks，可以手动进行。比如上面的共线性区块分析中找到的5个blocks中的少量样本对。</p>
+<h3 id="6-3-1-提取序列"><a href="#6-3-1-提取序列" class="headerlink" title="6.3.1. 提取序列"></a>6.3.1. 提取序列</h3><p>通过PseudogenePipeline运行结果的比对信息提取假基因和对应的真基因比对上的ORF区域，并提取3的倍数的碱基，以计算Ks。</p>
+<ol>
+<li>拿<code>_results/sample.4col.true.fa.RMfilt.hiConf</code>中的一条pseudogene序列的ID举例说明位置的含义</li>
+</ol>
+<ul>
+<li><code>gene38168;ctg947|625093-625785;336-566:693-4;4|0|0|0    ctg947    625785    625096</code>表示在染色体<code>ctg947</code>的<code>625093-625785</code>位置有一个假基因pseudogene。</li>
+<li>对应的真基因是<code>gene38168</code>；<code>336-566</code>和<code>693-4</code>分别是指真基因序列和假基因序列比对上的区域；比对的信息是从tblastn的比对结果中获取的。</li>
+<li>真基因<code>gene38168</code>的<strong>氨基酸</strong>序列的<code>336-566</code>位置。</li>
+<li>假基因的<code>693-4</code>位置是指在染色体<code>ctg947</code>的<code>625093-625785</code>位置的假基因核酸序列上的第4到693个碱基，由于起始位置比终止位置大，所以是4-693位置的反向互补序列。</li>
+</ul>
+<ol start="2">
+<li>根据pseudogene的序列位置和对应的真基因的位置信息，提取两者比对上的cds序列</li>
+</ol>
+<ul>
+<li>先从<code>_results/sample.4col.true.fa.RMfilt.hiConf</code>中提取出<code>gene38168</code>对应的假基因pseudogene；</li>
+<li>根据假基因的比对位置<code>693-4</code>提取染色体<code>ctg947</code>的<code>625093-625785</code>位置的<code>4-693</code>的核酸，由于起始位置比终止位置大，所以是<code>4-693</code>位置的反向互补序列。</li>
+<li>再根据真基因的ID<code>gene38168</code>提取真基因的cds序列；</li>
+<li>根据真基因的蛋白比对位<code>336-566</code>，通过计算起始位置$$336*3-2&#x3D;1008$$，和终止位置$$566*3&#x3D;1698$$，找到cds对应的<code>1008-1698</code>位置提取出来。</li>
+</ul>
+<ol start="3">
+<li>比对和删改</li>
+</ol>
+<ul>
+<li>把两者序列保存在一个fasta文件，并用mafft&#x2F;muscle等工具做align；</li>
+<li>以真基因序列为参考，根据align结果对提取的假基因序列进行删除和修改。比如删除比对到intron的部分；若是假基因中包含indel，则在indel处找到上一个密码子的第三位，在这个位置删除对应密码子；最终使得两条序列都包含3的倍数的对应密码子的orf序列。</li>
+<li>删改好后再做一次align，每个假基因和对应真基因保存成一个文件；</li>
+</ul>
+<h3 id="6-3-2-计算Ks"><a href="#6-3-2-计算Ks" class="headerlink" title="6.3.2. 计算Ks"></a>6.3.2. 计算Ks</h3><ol>
+<li>改格式</li>
+</ol>
+<ul>
+<li><code>AXTConvertor gene_pseudo.fa gene_pseudo.axt</code></li>
+<li>KaKs_Calculator的改格式工具，改比对后的fas格式（gene_pseudo.fa）为axt（gene_pseudo.axt）。</li>
+<li>axt格式包括三行，第一行两个序列ID之间用短横杠-相连，第二行第一条序列，第三行第二条序列。</li>
+</ul>
+<ol start="2">
+<li>计算Ks</li>
+</ol>
+<ul>
+<li><code>KaKs_Calculator -i gene_pseudo.axt -o gene_pseudo.kaks -m PN</code></li>
+<li>计算KaKs，用PN模型；结果就在gene_pseudo.kaks文件中。</li>
+</ul>
+<ol start="3">
+<li>合并所有计算的ks结果</li>
+</ol>
+<table>
+<thead>
+<tr>
+<th>gene-pseudo</th>
+<th>Ka</th>
+<th>Ks</th>
+<th>Ka&#x2F;Ks</th>
+</tr>
+</thead>
+<tbody><tr>
+<td>Ane07597-Ane07597.pseudo</td>
+<td>0.166453</td>
+<td>0.830702</td>
+<td>0.200376</td>
+</tr>
+<tr>
+<td>Ane38759-Ane38759.pseudo</td>
+<td>0.0068327</td>
+<td>0.0579878</td>
+<td>0.11783</td>
+</tr>
+<tr>
+<td>Ane38765-Ane38765.pseudo</td>
+<td>0.0181923</td>
+<td>0.0594878</td>
+<td>0.305815</td>
+</tr>
+<tr>
+<td>Ane41900-Ane41900.pseudo</td>
+<td>0.356588</td>
+<td>1.23697</td>
+<td>0.288276</td>
+</tr>
+<tr>
+<td>Ane41918-Ane41918.pseudo</td>
+<td>0.0304071</td>
+<td>0.033778</td>
+<td>0.900203</td>
+</tr>
+<tr>
+<td>Ane47496-Ane47496.pseudo</td>
+<td>0.025231</td>
+<td>0.210919</td>
+<td>0.119624</td>
+</tr>
+<tr>
+<td>Ane57929-Ane57929.pseudo</td>
+<td>0.0380011</td>
+<td>0.0703679</td>
+<td>0.540034</td>
+</tr>
+</tbody></table>
+<ul>
+<li>从结果中看出，Ane38759，Ane38765，Ane579290三个基因和它们的假基因之间的Ks接近WGD分析中计算的WGD所在的Ks&#x3D;0.06；可以作为共线性blocks内部假基因的证据。</li>
+</ul>
+<h1 id="7-references"><a href="#7-references" class="headerlink" title="7. references"></a>7. references</h1><ol>
+<li>wiki:pseudogene:<a href="https://en.wikipedia.org/wiki/Pseudogene">https://en.wikipedia.org/wiki/Pseudogene</a></li>
+<li>PseudogenePipeline github：<a href="https://github.com/ShiuLab/PseudogenePipeline">https://github.com/ShiuLab/PseudogenePipeline</a></li>
+<li>2022年08月在GBE上发表的荷叶铁线蕨基因组的文章：<a href="https://academic.oup.com/gbe/article/14/8/evac127/6659224">https://academic.oup.com/gbe/article/14/8/evac127/6659224</a></li>
+</ol>
+<hr>
+<ul>
+<li>欢迎关注微信公众号：<strong>生信技工</strong></li>
+<li>公众号主要分享生信分析、生信软件、基因组学、转录组学、植物进化、生物学概念等相关内容，包括生物信息学工具的基本原理、操作步骤和学习心得。</li>
+</ul>
+<img src="https://github.com/yanzhongsino/yanzhongsino.github.io/blob/hexo/source/wechat/Wechat_public_qrcode.jpg?raw=true" width=20% title="wechat_public_QRcode.png" align=center/>]]></content>
+      <categories>
+        <category>bioinfo</category>
+        <category>pseudogene</category>
+      </categories>
+      <tags>
+        <tag>Ks</tag>
+        <tag>pseudogene</tag>
+        <tag>PseudogenePipeline</tag>
+        <tag>假基因</tag>
       </tags>
   </entry>
 </search>