diff --git a/README.md b/README.md
index de6520e..d715967 100644
--- a/README.md
+++ b/README.md
@@ -68,24 +68,20 @@ Before you download the Tourmaline commands and directory structure from GitHub,
 
 #### Option 1: Native installation
 
-To run Tourmaline natively on a Mac (Intel) or Linux system, start with a Conda installation of Snakemake v7.30.1. We recommend using [Miniconda with a python >= 3.8](https://docs.conda.io/en/latest/miniconda.html):
+To run Tourmaline natively on a Mac (Intel) or Linux system, start with a Conda installation of QIIME 2 (for Linux, change "osx" to "linux"):
 
 ```bash
-conda install -c conda-forge -c bioconda snakemake=7.30.1
-```
-
-Then install QIIME 2 with conda (**for Linux, change "osx" to "linux"**):
-
-```bash
-wget https://data.qiime2.org/distro/core/qiime2-2023.5-py38-osx-conda.yml
-conda env create -n qiime2-2023.5 --file qiime2-2023.5-py38-osx-conda.yml
+wget https://data.qiime2.org/distro/core/qiime2-2023.2-py38-osx-conda.yml
+# make sure you are using the most updated conda
+conda update conda
+conda env create -n qiime2-2023.2 --file qiime2-2023.2-py38-osx-conda.yml
 ```
 
 Activate the environment and install the other Conda- or PIP-installable dependencies:
 
 ```
-conda activate qiime2-2023.5
-conda install -c conda-forge -c bioconda biopython muscle clustalo tabulate
+conda activate qiime2-2023.2
+conda install -c conda-forge -c bioconda snakemake biopython muscle clustalo tabulate
 conda install -c conda-forge deicode
 pip install empress
 qiime dev refresh-cache
@@ -99,9 +95,9 @@ Follow these instructions for Macs with M1/M2 chips.
 **First, set your Terminal application to run in [Rosetta mode](https://academy.bigbinary.com/learn-rubyonrails/setting-up-macos).**
 
 ```bash
-wget https://data.qiime2.org/distro/core/qiime2-2023.5-py38-osx-conda.yml
-CONDA_SUBDIR=osx-64 conda env create -n qiime2-2023.5 --file qiime2-2023.5-py38-osx-conda.yml
-conda activate qiime2-2023.5
+wget https://data.qiime2.org/distro/core/qiime2-2023.2-py38-osx-conda.yml
+CONDA_SUBDIR=osx-64 conda env create -n qiime2-2023.2 --file qiime2-2023.2-py38-osx-conda.yml
+conda activate qiime2-2023.2
 conda config --env --set subdir osx-64
 ```
 
@@ -158,8 +154,8 @@ Download reference database sequence and taxonomy files, named `refseqs.qza` and
 
 ```bash
 cd tourmaline/01-imported
-wget https://data.qiime2.org/2023.5/common/silva-138-99-seqs-515-806.qza
-wget https://data.qiime2.org/2023.5/common/silva-138-99-tax-515-806.qza
+wget https://data.qiime2.org/2023.2/common/silva-138-99-seqs-515-806.qza
+wget https://data.qiime2.org/2023.2/common/silva-138-99-tax-515-806.qza
 ln -s silva-138-99-seqs-515-806.qza refseqs.qza
 ln -s silva-138-99-tax-515-806.qza reftax.qza
 ```
@@ -197,12 +193,12 @@ Now edit, replace, or store the required input files as described here:
 
 Shown here is the DADA2 paired-end workflow. See the Wiki's [Run](https://github.com/aomlomics/tourmaline/wiki/4-Run) page for complete instructions on all steps, denoising methods, and filtering modes.
 
-Note that any of the commands below can be run with various options, including `--printshellcmds` to see the shell commands being executed and `--dryrun` to display which rules would be run but not execute them. To generate a graph of the rules that will be run from any Snakemake command, see the section "Directed acyclic graph (DAG)" on the [Run](https://github.com/aomlomics/tourmaline/wiki/4-Run) page. **Always include the --use-conda option.**
+Note that any of the commands below can be run with various options, including `--printshellcmds` to see the shell commands being executed and `--dryrun` to display which rules would be run but not execute them. To generate a graph of the rules that will be run from any Snakemake command, see the section "Directed acyclic graph (DAG)" on the [Run](https://github.com/aomlomics/tourmaline/wiki/4-Run) page.
 
 From the `tourmaline` directory (which you may rename), run Snakemake with the *denoise* rule as the target, changing the number of cores to match your machine:
 
 ```bash
-snakemake --use-conda dada2_pe_denoise --cores 4
+snakemake dada2_pe_denoise --cores 4
 ```
 
 Pausing after the *denoise* step allows you to make changes before proceeding:
@@ -216,19 +212,19 @@ Pausing after the *denoise* step allows you to make changes before proceeding:
 Continue the workflow without filtering (for now). If you are satisfied with your parameters and files, run the *taxonomy* rule (for unfiltered data):
 
 ```bash
-snakemake --use-conda dada2_pe_taxonomy_unfiltered --cores 4
+snakemake dada2_pe_taxonomy_unfiltered --cores 4
 ```
 
 Next, run the *diversity* rule (for unfiltered data):
 
 ```bash
-snakemake --use-conda dada2_pe_diversity_unfiltered --cores 4
+snakemake dada2_pe_diversity_unfiltered --cores 4
 ```
 
 Finally, run the *report* rule (for unfiltered data):
 
 ```bash
-snakemake --use-conda dada2_pe_report_unfiltered --cores 4
+snakemake dada2_pe_report_unfiltered --cores 4
 ```
 
 #### Filtered mode
@@ -243,19 +239,19 @@ After viewing the *unfiltered* results—the taxonomy summary and taxa barplot,
 Now we are ready to filter the representative sequences and feature table, generate new summaries, and generate a new taxonomy bar plot, by running the *taxonomy* rule (for filtered data):
 
 ```bash
-snakemake --use-conda dada2_pe_taxonomy_filtered --cores 4
+snakemake dada2_pe_taxonomy_filtered --cores 4
 ```
 
 Next, run the *diversity* rule (for filtered data):
 
 ```bash
-snakemake --use-conda dada2_pe_diversity_filtered --cores 4
+snakemake dada2_pe_diversity_filtered --cores 4
 ```
 
 Finally, run the *report* rule (for filtered data):
 
 ```bash
-snakemake --use-conda dada2_pe_report_filtered --cores 1
+snakemake dada2_pe_report_filtered --cores 1
 ```
 
 ### View output
diff --git a/Snakefile b/Snakefile
index 7d23de6..3512178 100644
--- a/Snakefile
+++ b/Snakefile
@@ -1,9 +1,9 @@
-#import pandas as pd
-#import numpy as np
-#from qiime2 import Artifact
-#import matplotlib.pyplot as plt
-#import seaborn as sns
-#from tabulate import tabulate
+import pandas as pd
+import numpy as np
+from qiime2 import Artifact
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tabulate import tabulate
 
 # GLOBALS ----------------------------------------------------------------------
 
@@ -298,8 +298,6 @@ rule deblur_se_report_filtered:
 rule check_metadata:
     output:
         touch("01-imported/check_metadata.done")
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "if [ -r '00-data/metadata.tsv' ]; then "
@@ -314,11 +312,31 @@ rule summarize_metadata:
     output:
         "01-imported/metadata_summary.md",
         "01-imported/metadata_columns.txt"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
-    shell:
-        "python scripts/summarize_metadata.py {input.metadata} {output[0]} {output[1]}"
+    run:
+        df = pd.read_csv(input.metadata, sep='\t')
+        cols = df.columns
+        df2 = pd.DataFrame(columns =[0,1], index=cols)
+        for col in cols:
+            if col in df.columns:
+                vc = df[col].value_counts()
+                if vc.index.shape == (0,):
+                    df2.loc[col, 0] = '(no values in column)'
+                    df2.loc[col, 1] = '--'
+                else:
+                    df2.loc[col, 0] = vc.index[0]
+                    df2.loc[col, 1] = vc.values[0]
+            else:
+                df2.loc[col, 0] = '(column not provided)'
+                df2.loc[col, 1] = '--'
+        df2.columns = ['Most common value', 'Count']
+        df2.index.name = 'Column name'
+        outstr = tabulate(df2, tablefmt="pipe", headers="keys")
+        with open(output[0], 'w') as target:
+            target.write(outstr)
+            target.write('\n')
+        with open(output[1], 'w') as target:
+            [target.write('%s\n' % i) for i in cols]
 
 rule check_inputs_params_pe:
     input:
@@ -326,11 +344,8 @@ rule check_inputs_params_pe:
         check="01-imported/check_metadata.done"
     params:
         column=config["beta_group_column"],
-        classifymethod=config["classify_method"],
     output:
         touch("01-imported/check_inputs_params_pe.done")
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "if [ -r '01-imported/fastq_pe.qza' ]; then "
@@ -339,28 +354,17 @@ rule check_inputs_params_pe:
         "    echo 'OK: FASTQ manifest file 00-data/manifest_pe.csv found; it will be used to create FASTQ archive 01-imported/fastq_pe.qza.'; "
         "else echo 'Error: FASTQ sequence data not found; either 00-data/manifest_pe.csv or 01-imported/fastq_pe.qza is required.' && exit 1; "
         "fi; "
-        "if [ {params.classifymethod} = naive-bayes ]; then "
-        "    if [ -r '01-imported/classifier.qza' ]; then "
-        "        echo 'OK: Reference sequences classifier 01-imported/classifier.qza found; reference sequences FASTA file 00-data/refseqs.fna not required.'; "
-        "    elif [ -r '01-imported/refseqs.qza' ]; then "
-        "        echo 'OK: Reference sequences archive 01-imported/refseqs.qza found; reference sequences FASTA file 00-data/refseqs.fna not required.'; "
-        "    elif [ -r '00-data/refseqs.fna' ]; then "
-        "        echo 'OK: Reference sequences FASTA file 00-data/refseqs.fna found; it will be used to create reference sequences archive 01-imported/refseqs.qza.'; "
-        "    else echo 'Error: Reference sequences not found; either 01-imported/classifier.qza or 00-data/refseqs.fna or 01-imported/refseqs.qza is required.' && exit 1; "
-        "    fi; "
-        "elif [ -r '01-imported/refseqs.qza' ]; then "
+        "if [ -r '01-imported/refseqs.qza' ]; then "
         "    echo 'OK: Reference sequences archive 01-imported/refseqs.qza found; reference sequences FASTA file 00-data/refseqs.fna not required.'; "
         "elif [ -r '00-data/refseqs.fna' ]; then "
         "    echo 'OK: Reference sequences FASTA file 00-data/refseqs.fna found; it will be used to create reference sequences archive 01-imported/refseqs.qza.'; "
         "else echo 'Error: Reference sequences not found; either 00-data/refseqs.fna or 01-imported/refseqs.qza is required.' && exit 1; "
         "fi; "
-        "if [ {params.classifymethod} != naive-bayes ]; then "
-        "    if [ -r '01-imported/reftax.qza' ]; then "
-        "        echo 'OK: Reference taxonomy archive 01-imported/reftax.qza found; reference taxonomy file 00-data/reftax.tsv not required.'; "
-        "    elif [ -r '00-data/reftax.tsv' ]; then "
-        "        echo 'OK: Reference taxonomy file 00-data/reftax.tsv found; it will be used to create reference taxonomy archive 01-imported/reftax.qza.'; "
-        "    else echo 'Error: Reference taxonomy not found; either 00-data/reftax.tsv or 01-imported/reftax.qza is required.' && exit 1; "
-        "    fi; "
+        "if [ -r '01-imported/reftax.qza' ]; then "
+        "    echo 'OK: Reference taxonomy archive 01-imported/reftax.qza found; reference taxonomy file 00-data/reftax.tsv not required.'; "
+        "elif [ -r '00-data/reftax.tsv' ]; then "
+        "    echo 'OK: Reference taxonomy file 00-data/reftax.tsv found; it will be used to create reference taxonomy archive 01-imported/reftax.qza.'; "
+        "else echo 'Error: Reference taxonomy not found; either 00-data/reftax.tsv or 01-imported/reftax.qza is required.' && exit 1; "
         "fi; "
         "if grep -q ^{params.column}$ {input}; then "
         "    echo 'OK: Metadata contains the column \"{params.column}\" that is specified as beta_group_column in config.yaml.'; "
@@ -374,8 +378,6 @@ rule check_inputs_params_se:
         column=config["beta_group_column"],
     output:
         touch("01-imported/check_inputs_params_se.done")
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "if [ -r '01-imported/fastq_se.qza' ]; then "
@@ -408,8 +410,6 @@ rule import_ref_seqs:
         "00-data/refseqs.fna"
     output:
         "01-imported/refseqs.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -422,8 +422,6 @@ rule import_ref_tax:
         "00-data/reftax.tsv"
     output:
         "01-imported/reftax.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -438,8 +436,6 @@ rule import_fastq_demux_pe:
         "01-imported/check_inputs_params_pe.done"
     output:
         "01-imported/fastq_pe.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -454,8 +450,6 @@ rule import_fastq_demux_se:
         "01-imported/check_inputs_params_se.done"
     output:
         "01-imported/fastq_se.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -472,8 +466,6 @@ rule summarize_fastq_demux_pe:
         "01-imported/check_inputs_params_pe.done"
     output:
         "01-imported/fastq_summary.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime demux summarize "
@@ -486,8 +478,6 @@ rule summarize_fastq_demux_se:
         "01-imported/check_inputs_params_se.done"
     output:
         "01-imported/fastq_summary.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime demux summarize "
@@ -499,8 +489,6 @@ rule export_fastq_summary_to_counts:
         "01-imported/fastq_summary.qzv"
     output:
         "01-imported/fastq_counts.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "unzip -qq -o {input} -d temp0; "
@@ -512,11 +500,14 @@ rule describe_fastq_counts:
         "01-imported/fastq_counts.tsv"
     output:
         "01-imported/fastq_counts_describe.md"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
-    shell:
-        "python scripts/describe_fastq_counts.py {input} {output}"
+    run:
+        s = pd.read_csv(input[0], index_col=0, sep='\t')
+        t = s.describe()
+        outstr = tabulate(pd.DataFrame(t.iloc[1:,0]), tablefmt="pipe", headers=['Statistic (n=%s)' % t.iloc[0,0].astype(int), 'Fastq sequences per sample'])
+        with open(output[0], 'w') as target:
+            target.write(outstr)
+            target.write('\n')
 
 # RULES: DENOISE ---------------------------------------------------------------
 
@@ -541,8 +532,6 @@ rule denoise_dada2_pe:
         table="02-output-dada2-pe-unfiltered/00-table-repseqs/table.qza",
         repseqs="02-output-dada2-pe-unfiltered/00-table-repseqs/repseqs.qza",
         stats="02-output-dada2-pe-unfiltered/00-table-repseqs/dada2_stats.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["dada2pe_threads"]
     shell:
         "qiime dada2 denoise-paired "
@@ -583,10 +572,6 @@ rule denoise_dada2_se:
         table="02-output-dada2-se-unfiltered/00-table-repseqs/table.qza",
         repseqs="02-output-dada2-se-unfiltered/00-table-repseqs/repseqs.qza",
         stats="02-output-dada2-se-unfiltered/00-table-repseqs/dada2_stats.qza"
-    conda:
-        "qiime2-2023.5"
-    conda:
-        "qiime2-2023.5"
     threads: config["dada2se_threads"]
     shell:
         "qiime dada2 denoise-single "
@@ -624,10 +609,6 @@ rule denoise_deblur_se:
         table="02-output-deblur-se-unfiltered/00-table-repseqs/table.qza",
         repseqs="02-output-deblur-se-unfiltered/00-table-repseqs/repseqs.qza",
         stats="02-output-deblur-se-unfiltered/00-table-repseqs/deblur_stats.qza"
-    conda:
-        "qiime2-2023.5"
-    conda:
-        "qiime2-2023.5"
     threads: config["deblur_threads"]
     shell:
         "qiime deblur denoise-other "
@@ -655,10 +636,6 @@ rule summarize_feature_table:
         metadata="00-data/metadata.tsv"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/table_summary.qzv"
-    conda:
-        "qiime2-2023.5"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime feature-table summarize "
@@ -666,29 +643,11 @@ rule summarize_feature_table:
         "--m-sample-metadata-file {input.metadata} "
         "--o-visualization {output}"
 
-rule summarize_repseqs:
-    input:
-        stats="02-output-{method}-{filter}/00-table-repseqs/dada2_stats.qza"
-    output:
-        "02-output-{method}-{filter}/00-table-repseqs/dada2_stats.qzv"
-    conda:
-        "qiime2-2023.5"
-    conda:
-        "qiime2-2023.5"
-    threads: config["other_threads"]
-    shell:
-        "qiime metadata tabulate "
-        "--m-input-file {input.stats} "
-        "--o-visualization {output}"
-
-
 rule export_table_to_biom:
     input:
         "02-output-{method}-{filter}/00-table-repseqs/table.qza"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/table.biom"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools export "
@@ -701,8 +660,6 @@ rule summarize_biom_samples:
         "02-output-{method}-{filter}/00-table-repseqs/table.biom"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/table_summary_samples.txt"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "biom summarize-table "
@@ -716,8 +673,6 @@ rule summarize_biom_features:
         "02-output-{method}-{filter}/00-table-repseqs/table.biom"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/table_summary_features.txt"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "biom summarize-table "
@@ -732,8 +687,6 @@ rule visualize_repseqs:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.qza"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime feature-table tabulate-seqs "
@@ -745,8 +698,6 @@ rule export_repseqs_to_fasta:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.qza"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.fasta"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools export "
@@ -759,8 +710,6 @@ rule repseqs_detect_amplicon_locus:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.fasta"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs_amplicon_type.txt"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "python scripts/detect_amplicon_locus.py -i {input} > {output}"
@@ -770,8 +719,6 @@ rule repseqs_lengths:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs.fasta"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs_lengths.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "perl scripts/fastaLengths.pl {input} > {output}"
@@ -781,11 +728,14 @@ rule repseqs_lengths_describe:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs_lengths.tsv"
     output:
         "02-output-{method}-{filter}/00-table-repseqs/repseqs_lengths_describe.md"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
-    shell:
-        "python scripts/repseqs_lengths_describe.py {input} {output}"
+    run:
+        s = pd.read_csv(input[0], header=None, index_col=0, sep='\t')
+        t = s.describe()
+        outstr = tabulate(t.iloc[1:], tablefmt="pipe", headers=['Statistic (n=%s)' % t.iloc[0].values[0].astype(int), 'Sequence length'])
+        with open(output[0], 'w') as target:
+            target.write(outstr)
+            target.write('\n')
 
 # RULES: TAXONOMY --------------------------------------------------------------
 
@@ -800,8 +750,6 @@ rule feature_classifier:
         classifymethod=config["classify_method"],
         classifyparams=config["classify_parameters"],
         searchout="02-output-{method}-unfiltered/01-taxonomy/search_results.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["feature_classifier_threads"]
     shell:
         "echo classify_method: {params.classifymethod}; "
@@ -842,8 +790,6 @@ rule visualize_taxonomy:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.qza"
     output:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime metadata tabulate "
@@ -857,8 +803,6 @@ rule taxa_barplot:
         metadata="00-data/metadata.tsv"
     output:
         "02-output-{method}-{filter}/01-taxonomy/taxa_barplot.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime taxa barplot "
@@ -875,8 +819,6 @@ rule export_taxa_biom:
         "02-output-{method}-{filter}/01-taxonomy/taxa_sample_table.tsv"
     params:
         taxalevel=config["classify_taxalevel"]
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime taxa collapse "
@@ -898,8 +840,6 @@ rule export_taxonomy_to_tsv:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.qza"
     output:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools export "
@@ -912,8 +852,6 @@ rule import_taxonomy_to_qza:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.tsv"
     output:
         "02-output-{method}-{filter}/01-taxonomy/taxonomy.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -934,8 +872,6 @@ rule align_repseqs:
     params:
         method=config["alignment_method"],
         muscle_iters=config["alignment_muscle_iters"]
-    conda:
-        "qiime2-2023.5"
     threads: config["alignment_threads"],
     shell:
         "if [ {params.method} = muscle ]; then "
@@ -988,8 +924,6 @@ rule phylogeny_fasttree:
         "02-output-{method}-{filter}/02-alignment-tree/aligned_repseqs.qza"
     output:
         "02-output-{method}-{filter}/02-alignment-tree/unrooted_tree.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["phylogeny_fasttree_threads"]
     shell:
         "qiime phylogeny fasttree "
@@ -1002,8 +936,6 @@ rule phylogeny_midpoint_root:
         "02-output-{method}-{filter}/02-alignment-tree/unrooted_tree.qza"
     output:
         "02-output-{method}-{filter}/02-alignment-tree/rooted_tree.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime phylogeny midpoint-root "
@@ -1019,8 +951,6 @@ rule visualize_tree:
         outliers="02-output-{method}-{filter}/02-alignment-tree/outliers.qza"
     output:
         "02-output-{method}-{filter}/02-alignment-tree/rooted_tree.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime empress community-plot "
@@ -1036,8 +966,6 @@ rule alignment_count_gaps:
         "02-output-{method}-{filter}/02-alignment-tree/aligned_repseqs.fasta"
     output:
         "02-output-{method}-{filter}/02-alignment-tree/aligned_repseqs_gaps.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "bash scripts/alignment_count_gaps.sh < {input} > {output}"
@@ -1064,8 +992,6 @@ rule alignment_detect_outliers:
         threshold = config["odseq_threshold"]
     output:
         "02-output-{method}-{filter}/02-alignment-tree/aligned_repseqs_outliers.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "Rscript --vanilla scripts/run_odseq.R {input} {params.metric} {params.replicates} {params.threshold} temp_odseq; "
@@ -1084,20 +1010,46 @@ rule tabulate_plot_repseq_properties:
         propdescribe="02-output-{method}-{filter}/02-alignment-tree/repseqs_properties_describe.md",
         proppdf="02-output-{method}-{filter}/02-alignment-tree/repseqs_properties.pdf",
         outliersforqza="02-output-{method}-{filter}/02-alignment-tree/outliers.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
-    shell:
-        "python scripts/plot_repseq_properties.py {input.lengths} {input.gaps} {input.outliers} {input.taxonomy} "
-        "{input.table} {output.proptsv} {output.propdescribe} {output.proppdf} {output.outliersforqza}"
+    run:
+        lengths = pd.read_csv(input['lengths'], names=['length'], index_col=0, sep='\t')
+        gaps = pd.read_csv(input['gaps'], names=['gaps'], index_col=0, sep='\t')
+        outliers = pd.read_csv(input['outliers'], names=['outlier'], index_col=0, sep='\t')
+        taxonomy = Artifact.load(input['taxonomy'])
+        taxonomydf = taxonomy.view(view_type=pd.DataFrame)
+        taxonomydf['level_1'] = [x.split(';')[0] for x in taxonomydf['Taxon']]
+        table = Artifact.load(input['table'])
+        tabledf = table.view(view_type=pd.DataFrame)
+        merged = pd.merge(lengths, gaps, left_index=True, right_index=True, how='outer')
+        merged = pd.merge(merged, outliers, left_index=True, right_index=True, how='outer')
+        merged = pd.merge(merged, taxonomydf['Taxon'], left_index=True, right_index=True, how='outer')
+        merged = pd.merge(merged, taxonomydf['level_1'], left_index=True, right_index=True, how='outer')
+        merged = pd.merge(merged, tabledf.sum().rename('observations'), left_index=True, right_index=True, how='outer')
+        merged.columns = ['length', 'gaps', 'outlier', 'taxonomy', 'taxonomy_level_1', 'observations']
+        merged.index.name = 'featureid'
+        merged['log10(observations)'] = [np.log10(x) for x in merged['observations']]
+        merged.sort_values('log10(observations)', ascending=False, inplace=True)
+        merged.to_csv(output['proptsv'], index=True, sep='\t')
+        t = merged.describe()
+        tcolumns = t.columns
+        tcolumns = tcolumns.insert(0, 'Statistic (n=%s)' % t.iloc[0].values[0].astype(int))
+        outstr = tabulate(t.iloc[1:], tablefmt="pipe", headers=tcolumns)
+        with open(output['propdescribe'], 'w') as target:
+            target.write(outstr)
+            target.write('\n')
+        g = sns.relplot(data=merged, x='length', y='gaps', col='outlier', hue='taxonomy_level_1', size='log10(observations)', sizes=(1,500), edgecolor = 'none', alpha=0.7)
+        g.set_axis_labels('length (bp) not including gaps', 'gaps (bp) in multiple sequence alignment')
+        plt.savefig(output['proppdf'], bbox_inches='tight')
+        outliers.columns = ['Outlier']
+        outliers.index.name = 'Feature ID'
+        outliers = outliers*1
+        outliers.to_csv(output['outliersforqza'], index=True, sep='\t')
 
 rule import_outliers_to_qza:
     input:
         "02-output-{method}-{filter}/02-alignment-tree/outliers.tsv"
     output:
         "02-output-{method}-{filter}/02-alignment-tree/outliers.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime tools import "
@@ -1111,8 +1063,6 @@ rule tabulate_repseqs_to_filter:
     output:
         outliers="02-output-{method}-{filter}/02-alignment-tree/repseqs_to_filter_outliers.tsv",
         unassigned="02-output-{method}-{filter}/02-alignment-tree/repseqs_to_filter_unassigned.tsv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "cat {input.proptsv} | grep -i 'outlier\|true' | cut -f1,4 > {output.outliers}; "
@@ -1140,8 +1090,6 @@ rule filter_sequences_table:
     output:
         repseqs="02-output-{method}-filtered/00-table-repseqs/repseqs.qza",
         table="02-output-{method}-filtered/00-table-repseqs/table.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         # FILTER SEQUENCES BY TAXONOMY
@@ -1210,11 +1158,17 @@ rule filter_taxonomy:
     output:
         taxonomytsv="02-output-{method}-filtered/01-taxonomy/taxonomy.tsv",
         taxonomyqza="02-output-{method}-filtered/01-taxonomy/taxonomy.qza"        
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
-    shell:
-        "python scripts/filter_taxonomy.py {input.taxonomy} {input.repseqs} {output.taxonomytsv} {output.taxonomyqza}"
+    run:
+        df_taxonomy = pd.read_csv(input['taxonomy'], index_col=0, sep='\t')
+        df_repseqs = pd.read_csv(input['repseqs'], header=None, index_col=0, sep='\t')
+        keep_ids = df_repseqs.index
+        df_taxonomy_filtered = df_taxonomy.loc[list(keep_ids)]
+        df_taxonomy_filtered.to_csv(output['taxonomytsv'], sep='\t')
+        artifact_taxonomy_filtered = Artifact.import_data('FeatureData[Taxonomy]', df_taxonomy_filtered)
+        artifact_taxonomy_filtered.save(output['taxonomyqza'])
+
+
 
 
 # RULES: DIVERSITY -------------------------------------------------------------
@@ -1228,8 +1182,6 @@ rule diversity_alpha_rarefaction:
         maxdepth=config["alpha_max_depth"]
     output:
         "02-output-{method}-{filter}/03-alpha-diversity/alpha_rarefaction.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime diversity alpha-rarefaction "
@@ -1268,8 +1220,6 @@ rule diversity_core_metrics_phylogenetic:
         weightedunifracemperor="02-output-{method}-{filter}/04-beta-diversity/weighted_unifrac_emperor.qzv",
         jaccardemperor="02-output-{method}-{filter}/04-beta-diversity/jaccard_emperor.qzv",
         braycurtisemperor="02-output-{method}-{filter}/04-beta-diversity/bray_curtis_emperor.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["diversity_core_metrics_phylogenetic_threads"]
     shell:
         "qiime diversity core-metrics-phylogenetic "
@@ -1302,8 +1252,6 @@ rule diversity_alpha_group_significance:
         metadata="00-data/metadata.tsv"
     output:
         "02-output-{method}-{filter}/03-alpha-diversity/{metric}_group_significance.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime diversity alpha-group-significance "
@@ -1321,8 +1269,6 @@ rule diversity_beta_group_significance:
         pairwise=config["beta_group_pairwise"]
     output:
         "02-output-{method}-{filter}/04-beta-diversity/{metric}_group_significance.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime diversity beta-group-significance "
@@ -1344,8 +1290,6 @@ rule deicode_auto_rpca:
     output:
         biplot="02-output-{method}-{filter}/04-beta-diversity/deicode_biplot.qza",
         distancematrix="02-output-{method}-{filter}/04-beta-diversity/deicode_distance_matrix.qza"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime deicode auto-rpca "
@@ -1365,8 +1309,6 @@ rule emperor_biplot:
         numfeatures=config["deicode_num_features"]
     output:
         emperor="02-output-{method}-{filter}/04-beta-diversity/deicode_biplot_emperor.qzv"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "qiime emperor biplot "
@@ -1415,8 +1357,6 @@ rule generate_report_md:
         refdatabase=config["database_name"]
     output:
         "03-reports/report_{method}_{filter}.md"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "echo '# Tourmaline Report' > {output};"
@@ -1624,8 +1564,6 @@ rule generate_report_html:
         theme=config["report_theme"]
     output:
         "03-reports/report_{method}_{filter}.html"
-    conda:
-        "qiime2-2023.5"
     threads: config["other_threads"]
     shell:
         "pandoc -i {input} -o {output};"
diff --git a/config.yaml b/config.yaml
index 8db9524..3e9ab9b 100644
--- a/config.yaml
+++ b/config.yaml
@@ -1,5 +1,5 @@
 # config.yaml - configuration file for the Tourmaline Snakemake workflow
-# Compatable with qiime2-2023.5
+# Compatable with qiime2-2023.2
 # User MUST edit these parameters before running their own data.
 # Detailed instructions: https://github.com/aomlomics/tourmaline/wiki.
 
diff --git a/docker/Dockerfile b/docker/Dockerfile
index c835a65..5bc4a7b 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -1,9 +1,9 @@
 # Use the official QIIME 2 image as a parent image
-FROM quay.io/qiime2/core:2023.5
+FROM quay.io/qiime2/core:2023.2
 
 # Label information
 LABEL maintainer="Luke Thompson"
-LABEL description="Docker image to build the Tourmaline Snakemake workflow for QIIME 2 v.2023.5"
+LABEL description="Docker image to build the Tourmaline Snakemake workflow for QIIME 2 v.2023.2"
 
 # Set up bash environment: aliases, colors, history
 RUN echo "alias cd..='cd ..'" >> ~/.bashrc
@@ -29,20 +29,14 @@ RUN apt-get update -y && \
 RUN pip install empress 
 
 # Add conda installation dir to $PATH (instead of doing 'conda activate')
-#ENV PATH="/opt/conda/envs/snakemake/bin:$PATH"
-ENV PATH /opt/conda/bin:${PATH}
-
-# Install snakemake environment
-RUN /bin/bash -c "conda update -n base -c defaults conda"
-RUN /bin/bash -c "conda create -y -c conda-forge -c bioconda -n snakemake snakemake snakemake-minimal --only-deps"
-RUN echo "source activate snakemake" > ~/.bashrc
-ENV PATH /opt/conda/envs/snakemake/bin:${PATH}
+ENV PATH="/opt/conda/envs/qiime2-2023.2/bin:$PATH"
 
 # This is necessary to install snakemake using conda
-#SHELL ["conda", "run", "-n", "qiime2-2023.2", "/bin/bash", "-c"]
+SHELL ["conda", "run", "-n", "qiime2-2023.2", "/bin/bash", "-c"]
  
-# Install tourmaline dependencies using conda
-RUN conda install -n qiime2-2023.5 -c conda-forge -c bioconda biopython muscle clustalo tabulate
-RUN conda install -n qiime2-2023.5 -c conda-forge deicode
-#RUN qiime dev refresh-cache
-RUN conda install -n qiime2-2023.5 -c bioconda bioconductor-msa bioconductor-odseq
+# Install snakemake and other dependencies using conda
+RUN conda update -n base -c defaults conda
+RUN conda install -c conda-forge -c bioconda snakemake biopython muscle clustalo tabulate
+RUN conda install -c conda-forge deicode
+RUN qiime dev refresh-cache
+RUN conda install -c bioconda bioconductor-msa bioconductor-odseq
diff --git a/scripts/describe_fastq_counts.py b/scripts/describe_fastq_counts.py
deleted file mode 100644
index 161ece5..0000000
--- a/scripts/describe_fastq_counts.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-
-import pandas as pd
-import sys
-from tabulate import tabulate
-
-# usage
-usage = '''
-describe_fastq_counts.py fastq_counts_tsv output1_md
-
-'''
-
-if len(sys.argv) < 1:
-    print(usage)
-    sys.exit()
-
-# input paths
-IN = sys.argv[1]
-
-# output paths
-OUT = sys.argv[2] # 'fastq_counts_describe.md'
-
-
-s = pd.read_csv(IN, index_col=0, sep='\t')
-t = s.describe()
-outstr = tabulate(pd.DataFrame(t.iloc[1:,0]), tablefmt="pipe", headers=['Statistic (n=%s)' % t.iloc[0,0].astype(int), 'Fastq sequences per sample'])
-with open(OUT, 'w') as target:
-    target.write(outstr)
-    target.write('\n')
-
diff --git a/scripts/filter_taxonomy.py b/scripts/filter_taxonomy.py
deleted file mode 100644
index 3d145cd..0000000
--- a/scripts/filter_taxonomy.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-
-import pandas as pd
-import sys
-from qiime2 import Artifact
-
-# usage
-usage = '''
-filter_taxonomy.py taxonomy repseqs taxonomytsv taxonomyqza
-'''
-
-if len(sys.argv) < 3:
-    print(usage)
-    sys.exit()
-
-# input paths
-taxonomy = sys.argv[1]
-repseqs = sys.argv[2]
-
-# output paths
-taxonomytsv = sys.argv[3] #
-taxonomyqza = sys.argv[4]
-
-df_taxonomy = pd.read_csv(taxonomy, index_col=0, sep='\t')
-df_repseqs = pd.read_csv(repseqs, header=None, index_col=0, sep='\t')
-keep_ids = df_repseqs.index
-df_taxonomy_filtered = df_taxonomy.loc[list(keep_ids)]
-df_taxonomy_filtered.to_csv(taxonomytsv, sep='\t')
-artifact_taxonomy_filtered = Artifact.import_data('FeatureData[Taxonomy]', df_taxonomy_filtered)
-artifact_taxonomy_filtered.save(taxonomyqza)
\ No newline at end of file
diff --git a/scripts/plot_repseq_properties.py b/scripts/plot_repseq_properties.py
deleted file mode 100644
index e9a4f29..0000000
--- a/scripts/plot_repseq_properties.py
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/usr/bin/env python
-
-import pandas as pd
-import numpy as np
-import sys
-from tabulate import tabulate
-from qiime2 import Artifact
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-# usage
-usage = '''
-plot_repseq_properties.py repseqs_lengths_tsv aligned_repseqs_gaps aligned_repseqs_outliers taxonomy_qza table_qza repseqs_properties repseqs_properties_describe repseqs_properties_pdf outliers_tsv
-
-'''
-
-if len(sys.argv) < 8:
-    print(usage)
-    sys.exit()
-
-# input paths
-lengths = sys.argv[1]
-gaps = sys.argv[2]
-outliers = sys.argv[3]
-taxonomy = sys.argv[4]
-table = sys.argv[5]
-
-# output paths
-proptsv = sys.argv[6] #
-propdescribe = sys.argv[7]
-proppdf = sys.argv[8]
-outliersforqza = sys.argv[9] 
-
-lengths = pd.read_csv(lengths, names=['length'], index_col=0, sep='\t')
-gaps = pd.read_csv(gaps, names=['gaps'], index_col=0, sep='\t')
-outliers = pd.read_csv(outliers, names=['outlier'], index_col=0, sep='\t')
-taxonomy = Artifact.load(taxonomy)
-taxonomydf = taxonomy.view(view_type=pd.DataFrame)
-taxonomydf['level_1'] = [x.split(';')[0] for x in taxonomydf['Taxon']]
-table = Artifact.load(table)
-tabledf = table.view(view_type=pd.DataFrame)
-merged = pd.merge(lengths, gaps, left_index=True, right_index=True, how='outer')
-merged = pd.merge(merged, outliers, left_index=True, right_index=True, how='outer')
-merged = pd.merge(merged, taxonomydf['Taxon'], left_index=True, right_index=True, how='outer')
-merged = pd.merge(merged, taxonomydf['level_1'], left_index=True, right_index=True, how='outer')
-merged = pd.merge(merged, tabledf.sum().rename('observations'), left_index=True, right_index=True, how='outer')
-merged.columns = ['length', 'gaps', 'outlier', 'taxonomy', 'taxonomy_level_1', 'observations']
-merged.index.name = 'featureid'
-merged['log10(observations)'] = [np.log10(x) for x in merged['observations']]
-merged.sort_values('log10(observations)', ascending=False, inplace=True)
-merged.to_csv(proptsv, index=True, sep='\t')
-t = merged.describe()
-tcolumns = t.columns
-tcolumns = tcolumns.insert(0, 'Statistic (n=%s)' % t.iloc[0].values[0].astype(int))
-outstr = tabulate(t.iloc[1:], tablefmt="pipe", headers=tcolumns)
-with open(propdescribe, 'w') as target:
-    target.write(outstr)
-    target.write('\n')
-g = sns.relplot(data=merged, x='length', y='gaps', col='outlier', hue='taxonomy_level_1', size='log10(observations)', sizes=(1,500), edgecolor = 'none', alpha=0.7)
-g.set_axis_labels('length (bp) not including gaps', 'gaps (bp) in multiple sequence alignment')
-plt.savefig(proppdf, bbox_inches='tight')
-outliers.columns = ['Outlier']
-outliers.index.name = 'Feature ID'
-outliers = outliers*1
-outliers.to_csv(outliersforqza, index=True, sep='\t')
-
diff --git a/scripts/repseqs_lengths_describe.py b/scripts/repseqs_lengths_describe.py
deleted file mode 100644
index ca9549a..0000000
--- a/scripts/repseqs_lengths_describe.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-
-import pandas as pd
-import sys
-from tabulate import tabulate
-
-# usage
-usage = '''
-repseqs_lengths_describe.py repseqs_lengths_tsv repseqs_lengths_md
-
-'''
-
-if len(sys.argv) < 1:
-    print(usage)
-    sys.exit()
-
-# input paths
-IN = sys.argv[1]
-
-# output paths
-OUT = sys.argv[2] # 'repseqs_lengths_describe.md'
-
-s = pd.read_csv(IN, header=None, index_col=0, sep='\t')
-t = s.describe()
-outstr = tabulate(t.iloc[1:], tablefmt="pipe", headers=['Statistic (n=%s)' % t.iloc[0].values[0].astype(int), 'Sequence length'])
-with open(OUT, 'w') as target:
-    target.write(outstr)
-    target.write('\n')
-
diff --git a/scripts/summarize_metadata.py b/scripts/summarize_metadata.py
deleted file mode 100644
index 2bc7f5d..0000000
--- a/scripts/summarize_metadata.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-
-import pandas as pd
-from tabulate import tabulate
-import sys
-
-# usage
-usage = '''
-summarize_metadata.py metadata output1_md output2_txt
-
-'''
-
-if len(sys.argv) < 2:
-    print(usage)
-    sys.exit()
-
-# input paths
-metadata = sys.argv[1]
-
-# output paths
-output1 = sys.argv[2] # 'manifest_pe.csv'
-output2 = sys.argv[3] # 'manifest_se.csv'
-
-df = pd.read_csv(metadata, sep='\t')
-cols = df.columns
-df2 = pd.DataFrame(columns =[0,1], index=cols)
-for col in cols:
-    if col in df.columns:
-        vc = df[col].value_counts()
-        if vc.index.shape == (0,):
-            df2.loc[col, 0] = '(no values in column)'
-            df2.loc[col, 1] = '--'
-        else:
-            df2.loc[col, 0] = vc.index[0]
-            df2.loc[col, 1] = vc.values[0]
-    else:
-        df2.loc[col, 0] = '(column not provided)'
-        df2.loc[col, 1] = '--'
-df2.columns = ['Most common value', 'Count']
-df2.index.name = 'Column name'
-outstr = tabulate(df2, tablefmt="pipe", headers="keys")
-with open(output1, 'w') as target:
-    target.write(outstr)
-    target.write('\n')
-with open(output2, 'w') as target:
-    [target.write('%s\n' % i) for i in cols]
diff --git a/tourmaline-qiime2-2023.2-py38-osx-M1-conda-minimal-sorted.yml b/tourmaline-qiime2-2023.2-py38-osx-M1-conda-minimal-sorted.yml
new file mode 100644
index 0000000..fa0617d
--- /dev/null
+++ b/tourmaline-qiime2-2023.2-py38-osx-M1-conda-minimal-sorted.yml
@@ -0,0 +1,635 @@
+channels:
+  - qiime2/label/r2023.2
+  - conda-forge
+  - bioconda
+  - defaults
+dependencies:
+  - _r-mutex=1.0.1
+  - altair=4.2.2
+  - anyio=3.6.2
+  - appnope=0.1.3
+  - argcomplete=2.0.0
+  - argon2-cffi-bindings=21.2.0
+  - argon2-cffi=21.3.0
+  - astor=0.8.1
+  - asttokens=2.2.1
+  - atpublic=3.0.1
+  - attrs=22.2.0
+  - backcall=0.2.0
+  - backports.functools_lru_cache=1.6.4
+  - backports=1.0
+  - beautifulsoup4=4.11.2
+  - bibtexparser=1.4.0
+  - bioconductor-ancombc=2.0.1
+  - bioconductor-beachmat=2.14.0
+  - bioconductor-biobase=2.58.0
+  - bioconductor-biocbaseutils=1.0.0
+  - bioconductor-biocgenerics=0.44.0
+  - bioconductor-biocneighbors=1.16.0
+  - bioconductor-biocparallel=1.32.5
+  - bioconductor-biocsingular=1.14.0
+  - bioconductor-biomformat=1.26.0
+  - bioconductor-biostrings=2.66.0
+  - bioconductor-dada2=1.26.0
+  - bioconductor-data-packages=20230202
+  - bioconductor-decipher=2.26.0
+  - bioconductor-decontam=1.18.0
+  - bioconductor-delayedarray=0.24.0
+  - bioconductor-delayedmatrixstats=1.20.0
+  - bioconductor-dirichletmultinomial=1.40.0
+  - bioconductor-genomeinfodb=1.34.8
+  - bioconductor-genomeinfodbdata=1.2.9
+  - bioconductor-genomicalignments=1.34.0
+  - bioconductor-genomicranges=1.50.0
+  - bioconductor-iranges=2.32.0
+  - bioconductor-matrixgenerics=1.10.0
+  - bioconductor-mia=1.6.0
+  - bioconductor-msa
+  - bioconductor-multiassayexperiment=1.24.0
+  - bioconductor-multtest=2.54.0
+  - bioconductor-odseq
+  - bioconductor-phyloseq=1.42.0
+  - bioconductor-rhdf5=2.42.0
+  - bioconductor-rhdf5filters=1.10.0
+  - bioconductor-rhdf5lib=1.20.0
+  - bioconductor-rhtslib=2.0.0
+  - bioconductor-rsamtools=2.14.0
+  - bioconductor-s4vectors=0.36.0
+  - bioconductor-scaledmatrix=1.6.0
+  - bioconductor-scater=1.26.0
+  - bioconductor-scuttle=1.8.0
+  - bioconductor-shortread=1.56.0
+  - bioconductor-singlecellexperiment=1.20.0
+  - bioconductor-sparsematrixstats=1.10.0
+  - bioconductor-summarizedexperiment=1.28.0
+  - bioconductor-treeio=1.22.0
+  - bioconductor-treesummarizedexperiment=2.6.0
+  - bioconductor-xvector=0.38.0
+  - bioconductor-zlibbioc=1.44.0
+  - biom-format=2.1.12
+  - biopython
+  - blast=2.12.0
+  - bleach=6.0.0
+  - bokeh=3.0.3
+  - bowtie2=2.5.1
+  - brotli-bin=1.0.9
+  - brotli=1.0.9
+  - brotlipy=0.7.0
+  - bwidget=1.9.14
+  - bzip2=1.0.8
+  - c-ares=1.18.1
+  - ca-certificates=2022.12.7
+  - cachecontrol=0.12.11
+  - cached_property=1.5.2
+  - cairo=1.16.0
+  - cctools_osx-64=973.0.1
+  - certifi
+  - cffi=1.15.1
+  - charset-normalizer=2.1.1
+  - clang-14=14.0.6
+  - clang=14.0.6
+  - clang_osx-64=14.0.6
+  - clangxx=14.0.6
+  - clangxx_osx-64=14.0.6
+  - click=8.1.3
+  - clustalo
+  - colorama=0.4.6
+  - comm=0.1.2
+  - compiler-rt=14.0.6
+  - compiler-rt_osx-64=14.0.6
+  - contourpy=1.0.7
+  - cryptography=39.0.1
+  - curl=7.88.1
+  - cutadapt=4.2
+  - cycler=0.11.0
+  - cython=0.29.33
+  - deblur=1.1.1
+  - debugpy=1.6.6
+  - decorator=4.4.2
+  - defusedxml=0.7.1
+  - deicode
+  - dendropy=4.5.2
+  - dill=0.3.6
+  - dnaio=0.10.0
+  - emperor=1.0.3
+  - entrez-direct=16.2
+  - entrypoints=0.4
+  - exceptiongroup=1.1.0
+  - executing=1.2.0
+  - expat=2.5.0
+  - fastcluster=1.2.6
+  - fasttree=2.1.11
+  - flit-core=3.8.0
+  - flufl.lock=7.1
+  - font-ttf-dejavu-sans-mono=2.37
+  - font-ttf-inconsolata=3.000
+  - font-ttf-source-code-pro=2.038
+  - font-ttf-ubuntu=0.83
+  - fontconfig=2.14.2
+  - fonts-conda-ecosystem=1
+  - fonts-conda-forge=1
+  - fonttools=4.38.0
+  - formulaic=0.5.2
+  - freetype=2.12.1
+  - fribidi=1.0.10
+  - future=0.18.3
+  - gettext=0.21.1
+  - gfortran_impl_osx-64=11.3.0
+  - gfortran_osx-64=11.3.0
+  - glpk=5.0
+  - gmp=6.2.1
+  - gneiss=0.4.6
+  - graphite2=1.3.13
+  - graphlib-backport=1.0.3
+  - gsl=2.7
+  - h5py=2.10.0
+  - harfbuzz=6.0.0
+  - hdf5=1.10.6
+  - hdmedians=0.14.2
+  - hmmer=3.1b2
+  - htslib=1.16
+  - icu=70.1
+  - idna=3.4
+  - ijson=3.2.0.post0
+  - importlib-metadata=4.8.3
+  - importlib_metadata=4.8.3
+  - importlib_resources=5.12.0
+  - iniconfig=2.0.0
+  - interface_meta=1.3.0
+  - iow=1.0.5
+  - ipykernel=6.21.2
+  - ipython=8.10.0
+  - ipython_genutils=0.2.0
+  - ipywidgets=8.0.4
+  - iqtree=2.2.0.3
+  - isa-l=2.30.0
+  - isl=0.25
+  - jedi=0.18.2
+  - jinja2=3.1.2
+  - joblib=1.2.0
+  - jpeg=9e
+  - jq=1.6
+  - jsonschema=4.17.3
+  - jupyter_client=8.0.3
+  - jupyter_core=5.2.0
+  - jupyter_events=0.6.3
+  - jupyter_server=2.3.0
+  - jupyter_server_terminals=0.4.4
+  - jupyterlab_pygments=0.2.2
+  - jupyterlab_widgets=3.0.5
+  - kiwisolver=1.4.4
+  - krb5=1.20.1
+  - lcms2=2.14
+  - ld64_osx-64=609
+  - lerc=4.0.0
+  - libblas=3.9.0
+  - libbrotlicommon=1.0.9
+  - libbrotlidec=1.0.9
+  - libbrotlienc=1.0.9
+  - libcblas=3.9.0
+  - libclang-cpp14=14.0.6
+  - libcurl=7.88.1
+  - libcxx=15.0.7
+  - libdeflate=1.13
+  - libedit=3.1.20191231
+  - libev=4.33
+  - libffi=3.4.2
+  - libgfortran-devel_osx-64=11.3.0
+  - libgfortran5=11.3.0
+  - libgfortran=5.0.0
+  - libglib=2.74.1
+  - libiconv=1.17
+  - libidn2=2.3.4
+  - liblapack=3.9.0
+  - liblapacke=3.9.0
+  - libllvm11=11.1.0
+  - libllvm14=14.0.6
+  - libnghttp2=1.51.0
+  - libopenblas=0.3.21
+  - libpng=1.6.39
+  - libsodium=1.0.18
+  - libsqlite=3.40.0
+  - libssh2=1.10.0
+  - libtiff=4.4.0
+  - libunistring=0.9.10
+  - libwebp-base=1.2.4
+  - libxcb=1.13
+  - libxml2=2.10.3
+  - libxslt=1.1.37
+  - libzlib=1.2.13
+  - llvm-openmp=15.0.7
+  - llvm-tools=14.0.6
+  - llvmlite=0.39.1
+  - lockfile=0.12.2
+  - lxml=4.9.2
+  - lz4-c=1.9.4
+  - lz4=4.3.2
+  - mafft=7.515
+  - make=4.3
+  - markupsafe=2.1.2
+  - matplotlib-base=3.6.0
+  - matplotlib-inline=0.1.6
+  - matplotlib=3.6.0
+  - mistune=2.0.5
+  - mpc=1.3.1
+  - mpfr=4.1.0
+  - msgpack-python=1.0.4
+  - munkres=1.1.4
+  - muscle
+  - natsort=8.3.0
+  - nbclassic=0.5.2
+  - nbclient=0.7.2
+  - nbconvert-core=7.2.9
+  - nbconvert-pandoc=7.2.9
+  - nbconvert=7.2.9
+  - nbformat=5.7.3
+  - ncurses=6.3
+  - nest-asyncio=1.5.6
+  - networkx=3.0
+  - nlopt=2.7.1
+  - nose=1.3.7
+  - notebook-shim=0.2.2
+  - notebook=6.5.2
+  - numba=0.56.4
+  - numpy=1.23.5
+  - oniguruma=6.9.8
+  - openjdk=17.0.3
+  - openjpeg=2.5.0
+  - openssl=3.0.8
+  - packaging=23.0
+  - pandas=1.5.3
+  - pandoc=2.19.2
+  - pandocfilters=1.5.0
+  - pango=1.50.13
+  - parso=0.8.3
+  - patsy=0.5.3
+  - pbzip2=1.1.13
+  - pcre2=10.40
+  - pcre=8.45
+  - perl-archive-tar=2.40
+  - perl-carp=1.50
+  - perl-common-sense=3.75
+  - perl-compress-raw-bzip2=2.201
+  - perl-compress-raw-zlib=2.202
+  - perl-encode=3.19
+  - perl-exporter-tiny=1.002002
+  - perl-exporter=5.74
+  - perl-extutils-makemaker=7.66
+  - perl-io-compress=2.201
+  - perl-io-zlib=1.14
+  - perl-json-xs=2.34
+  - perl-json=4.10
+  - perl-list-moreutils-xs=0.430
+  - perl-list-moreutils=0.430
+  - perl-parent=0.241
+  - perl-pathtools=3.75
+  - perl-scalar-list-utils=1.63
+  - perl-storable=3.15
+  - perl-types-serialiser=1.01
+  - perl=5.32.1
+  - pexpect=4.8.0
+  - pickleshare=0.7.5
+  - pigz=2.6
+  - pillow=9.2.0
+  - pip=23.0.1
+  - pip:
+      - empress
+  - pixman=0.40.0
+  - pkgutil-resolve-name=1.3.10
+  - platformdirs=3.0.0
+  - pluggy=1.0.0
+  - prometheus_client=0.16.0
+  - prompt-toolkit=3.0.36
+  - psutil=5.9.4
+  - pthread-stubs=0.4
+  - ptyprocess=0.7.0
+  - pure_eval=0.2.2
+  - pycparser=2.21
+  - pygments=2.14.0
+  - pynndescent=0.5.8
+  - pyopenssl=23.0.0
+  - pyparsing=3.0.9
+  - pyrsistent=0.19.3
+  - pysocks=1.7.1
+  - pytest=7.2.1
+  - python-dateutil=2.8.2
+  - python-fastjsonschema=2.16.3
+  - python-isal=1.1.0
+  - python-json-logger=2.0.7
+  - python=3.8.16
+  - python_abi=3.8
+  - pytz=2022.7.1
+  - pyyaml=6.0
+  - pyzmq=25.0.0
+  - q2-alignment=2023.2.0
+  - q2-composition=2023.2.0
+  - q2-cutadapt=2023.2.0
+  - q2-dada2=2023.2.0
+  - q2-deblur=2023.2.0
+  - q2-demux=2023.2.0
+  - q2-diversity-lib=2023.2.0
+  - q2-diversity=2023.2.0
+  - q2-emperor=2023.2.0
+  - q2-feature-classifier=2023.2.0
+  - q2-feature-table=2023.2.0
+  - q2-fragment-insertion=2023.2.0
+  - q2-gneiss=2023.2.0
+  - q2-longitudinal=2023.2.0
+  - q2-metadata=2023.2.0
+  - q2-mystery-stew=2023.2.0
+  - q2-phylogeny=2023.2.0
+  - q2-quality-control=2023.2.0
+  - q2-quality-filter=2023.2.0
+  - q2-sample-classifier=2023.2.0
+  - q2-taxa=2023.2.0
+  - q2-types=2023.2.0
+  - q2-vsearch=2023.2.0
+  - q2cli=2023.2.0
+  - q2galaxy=2023.2.0
+  - q2templates=2023.2.0
+  - qiime2=2023.2.0
+  - r-acepack=1.4.1
+  - r-ade4=1.7_22
+  - r-ape=5.7
+  - r-askpass=1.1
+  - r-assertthat=0.2.1
+  - r-backports=1.4.1
+  - r-base=4.2.2
+  - r-base64enc=0.1_3
+  - r-beeswarm=0.4.0
+  - r-bh=1.81.0_1
+  - r-bibtex=0.5.1
+  - r-bit64=4.0.5
+  - r-bit=4.0.5
+  - r-bitops=1.0_7
+  - r-blob=1.2.3
+  - r-boot=1.3_28.1
+  - r-broom=1.0.3
+  - r-bslib=0.4.2
+  - r-cachem=1.0.7
+  - r-cairo=1.6_0
+  - r-callr=3.7.3
+  - r-cellranger=1.1.0
+  - r-checkmate=2.1.0
+  - r-class=7.3_21
+  - r-cli=3.6.0
+  - r-clipr=0.8.0
+  - r-cluster=2.1.4
+  - r-codetools=0.2_19
+  - r-colorspace=2.1_0
+  - r-cpp11=0.4.3
+  - r-crayon=1.5.2
+  - r-curl=4.3.3
+  - r-cvxr=1.0_11
+  - r-data.table=1.14.8
+  - r-dbi=1.1.3
+  - r-dbplyr=2.3.1
+  - r-deldir=1.0_6
+  - r-desctools=0.99.48
+  - r-digest=0.6.31
+  - r-doparallel=1.0.17
+  - r-dorng=1.8.6
+  - r-dplyr=1.1.0
+  - r-dqrng=0.3.0
+  - r-dtplyr=1.3.0
+  - r-e1071=1.7_13
+  - r-ecosolver=0.5.4
+  - r-ellipsis=0.3.2
+  - r-emmeans=1.8.4_1
+  - r-energy=1.7_11
+  - r-estimability=1.4.1
+  - r-evaluate=0.20
+  - r-exact=3.2
+  - r-expm=0.999_7
+  - r-fansi=1.0.4
+  - r-farver=2.1.1
+  - r-fastmap=1.1.1
+  - r-fnn=1.1.3.1
+  - r-forcats=1.0.0
+  - r-foreach=1.5.2
+  - r-foreign=0.8_84
+  - r-formatr=1.14
+  - r-formula=1.2_5
+  - r-frictionless=1.0.2
+  - r-fs=1.6.1
+  - r-futile.logger=1.4.3
+  - r-futile.options=1.0.1
+  - r-gargle=1.3.0
+  - r-gbrd=0.4_11
+  - r-generics=0.1.3
+  - r-getopt=1.20.3
+  - r-ggbeeswarm=0.7.1
+  - r-ggplot2=3.4.1
+  - r-ggrastr=1.0.1
+  - r-ggrepel=0.9.3
+  - r-gld=2.6.6
+  - r-glue=1.6.2
+  - r-gmp=0.7_1
+  - r-googledrive=2.0.0
+  - r-googlesheets4=1.0.1
+  - r-gridextra=2.3
+  - r-gsl=2.1_8
+  - r-gtable=0.3.1
+  - r-haven=2.5.1
+  - r-highr=0.10
+  - r-hmisc=4.8_0
+  - r-hms=1.1.2
+  - r-htmltable=2.4.1
+  - r-htmltools=0.5.4
+  - r-htmlwidgets=1.6.1
+  - r-httr=1.4.5
+  - r-hwriter=1.3.2.1
+  - r-ids=1.0.1
+  - r-igraph=1.4.1
+  - r-interp=1.1_3
+  - r-irlba=2.3.5.1
+  - r-isoband=0.2.7
+  - r-iterators=1.0.14
+  - r-jpeg=0.1_10
+  - r-jquerylib=0.1.4
+  - r-jsonlite=1.8.4
+  - r-knitr=1.42
+  - r-labeling=0.4.2
+  - r-lambda.r=1.2.4
+  - r-lattice=0.20_45
+  - r-latticeextra=0.6_30
+  - r-lazyeval=0.2.2
+  - r-lifecycle=1.0.3
+  - r-lme4=1.1_31
+  - r-lmertest=3.1_3
+  - r-lmom=2.9
+  - r-lubridate=1.9.2
+  - r-magrittr=2.0.3
+  - r-mass=7.3_58.2
+  - r-matrix=1.5_3
+  - r-matrixstats=0.63.0
+  - r-memoise=2.0.1
+  - r-mgcv=1.8_41
+  - r-mime=0.12
+  - r-minqa=1.2.5
+  - r-modelr=0.1.10
+  - r-munsell=0.5.0
+  - r-mvtnorm=1.1_3
+  - r-nlme=3.1_162
+  - r-nloptr=2.0.3
+  - r-nnet=7.3_18
+  - r-numderiv=2016.8_1.1
+  - r-openssl=2.0.5
+  - r-optparse=1.7.3
+  - r-osqp=0.6.0.8
+  - r-permute=0.9_7
+  - r-pheatmap=1.0.12
+  - r-pillar=1.8.1
+  - r-pixmap=0.4_12
+  - r-pkgconfig=2.0.3
+  - r-pkgmaker=0.32.8
+  - r-plogr=0.2.0
+  - r-plyr=1.8.8
+  - r-png=0.1_8
+  - r-prettyunits=1.1.1
+  - r-processx=3.8.0
+  - r-progress=1.2.2
+  - r-proxy=0.4_27
+  - r-ps=1.7.2
+  - r-purrr=1.0.1
+  - r-r6=2.5.1
+  - r-ragg=1.2.4
+  - r-rappdirs=0.3.3
+  - r-rbibutils=2.2.13
+  - r-rcolorbrewer=1.1_3
+  - r-rcpp=1.0.10
+  - r-rcppannoy=0.0.20
+  - r-rcppeigen=0.3.3.9.3
+  - r-rcpphnsw=0.4.1
+  - r-rcppml=0.3.7
+  - r-rcppparallel=5.1.6
+  - r-rcppprogress=0.4.2
+  - r-rcurl=1.98_1.10
+  - r-rdpack=2.4
+  - r-readr=2.1.4
+  - r-readxl=1.4.2
+  - r-registry=0.5_1
+  - r-rematch2=2.1.2
+  - r-rematch=1.0.1
+  - r-reprex=2.0.2
+  - r-reshape2=1.4.4
+  - r-rlang=1.0.6
+  - r-rmarkdown=2.20
+  - r-rmpfr=0.9_1
+  - r-rngtools=1.5.2
+  - r-rootsolve=1.8.2.3
+  - r-rpart=4.1.19
+  - r-rspectra=0.16_1
+  - r-rsqlite=2.2.20
+  - r-rstudioapi=0.14
+  - r-rsvd=1.0.5
+  - r-rtsne=0.16
+  - r-rvest=1.0.3
+  - r-sass=0.4.5
+  - r-scales=1.2.1
+  - r-scs=3.0_1
+  - r-selectr=0.4_2
+  - r-sitmo=2.0.2
+  - r-snow=0.4_4
+  - r-sp=1.6_0
+  - r-statmod=1.5.0
+  - r-stringi=1.7.12
+  - r-stringr=1.5.0
+  - r-survival=3.5_3
+  - r-sys=3.4.1
+  - r-systemfonts=1.0.4
+  - r-textshaping=0.3.6
+  - r-tibble=3.1.8
+  - r-tidyr=1.3.0
+  - r-tidyselect=1.2.0
+  - r-tidytree=0.4.2
+  - r-tidyverse=1.3.2
+  - r-timechange=0.2.0
+  - r-tinytex=0.44
+  - r-tzdb=0.3.0
+  - r-utf8=1.2.3
+  - r-uuid=1.1_0
+  - r-uwot=0.1.14
+  - r-vctrs=0.5.2
+  - r-vegan=2.6_4
+  - r-vipor=0.4.5
+  - r-viridis=0.6.2
+  - r-viridislite=0.4.1
+  - r-vroom=1.6.1
+  - r-withr=2.5.0
+  - r-xfun=0.37
+  - r-xml2=1.3.3
+  - r-xtable=1.8_4
+  - r-yaml=2.3.7
+  - r-yulab.utils=0.0.6
+  - raxml=8.2.12
+  - readline=8.1.2
+  - requests=2.28.2
+  - rfc3339-validator=0.1.4
+  - rfc3986-validator=0.1.1
+  - samtools=1.16.1
+  - scikit-bio=0.5.7
+  - scikit-learn=0.24.1
+  - scipy=1.8.1
+  - seaborn-base=0.12.2
+  - seaborn=0.12.2
+  - send2trash=1.8.0
+  - sepp=4.3.10
+  - setuptools=67.4.0
+  - sigtool=0.1.3
+  - six=1.16.0
+  - snakemake
+  - sniffio=1.3.0
+  - sortmerna=2.0
+  - soupsieve=2.3.2.post1
+  - stack_data=0.6.2
+  - statsmodels=0.13.5
+  - tabulate
+  - tapi=1100.0.11
+  - tbb=2021.8.0
+  - terminado=0.17.1
+  - threadpoolctl=3.1.0
+  - tinycss2=1.2.1
+  - tk=8.6.12
+  - tktable=2.10
+  - toml=0.10.2
+  - tomli=2.0.1
+  - toolz=0.12.0
+  - tornado=6.2
+  - tqdm=4.64.1
+  - traitlets=5.9.0
+  - typing-extensions=4.4.0
+  - typing_extensions=4.4.0
+  - tzlocal=2.1
+  - umap-learn=0.5.3
+  - unicodedata2=15.0.0
+  - unifrac-binaries=1.1.1
+  - unifrac=1.1.1
+  - urllib3=1.26.14
+  - vsearch=2.22.1
+  - wcwidth=0.2.6
+  - webencodings=0.5.1
+  - websocket-client=1.5.1
+  - wget=1.20.3
+  - wheel=0.38.4
+  - widgetsnbextension=4.0.5
+  - wrapt=1.15.0
+  - xmltodict=0.13.0
+  - xopen=1.7.0
+  - xorg-kbproto=1.0.7
+  - xorg-libice=1.0.10
+  - xorg-libsm=1.2.3
+  - xorg-libx11=1.7.2
+  - xorg-libxau=1.0.9
+  - xorg-libxdmcp=1.1.3
+  - xorg-libxt=1.2.1
+  - xorg-xproto=7.0.31
+  - xyzservices=2023.2.0
+  - xz=5.2.6
+  - yaml=0.2.5
+  - yq=3.1.1
+  - zeromq=4.3.4
+  - zipp=3.15.0
+  - zlib=1.2.13
+  - zstandard=0.19.0
+  - zstd=1.5.2