Salmon is one of a breed of new, very fast RNAseq counting packages. Like Kallisto and Sailfish, Salmon counts fragments without doing up-front read mapping. Salmon can be used with edgeR and others to do differential expression analysis (if you are quantifying RNAseq data).
Today we will use it to get a handle on the relative distribution of genomic reads across the predicted protein regions.
The goals of this tutorial are to:
- Install salmon
- Use salmon to estimate gene coverage in our metagenome dataset
Extra resources:
- see the finished plotting notebook.
- see the extract-sequences.py script.
Download and extract the latest version of Salmon and add it to your PATH:
cd wget https://github.com/COMBINE-lab/salmon/releases/download/v0.7.2/Salmon-0.7.2_linux_x86_64.tar.gz tar -xvzf Salmon-0.7.2_linux_x86_64.tar.gz cd Salmon-0.7.2_linux_x86_64 export PATH=$PATH:$HOME/Salmon-0.7.2_linux_x86_64/bin
Go to the data directory and download the prokka annotated sequences, assembled metagenome, and fastq files:
cd ~ mkdir -p data cd data curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1976948.abundtrim.subset.pe.fq.gz curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1977249.abundtrim.subset.pe.fq.gz curl -L -O https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/prokka_annotation_assembly.tar.gz tar -xvzf prokka_annotation_assembly.tar.gz
Make a new directory for the quantification of data with Salmon:
mkdir quant cd quant
Grab the nucleotide (*ffn) predicted protein regions from Prokka and link them here. Also grab the trimmed sequence data (*fq)
ln -fs ~/data/prokka_annotation/*ffn . ln -fs ~/data/*.abundtrim.subset.pe.fq.gz .
Create the salmon index:
salmon index -t metagG.ffn -i transcript_index --type quasi -k 31
Salmon requires that paired reads be separated into two files. We can split the reads using the split-paired-reads.py from the khmer package:
for file in *.abundtrim.subset.pe.fq.gz
do
tail=.fq.gz
BASE=${file/$tail/}
split-paired-reads.py $BASE$tail -1 ${file/$tail/}.1.fq -2 ${file/$tail/}.2.fq
done
Now, we can quantify our reads against this reference:
for file in *.pe.1.fq
do
tail1=.abundtrim.subset.pe.1.fq
tail2=.abundtrim.subset.pe.2.fq
BASE=${file/$tail1/}
salmon quant -i transcript_index --libType IU \
-1 $BASE$tail1 -2 $BASE$tail2 -o $BASE.quant;
done
(Note that --libType must come before the read files!)
This will create a bunch of directories named after the fastq files that we just pushed through. Take a look at what files there are within one of these directories:
find SRR1976948.quant -type f
Now, the quant.sf files actually contain the relevant information about expression – take a look:
head -10 SRR1976948.quant/quant.sf
The first column contains the transcript names, and the fourth column is what we will want down the road - the normalized counts (TPM). However, they’re not in a convenient location / format for use; let's fix that.
Download the gather-counts.py script:
curl -L -O https://raw.githubusercontent.com/ngs-docs/2016-metagenomics-sio/master/gather-counts.py
and run it:
python2 ./gather-counts.py
This will give you a bunch of .counts files, which are processed from the quant.sf files and named for the directory from which they emanate.
In Jupyter Notebook, open a new Python3 notebook and enter:
%matplotlib inline import numpy from pylab import *
In another cell:
cd ~/data/quant
In another cell:
counts1 = [ x.split()[1] for x in open('SRR1976948.quant.counts')]
counts1 = [ float(x) for x in counts1[1:] ]
counts1 = numpy.array(counts1)
counts2 = [ x.split()[1] for x in open('SRR1977249.quant.counts')]
counts2 = [ float(x) for x in counts2[1:] ]
counts2 = numpy.array(counts2)
plot(counts1, counts2, '*')