Gene Abundance Estimation with Salmon¶

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

Installing Salmon¶

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

Running Salmon¶

Go to the data directory and download the prokka annotated sequences, assembled metagenome, and fastq files:

mkdir -p /mnt/data
cd /mnt/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 /mnt/quant
cd /mnt/quant

Grab the nucleotide (*ffn) predicted protein regions from Prokka and link them here. Also grab the trimmed sequence data (*fq)

ln -fs /mnt/data/prokka_annotation/*ffn .
ln -fs /mnt/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

Working with count data¶

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.

Plotting the results¶

In Jupyter Notebook, open a new Python3 notebook and enter:

%matplotlib inline
import numpy
from pylab import *

In another cell:

cd /mnt/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, '*')

References¶

LICENSE: This documentation and all textual/graphic site content is licensed under the Creative Commons - 0 License (CC0) -- fork @ github. Presentations (PPT/PDF) and PDFs are the property of their respective owners and are under the terms indicated within the presentation.