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Using Salmon Quantification to Reduce Transcriptome Complexity

author
  1. Tessa Pierce
date

Nov 2, 2015

In many assemblies - especially de novo assemblies, done without a reference genome - there are many transcripts that don't have significant expression levels. This can interfere with quantification and annotation. We can use Salmon to eliminate many of these transcripts, by estimating their transcription levels and removing low-expressing transcripts.

NOTE: Richard Smith-Unna has suggested that a better approach is to use transrate to filter your contigs; it actually does this automatically in read mapping mode. For example, the "good contigs" output for the Nematostella data set processed in transrate.rst will end up in transrate_results/nema/good.nema.fa.

We'll start from the m3.xlarge Amazon machine booted & configured in salmon.rst. If you are just running this, you'll need to run the apt-get commands, install khmer, and mount the data snapshot before continuing.

First, create a working directory and download a full set of Salmon quant results (done on all the reads):

mkdir /mnt/reduced
cd /mnt/reduced

curl -O http://athyra.idyll.org/~t/nema-salmon-quant.tar.gz
tar xzf nema-salmon-quant.tar.gz

Reducing transcriptome numbers

Reduce transcriptome complexity by removing low coverage contigs. Use awk to sum the counts in the numReads column of each quant.sf file and print contignames that have counts above threshold (10 total counts):

awk '{x[$1] += $3} END {for(y in x) if (x[y] >10) print y}' *.quant/quant.sf | sort > contigs_over_threshold.txt

Then, use this list of contigs to subset the fasta reference, by getting (and running) a python script to subset the fasta file, keeping all entries that match the contig names above:

curl -O https://raw.githubusercontent.com/bluegenes/fasta_tools/master/extract_fasta.py
python extract_fasta.py /mnt/data/nema.fa contigs_over_threshold.txt

You'll see the results here:

ls -la /mnt/data/nema*

Evaluate the full and reduced transcriptomes using BUSCO.

Install BUSCO:

cd $HOME
curl -O http://busco.ezlab.org/files/BUSCO_v1.1b1.tar.gz
tar -zxf BUSCO_v1.1b1.tar.gz
cd BUSCO_v1.1b1/
chmod +x BUSCO_v1.1b1.py
export PATH=$PATH:$(pwd)

Run BUSCO on the filtered assembly:

mkdir /mnt/busco
cd /mnt/busco

#Download metazoa busco database
curl -LO http://busco.ezlab.org/files/metazoa_buscos.tar.gz
tar -zxf metazoa_buscos.tar.gz

python3 ~/BUSCO_v1.1b1/BUSCO_v1.1b1.py -m trans -in /mnt/data/nema_inList.fa \
   --cpu 16 -l metazoa -f -o nema_inList_metazoaBusco

python3 ~/BUSCO_v1.1b1/BUSCO_v1.1b1.py -m trans -in /mnt/data/nema.fa \
   --cpu 16 -l metazoa -f -o nema_all_metazoaBusco

less run*/short*

Visualize transcriptome coverage in R:

curl -O -L https://raw.githubusercontent.com/ngs-docs/2015-nov-adv-rna/master/plotTPM.R
Rscript plotTPM.R