Introduction to Bulk RNAseq data analysis

Quantification of Gene Expression with Salmon

Exercise 1 - Create Salmon index

First ensure you are working in the correct directory

cd ~/Course_Materials/RNAseq 

1. Create concatenated trancriptome/genome reference file

cat references/Mus_musculus.GRCm38.cdna.chr14.fa.gz \
    references/Mus_musculus.GRCm38.dna_sm.chr14.fa.gz \
    > references/gentrome.chr14.fa.gz

2. Create decoy sequence list from the genomic fasta

echo "14" > references/decoys.txt

3. Use salmon index to create the index. You will need to provide three pieces of information:
   • the Transcript fasta file - references/gentrome.chr14.fa.gz
   • the decoys - references/decoys.txt
   • the salmon index - a directory to write the index to, use references/salmon_index_chr14

Also add -p 7 to the command to instruct salmon to use 7 threads/cores. To find the flags for the other three pieces of information use:

salmon index --help

Version Info: This is the most recent version of salmon.

Index
==========
Creates a salmon index.

Command Line Options:
  -v [ --version ]              print version string  
  -h [ --help ]                 produce help message  
  -t [ --transcripts ] arg  Transcript fasta file.  
  -k [ --kmerLen ] arg (=31)    The size of k-mers that should be used for the 
                                quasi index.  
  -i [ --index ] arg        salmon index.  
  --gencode                     This flag will expect the input transcript   
  ...  
  ...  
  ...  
  -d [ --decoys ] arg       Treat these sequences ids from the reference as
                                the decoys that may have sequence homologous to
                                some known transcript. for example in case of 
                                the genome, provide a list of chromosome name 
                                --- one per line  

salmon index \
    -t references/gentrome.chr14.fa.gz \
    -d references/decoys.txt \
    -p 7 \
    -i references/salmon_index_chr14

Exercise 2 - Quantify with Salmon

1. Make directory called salmon_output

mkdir salmon_output

2. Use salmon quant to quantify the gene expression from the raw fastq. To see all the options run salmon quant --help-reads. There are lot of possible parameters, we will need to provide the following:
   • salmon index - references/salmon_index
   • -l A - Salmon needs to use some information about the library preparation, we could explicitly give this, but it is easy enough for Salmon to Automatically infer this from the data.
   • File containing the #1 mates - fastq/SRR7657883.sra_1.fastq.gz
   • File containing the #2 mates - fastq/SRR7657883.sra_2.fastq.gz
   • Output quantification directory - salmon_output/SRR7657883
   • --gcBias - salmon can optionally correct for GC content bias, it is recommended to always use this
   • The number of threads to use - 7

salmon quant \
    -p 7 \
    -i references/salmon_index \
    --gcBias \
    -l A \
    -1 fastq/SRR7657883.sra_1.fastq.gz \
    -2 fastq/SRR7657883.sra_2.fastq.gz \
    -o salmon_output/SRR7657883

Exercise 3 - Run multiqc

1. Run multiqc on the salmon_output directory and create a report called Salmon_quantification_report in the salmon_output directory.

multiqc -z -n Salmon_quantification_report -o salmon_output salmon_output

\(\Rightarrow\) salmon_output/Salmon_quantification_report.html

2. Open the report and determine what percentage of the reads have been aligned to the transcriptome.

With Salmon we have ~85% of reads mapped to the transcriptome.