Question: RNA-Seq analysis using STAR and Salmon
gravatar for rachel.kubik12
23 months ago by
rachel.kubik1280 wrote:

Hello! I am having some trouble figuring out how to use Salmon. I have around 30 different samples which I trimmed using bbmap then aligned them using STAR. I have all of the BAM files from this alignment. Should I merge them all before running Salmon or because they are all unique samples, do I run them separately? I also aligned them to the UMD3.1 cattle genome. Is this ok for Salmon or should I align it to a different reference? I am very new to all this and trying to teach myself as I go. So if anyone has any other sites that could help me out, that would be great!


rna-seq • 5.0k views
ADD COMMENTlink modified 23 months ago by aka001190 • written 23 months ago by rachel.kubik1280

There are a few conceptual issues that might help you in the analysis:

  1. You said you aligned the reads with STAR to the UMD3.1 cattle genome. With such alignments you cannot quantify using salmon. To use Salmon you'll need to work with a transcriptome, available from here (you'll want to download cDNA).

  2. If your interest is in finding unannotated splice sites or transcripts in the cow then you ought to be aligning to the genome as you did; you could then run a variety of tools to analyze the results; salmon doesn't do that.

  3. Salmon can be used to take STAR alignments to the transcriptome and quantify them. That is, you could feed it the STAR alignments to the ENSEMBL cDNA. It can also quantify directly from the reads by pseudoalignment (the distinction is explained here I don't know of a benchmark that has tested whether alignment with STAR + Salmon is better or worse than Salmon with pseudoalignment.

  4. You asked about whether to quantify the samples jointly or not. That depends on the downstream analysis you will perform with them.

ADD REPLYlink written 23 months ago by Lior Pachter370
gravatar for Friederike
23 months ago by
United States
Friederike5.1k wrote:

Salmon would typically be used instead of STAR, not in addition to.

The typical workflow is:

  1. raw read QC using FastQC
  2. trimming (if necessary)
  3. alignment, e.g. using STAR
  4. counting reads that overlap with genes, e.g. using featureCounts (alternatively, Salmon or Kallisto will omit step 3 and directly produce the read counts per transcript)
  5. differential gene expression analysis

Bioconductor has a couple of great and detailed workflows:

RNA-seqlopedia is a very comprehensive source of information specifically for RNA-seq.

The course notes here are similarly detailed, but a bit more focused just on plain differential gene expression analysis.

ADD COMMENTlink written 23 months ago by Friederike5.1k
gravatar for aka001
23 months ago by
aka001190 wrote:

As you are mentioning Salmon, I would guess you want to count at the transcript level. You should run it for individual BAM files (without merging). As long as you have the reference (that is, annotation file of the transcriptome, not the genome file itself), Salmon should be usable.

If you want to do counting at the gene level, you would probably want to use featureCounts or --quantMode GeneCounts option with STAR.

ADD COMMENTlink written 23 months ago by aka001190

If you want gene abundances, you should consider using salmon and then aggregating to the gene level using tximport, this will generally be more accurate than a read counting pipeline.

ADD REPLYlink written 23 months ago by Rob3.5k
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