STAR quantMode vs RSEM vs Kallisto I recently discovered this Snakemake pipeline for RNASeq that uses STAR's quantMode to quantify gene expression for DESeq2 differential expression analysis.

In the past I've always seen the workflow as:


STAR alignment
RSEM/HTSeqCount quantification
DESeq2 analysis


More recently I've also seen:


Kallisto/Salmon pseudo-alignment quantification
DESeq2/Sleuth analysis


I tried searching online for differences between STAR quantMode vs other quantification algorithms but couldn't find many details. What are the benefits and drawbacks of using STAR quantMode vs RSEM/Kallisto/Salmon?
STAR quantMode (GeneCounts) essentially provides the same output as HTSeq-Count would, ie. number of reads that cover a given gene. This is the most simple measure of expression you could get from RNA-seq data. Kallisto and Salmon utilize pseudo-alignment to determine expression measures of transcripts (as opposed to genes). RSEM uses some algorithm to determine isoform fractions, but still based on alignments. (Note that I don't have any personal experience using RSEM.)

It ultimately depends on the research question which method will be most appropriate for you.

read counts

pros:


relatively easy to understand concept-wise
can be used to generate many different expression measures (FPKMs, TPM, CPM, etc.) and is, thus, more versatile in down-stream analyses


cons:


you can't distinguish isoforms (from the counts themselves)
requires alignment and, thus, more time to obtain


salmon/kallisto:

pros:


faster (due to lack of alignment step)
quantifies (known) isoforms


cons:


expression values are more abstract, less easy to explain
gene-level analyses will require some extra work


RSEM

Likely lies somewhere in between, still requiring alignments will make it slower. But it does give you expression values for isoforms.