text: user6674: 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?
  
  user6955: 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. 
  
original_text: 6674: <p>I recently discovered <a href="https://github.com/snakemake-workflows/rna-seq-star-deseq2" rel="nofollow noreferrer">this Snakemake pipeline</a> for RNASeq that uses STAR's quantMode to quantify gene expression for DESeq2 differential expression analysis.</p>
  
  <p>In the past I've always seen the workflow as:</p>
  
  <ol>
  <li>STAR alignment</li>
  <li>RSEM/HTSeqCount quantification</li>
  <li>DESeq2 analysis</li>
  </ol>
  
  <p>More recently I've also seen:</p>
  
  <ol>
  <li>Kallisto/Salmon pseudo-alignment quantification</li>
  <li>DESeq2/Sleuth analysis</li>
  </ol>
  
  <p>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?</p>
   6955: <p>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.)</p>
  
  <p>It ultimately depends on the research question which method will be most appropriate for you.</p>
  
  <h1>read counts</h1>
  
  <p>pros:</p>
  
  <ul>
  <li>relatively easy to understand concept-wise</li>
  <li>can be used to generate many different expression measures (FPKMs, TPM, CPM, etc.) and is, thus, more versatile in down-stream analyses</li>
  </ul>
  
  <p>cons:</p>
  
  <ul>
  <li>you can't distinguish isoforms (from the counts themselves)</li>
  <li>requires alignment and, thus, more time to obtain</li>
  </ul>
  
  <h1>salmon/kallisto:</h1>
  
  <p>pros:</p>
  
  <ul>
  <li>faster (due to lack of alignment step)</li>
  <li>quantifies (known) isoforms</li>
  </ul>
  
  <p>cons:</p>
  
  <ul>
  <li>expression values are more abstract, less easy to explain</li>
  <li>gene-level analyses will require some extra work</li>
  </ul>
  
  <h1>RSEM</h1>
  
  <p>Likely lies somewhere in between, still requiring alignments will make it slower. But it does give you expression values for isoforms. </p>
  