Can anyone with experience using aligners like Salmon and STAR for RNA-seq comment on the strengths and weakness of one method over another. So far I have performed the alignments and quantification and noticed some pronounced differences in the generated counts and I was curious to hear the thoughts of more experienced users.
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The internal mode should be the same as htseq-count. But what I said still stands; Salmon will be smarter than htseq or featureCounts at dealing with ambiguous reads.
Whether STAR + RSEM is significantly better than Salmon, that's a different question, because RSEM is smart about handling ambiguous reads
Correct; kallisto and salmon quantification are much better. See:
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STAR and salmon are using two methodologies. While STAR is aligning the reads, salmon is using
quasi-mapping. You can read more about the differences from @Rob Patro (author of salmon) here: C: Mapping vs Quasi-MappingI ended up finding this, which offers a nice overview of the differences (A: Could you explain the difference between STAR, KALLISTO, SALMON etc. to experime ). I am still curious to hear from people with experience working with both approaches to see if they ever encountered any issues.