I am thinking about generating read count matrix at both gene-level and transcript (isoform)-level.
According to a previous post:
Why run FeatureCounts after Stringtie? (Galaxy recommends!)
How to get read counts on transcript level using featurecounts?
It seems that I can use FeatureCounts
for gene quantification and Stringtie
for transcript/isoform quantification, am I right?
Since transcripts are heavily overlapping, featurecounts cannot properly sort out reads mapping to the same exon, thus is not suitable to count transcripts/isoforms. Then how this can be overcome in Stringtie? Are common reads properly sorted using stringtie?
Many people suggested an alignment-free tool, Salmon
, for transcript quantification. Since I am interested to find both DE genes and DE transcripts/isoforms in my DE analysis, I assume Stringtie would be a more handy option since I can get both gene and transcript counts in one run.
Therefore, my question would be, is gene/transcript quantification reliable using Stringtie? How does it distribute common reads shared by multiple isoforms, which is the major problem to quantify isoforms.
I have read the original papers and related posts here in biostars but still not sure...appreciate it if someone can clarify this for me.
Thank you for your response. Will give it a try!
Thanks for this comment. Could you point to some example benchmarks you are referring to?