I am thinking about generating read count matrix at both gene-level and transcript (isoform)-level.
According to a previous post:
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.