So I'm doing this undergraduate thesis in which I have to analyze possible differential transcript usage events for ACOT9.
I was told to download a FireBrowse file containing mRNA-seq analyses for BRCA called illuminahiseq_rnaseqv2-RSEM_isoforms_normalized (MD5), identify the raw expression of those ACOT9 isoforms, and apply a pseudocount transformation (I don't know why is it neccesary, it's already normalized, right?). I also had to identify data of primary tumor and healthy individuals (but the archive doesn't says anything like "tumor", "cancer", "healthy", or I haven't noticed, so I don't know how to identify them either). Next, perform a "pairwise analysis" to identify isoform switch (and somehow I should get this histogram that will help me identify potential significant isoform switch events).
He told me I could perform all those analysis in R or Excel (highly recommended me R). The thing is, I'm pretty new in bioinformatics, the last time I did some "bioinformatic" stuff it was during my first semester in a course which barely showed us some basic R.
May someone please tell me how can I do all of this? My supervisor won't answer my doubts because "you’re supposed to figure it out on your own", and I wanna do it, but I need some basic guidance.
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Damy ruyz •
1 answer
The edgeR package reads RSEM output (catchRSEM) and performs DTU analyses, see:
Baldoni PL#, Chen L#, Li M, Chen Y, Smyth GK (2025). Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR. Nucleic Acids Research 53(22), gkaf1305. https://doi.org/10.1093/nar/gkaf1305
edgeR works directly on the isoform counts and does not require transformations.
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