Hi There,
I am trying to find genes that express different isoforms in cancer vs normal RNA-seq data. The first thing that came to my mind was to do a differential expression analysis at exon level (I used edgeR for this)... then if I find a gene that express less number of exon compared to the annotation data for that gene, it means the gene is expressing a different isoform in cancer.
But this is clearly not the right way to do this, and I could not find any other way for this kind of analysis. I was wondering if someone has done this kind of analysis before can share his/her experience with me.
I really appreciate your help, Thanks, Sahel
3 answers
Hi JC, I actually took your advice and started to use Tophat/Cufflink a few weeks ago. I have 15 tumor and 5 matched normals (human RNA-seq). Everything works great except Cuffdiff. Right now I am running Cuffdiff on the merge.gtf file of all 20 samples, but it keeps running out of memory and looks to take weeks to run! (I am using 12 cpus with 50GB RAM). I was wondering if you experience the same thing or I am doing something wrong? I have another 100 sample coming in for this project, however it seems it is impossible to use tophat/cufflink with 100 samples! Thanks very much, :-)
An alternative IsoformSwitchAnalyzeR ( http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ ) which enables statistical identification of isoform switches with predicted functional consequences where the consequences can be chosen from a long list but includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc. IsoformSwitchAnalyzeR directly parses RSEM output (also support cufflinks/cuffdiff, Kallisto andSalmon). IsoformSwitchAnalyzeR
Hope this helps
Kristoffer
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