In RNA Sequencing, there are a lot of analysis we can perform. For example, a standard pipeline will involve:
- Alignment of the reads (STAR, RUM, TopHat, MapSplice, GSNAP etc)
- Gene Counting (HTSeq, Cufflink etc)
- Differential gene expression analysis (DESeq, EdgeR, Cuffdiff etc)
- Functional Annotation (DAVID)
- Pathway Analysis (SPIA, GSEA, WGCNA etc)
This set of analysis should more or less gives us certain biological idea of what is happening to our sample. However, in recent attempts to analyse our RNA Sequencing data, it was found that it is very difficult to interpret the differential exon usage results, or in general, differential isoform expression. A possible pipeline will be something like
Alignment -> Gene Counting -> Differential gene expression analysis (DEXSeq)
Alignment -> MATS (or other tools)
Alternatively We can also try De novo assembly with Trinity or OASIS to construct the transcriptome or just align to the transcriptome of our organism, then pipe to RSEM and the perform EBSeq analysis.
But what afterwards? After obtaining the differential gene expression, we can try different from of functional annotation, but it is much more difficult for us to perform functional annotation on the different isoforms or exon. When you have more than a 1000 differentially expressed exons, how do you know exactly what's wrong in your condition or what is happening?
tldr: What are your usual practice to explain the results from differential exon usage / differential isoform expression analysis especially when you don't have a defined set of candidate genes?