Analysis of differential transcript usage (DTU) Recent breakthroughs in bioinformatics tools for quantification (e.g.
Cufflinks/Kallisto/Salmon etc.) and tools which can identify differential transcript usage (DTU) (e.g. DRIMSeq, Cufflinks etc.) mean that from RNA-seq data we can now relatively easy obtain a genome wide analysis of transcripts that are differentially used betwen conditions.

What can you use these results for? What systematic analysis does this transcript level data enable?
I will take the liberty of giving one possible answers to my own question – but I’m very interested in other answers.

One analysis type that such data enables is the analysis of transcript switches with predicted potential consequences. 

I myself have recently developed such a tool called IsoformSwitchAnalyzeR. IsoformSwitchAnalyzeR enables statistical identification (via DRIMSeq) of isoform switches with predicted functional consequences. The consequences analyzed can be chosen from a long list which includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc. The R package also enables easy visualization of isoform switches along with their consequences and it directly supports the output of Cufflinks/Cuffdiff, RSEM, Salmon and Kallisto.

Apart from enabling identification of interesting examples switch identification also enables systematic analysis of what genes are affected. For inspiration I will recommend one of my own articles (describing results obtained with IsoformSwitchAnalyzeR) as well Hector et al’s recent paper (which is not using IsoformSwitchAnalyzeR). Of particular interest and finesse is Hector’s analysis of how isoform switches can disrupt protein-protein interactions.

Looking forward to hear more ideas.