Section: New Results

De-novo calling alternative splicing events from RNA-seq data

We addressed the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we proposed a general model for all polymorphisms in such graphs. We then introduced an exact algorithm, called KisSplice , to extract alternative splicing events. The first version of KisSplice appeared in 2011, but several important improvements were implemented in 2012 [24] . The first improvement was the memory consumption, the new version is much more memory efficient and can handle datasets of approximately ${10}^8$ reads. The second was in the running time, the enumeration step can now be done in parallel, which results in a significant speedup in the overall running time. Finally, an improved event quantification step was added to the method.

Application-wise, we showed that KisSplice enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KisSplice identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far.