KISSPLICE: de-novo calling alternative splicing events from RNA-seq data

Background In this paper, we address 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 propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE 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. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/.