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 KISSPLICE, to extract alternative splicing events. Results: We show 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. 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. KISSPLICE is available for download at http://alcovna.genouest.org/kissplice/. © 2012 Sacomoto et al.
CITATION STYLE
Sacomoto, G. A. T., Kielbassa, J., Chikhi, R., Uricaru, R., Antoniou, P., Sagot, M. F., … Lacroix, V. (2012). Kissplice: De-novo calling alternative splicing events from RNA-seq data. BMC Bioinformatics, 13(SUPPL.6). https://doi.org/10.1186/1471-2105-13-S6-S5
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