kisSplice, détection d'évènements d'épissages alternatifs dans les données RNA-seq

Gustavo Akio Tominaga Sacomoto 1 J. Kielbassa 1 Pavlos Antoniou 2 Rayan Chikhi 2 Raluca Uricaru 3 Marie-France Sagot 1, * Pierre Peterlongo 2 Vincent Lacroix 1
* Corresponding author
1 BAMBOO - An algorithmic view on genomes, cells, and environments
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
2 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper, we address the problem of identifying polymorphisms in RNA-seq data when no reference genome is available, without performing an assembly of the transcripts. Based on the fundamental idea that each polymor- phism will correspond 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 to extract alternative splic- ing events and show that it enables to identify more correct events than current transcriptome assemblers. Additionally, when we applied our method on a 71M reads dataset from human, we were able to identify 3884 events, out of which 57% are not present in the annotations, which con rms recent estimates show- ing that the complexity of alternative splicing has been largely underestimated so far.
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Gustavo Akio Tominaga Sacomoto, J. Kielbassa, Pavlos Antoniou, Rayan Chikhi, Raluca Uricaru, et al.. kisSplice, détection d'évènements d'épissages alternatifs dans les données RNA-seq. [Research Report] RR-7852, INRIA. 2011, pp.12. ⟨hal-00654249v2⟩

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