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BMC Bioinformatics. 2012 Apr 19;13 Suppl 6:S5. doi: 10.1186/1471-2105-13-S6-S5.

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

Author information

1
INRIA Grenoble Rhône-Alpes, France.

Abstract

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/.

PMID:
22537044
PMCID:
PMC3358658
DOI:
10.1186/1471-2105-13-S6-S5
[Indexed for MEDLINE]
Free PMC Article

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