Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows

Elsa Bernard; Laurent Jacob; Julien Mairal; Jean-Philippe Vert

doi:10.1093/bioinformatics/btu317

Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows

Elsa Bernard ^{1, 2} Laurent Jacob ³ Julien Mairal ⁴ Jean-Philippe Vert ^{1, 2, *}

* Auteur correspondant

1 CBIO - Centre de Bioinformatique

2 Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe

3 LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558

4 LEAR - Learning and recognition in vision

Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble

Abstract : Several state-of-the-art methods for isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the--possibly exponentially-- large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1-penalty are either restricted to genes with few exons, or only run the regression algorithm on a small set of pre-selected isoforms. We introduce a new technique called FlipFlop which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available. Source code is freely available as an R package from the Bioconductor web site (http://www.bioconductor.org/) and more information is available at http://cbio.ensmp.fr/flipflop.

Keywords : RNA-seq isoforms graph flow lasso

Type de document :

Article dans une revue

Bioinformatics, Oxford University Press (OUP), 2014, 30 (17), pp.2447-2455. 〈10.1093/bioinformatics/btu317〉

Domaine :

Sciences du Vivant [q-bio] / Bio-Informatique, Biologie Systémique [q-bio.QM]

Informatique [cs] / Bio-informatique [q-bio.QM]

Informatique [cs] / Apprentissage [cs.LG]

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https://hal-mines-paristech.archives-ouvertes.fr/hal-00803134
Contributeur : Elsa Bernard <>
Soumis le : jeudi 21 août 2014 - 11:13:44
Dernière modification le : jeudi 21 mars 2019 - 14:51:22
Document(s) archivé(s) le : mardi 11 avril 2017 - 20:08:26

Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows

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Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows

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