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)

1 CBIO - Centre de Bioinformatique
2 Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe
3 Statistique en grande dimension pour la génomique
4 LEAR - Learning and recognition in vision

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.

Mots clés: RNA-seq; isoforms; graph flow; lasso

Domaines: Bio-Informatique, Biologie Systémique [q-bio.QM]; Bio-informatique [q-bio.QM]; Apprentissage [cs.LG]

Elsa Bernard, Laurent Jacob, Julien Mairal, Jean-Philippe Vert. Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows. Bioinformatics, 2014, 30 (17), pp.2447-2455. ⟨10.1093/bioinformatics/btu317⟩. ⟨hal-00803134v3⟩