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A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples

Elsa Bernard 1, 2, 3 Laurent Jacob 4 Julien Mairal 5 Eric Viara 6 Jean-Philippe Vert 2, 3, 1
4 Statistique en grande dimension pour la génomique
PEGASE - Département PEGASE [LBBE]
5 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop.
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Elsa Bernard, Laurent Jacob, Julien Mairal, Eric Viara, Jean-Philippe Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. BMC Bioinformatics, BioMed Central, 2015, 16 (1), pp.262:1-10. ⟨10.1186/s12859-015-0695-9⟩. ⟨hal-01123141v3⟩

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