Introduction

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.

Publications

  1. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.
    Cite this
    Bernard E, Jacob L, Mairal J, Viara E, Vert JP, 2015-08-01 - BMC bioinformatics
  2. Efficient RNA isoform identification and quantification from RNA-Seq data with network flows.
    Cite this
    Bernard E, Jacob L, Mairal J, Vert JP, 2014-09-01 - Bioinformatics (Oxford, England)

Credits

  1. Elsa Bernard
    Developer

    INSERM U900, Paris

  2. Laurent Jacob
    Developer

    Laboratoire Biométrie et Biologie Evolutive, Université de Lyon

  3. Julien Mairal
    Developer

    Inria, LEAR Team

  4. Eric Viara
    Developer

    Sysra, 91330

  5. Jean-Philippe Vert
    Investigator

    INSERM U900, Paris

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Summary
AccessionBT006474
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByJean-Philippe Vert