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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, *
* Corresponding author
4 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 : 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. and more information is available at
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Submitted on : Tuesday, September 10, 2013 - 10:52:49 AM
Last modification on : Tuesday, October 19, 2021 - 11:13:04 PM
Long-term archiving on: : Thursday, April 6, 2017 - 5:00:29 PM



Elsa Bernard, Laurent Jacob, Julien Mairal, Jean-Philippe Vert. Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows. 2014, ⟨10.1093/bioinformatics/btu317⟩. ⟨hal-00803134v2⟩



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