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Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows
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.
Littérature citée [2 références]
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 26 mars 2020 - 20:49:29
Document(s) archivé(s) le : mardi 11 avril 2017 - 20:08:26
Contributeur : Elsa Bernard <>
Soumis le : jeudi 21 août 2014 - 11:13:44
Dernière modification le : jeudi 26 mars 2020 - 20:49:29
Document(s) archivé(s) le : mardi 11 avril 2017 - 20:08:26
