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Efficient RNA isoform identification and quantification from RNA-Seq data with network flows

Banff International Research Station for Mathematical Innovation and Discovery

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 preselected 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.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: Centre National de Recherche Scientifique

2016-04-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57645

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/