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

Motivation: Several state-of-the-art methods for isoform identification and quantification are based on l(1)-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 l(1)-penalty are either restricted to genes with few exons or only run the regression algorithm on a small set of preselected isoforms. Results: 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.