BIRS/Banff 15w5142 - Statistical and Computational Challenges In Bridging Functional Genomics, Epigenomics, Molecular QTLs, and Disease Genetics

Monday 9:15am-9:50am Laurent Jacob Efficient RNA isoform identification and quantification from RNA-Seq data with network flows Several state-of-the-art methods for isoform identification and quantification are based on l1regularized 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 l1penalty 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. [email protected]