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

ABSTRACT:

Motivation

Several state-of-the-art methods for isoform identification and quantification are based on [Formula: see text]-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 [Formula: see text]-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

SUBMITTER: Bernard E 

PROVIDER: S-EPMC4147886 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature