A Novel Combinatorial Method for Estimating Transcript Expression with RNA-Seq: Bounding the Number of Paths
Abstract
RNA-Seq technology offers new high-throughput ways for transcript identification and quantification based on short reads, and has recently attracted great interest. The problem is usually modeled by a weighted splicing graph whose nodes stand for exons and whose edges stand for split alignments to the exons. The task consists of finding a number of paths, together with their expression levels, which optimally explain the coverages of the graph under various fitness functions, such least sum of squares. In (Tomescu et al. RECOMB-seq 2013) we showed that under general fitness functions, if we allow a polynomially bounded number of paths in an optimal solution, this problem can be solved in polynomial time by a reduction to a min-cost flow program. In this paper we further refine this problem by asking for a bounded number k of paths that optimally explain the splicing graph. This problem becomes NP-hard in the strong sense, but we give a fast combinatorial algorithm based on dynamic programming for it. In order to obtain a practical tool, we implement three optimizations and heuristics, which achieve better performance on real data, and similar or better performance on simulated data, than state-of-the-art tools Cufflinks, IsoLasso and SLIDE. Our tool, called Traph, is available at http://www.cs.helsinki.fi/gsa/traph/
Cite
@article{arxiv.1307.7811,
title = {A Novel Combinatorial Method for Estimating Transcript Expression with RNA-Seq: Bounding the Number of Paths},
author = {Alexandru I. Tomescu and Anna Kuosmanen and Romeo Rizzi and Veli Mäkinen},
journal= {arXiv preprint arXiv:1307.7811},
year = {2013}
}
Comments
Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013)