A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Bernard, Elsa; Jacob, Laurent; Mairal, Julien; Viara, Eric; Vert, Jean-Philippe
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Isoform -- RNA-seq -- Alternative splicing -- Muti-task estimation -- Convex optimization; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0695-9
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. Results We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Conclusion Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available athttp://cbio.ensmp.fr/flipflop .