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Bioinformatics Advance Access originally published online on February 25, 2009
Bioinformatics 2009 25(8):1026-1032; doi:10.1093/bioinformatics/btp113
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Statistical inferences for isoform expression in RNA-Seq

Hui Jiang 1 and Wing Hung Wong 2,*

1Institute for Computational and Mathematical Engineering and 2Department of Statistics, Stanford University, Stanford, CA 94305, USA

*To whom correspondence should be addressed.


  Abstract

Summary: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods.

Contact: whwong{at}stanford.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on October 12, 2008; revised on February 22, 2009; accepted on February 22, 2009

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D. Hiller, H. Jiang, W. Xu, and W. H. Wong
Identifiability of isoform deconvolution from junction arrays and RNA-Seq
Bioinformatics, December 1, 2009; 25(23): 3056 - 3059.
[Abstract] [Full Text] [PDF]


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