Journal Article

Statistical inferences for isoform expression in RNA-Seq

Hui Jiang and Wing Hung Wong

in Bioinformatics

Volume 25, issue 8, pages 1026-1032
Published in print April 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp113
Statistical inferences for isoform expression in RNA-Seq

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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@stanford.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4869 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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