Inference of Alternative Splicing from RNA-Seq Data with Probabilistic Splice Graphs

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2013 Jul 13

PMID 23846746

Citations 13

Authors

Laura H LeGault

Colin N Dewey

Affiliations

Soon will be listed here.

Abstract

Motivation: Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.

Results: We present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate.

Availability: Software implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer.

Contact: cdewey@biostat.wisc.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

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Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.

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