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Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.

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.

Pubmed ID: 23846746

Authors

  • LeGault LH
  • Dewey CN

Journal

Bioinformatics (Oxford, England)

Publication Data

September 15, 2013

Associated Grants

  • Agency: NHGRI NIH HHS, Id: HG005232
  • Agency: NHGRI NIH HHS, Id: R01 HG005232

Mesh Terms

  • Algorithms
  • Alternative Splicing
  • Animals
  • Cell Line
  • Drosophila
  • Gene Expression Profiling
  • Humans
  • Models, Statistical
  • Sequence Analysis, RNA
  • Software