title: Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  Software implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer.
  
  
  BACKGROUND
  cdewey@biostat.wisc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.
authors.forename: Laura H
authors.initials: LH
authors.lastname: LeGault
authors.forename: Colin N
authors.initials: CN
authors.lastname: Dewey
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D000818:Animals; D002460:Cell Line; D004330:Drosophila / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software