Dataset Information
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
Ontology highlight
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.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: LeGault LH
PROVIDER: S-EPMC3753571 | biostudies-literature | 2013 Sep
REPOSITORIES: biostudies-literature
Publications
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
Bioinformatics (Oxford, England) 20130711 18
<h4>Motivation</h4>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 ...[more]