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: firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
We have not found any resources mentioned in this publication.
SciCrunch® is a data sharing and display platform. Anyone can create a custom portal where they can select searchable subsets of hundreds of data sources, brand their web pages and create their community. SciCrunch® will push data updates automatically to all portals on a weekly basis. User communities can also add their own data to SciCrunch®, however this is not currently a free service.