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.
Researchers from the University of Wisconsin, Madison present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. They prove that their models are often identifiable and demonstrate that their 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
- Legault LH, Dewey CN. (2013) Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs. Bioinformatics 29(18), 2300-10. [article]