Improved identification of conserved cassette exons using Bayesian networks

Background: Alternative splicing is a major contributor to the diversity of eukaryotic transcriptomes and proteomes. Currently, large scale detection of alternative splicing using expressed sequence tags (ESTs) or microarrays does not capture all alternative splicing events. Moreover, for many species genomic data is being produced at a far greater rate than corresponding transcript data, hence in silico methods of predicting alternative splicing have to be improved. Results: Here, we show that the use of Bayesian networks (BNs) allows accurate prediction of evolutionary conserved exon skipping events. At a stringent false positive rate of 0.5%, our BN achieves an improved true positive rate of 61%, compared to a previously reported 50% on the same dataset using support vector machines (SVMs). Incorporating several novel discriminative features such as intronic splicing regulatory elements leads to the improvement. Features related to mRNA secondary structure increase the prediction p...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Rileen Sinha, Michael Hiller, Rainer Pudimat, Ulrike Gausmann, Matthias Platzer, Rolf Backofen
• Keywords: Alternative Splicing, BMCBI 2008, Exons, Positive Rate