Finding association rules of cis-regulatory elements involved in alternative splicing
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Efficient Algorithms for the Computational Design of Optimal Tiling Arrays
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining of cis-Regulatory Motifs Associated with Tissue-Specific Alternative Splicing
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Efficient computational design of tiling arrays using a shortest path approach
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
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Motivation: Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues. Results: GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT--PCR assays. Contact: ofer@psi.toronto.edu Supplementary information: http://www.psi.toronto.edu/GenASAP