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Bioinformatics Advance Access originally published online on January 10, 2006
Bioinformatics 2006 22(5):606-613; doi:10.1093/bioinformatics/btk028
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Inferring global levels of alternative splicing isoforms using a generative model of microarray data

Ofer Shai 1,*, Quaid D. Morris 2, Benjamin J. Blencowe 2 and Brendan J. Frey 1

1Department of Electrical and Computer Engineering, University of Toronto Toronto, Canada, M5S 3G8
2Banting and Best Department of Medical Research, University of Toronto Toronto, Canada, M5G 1L6

*To whom correspondence should be addressed.

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{at}psi.toronto.edu

Supplementary information: http://www.psi.toronto.edu/GenASAP

Received on November 15, 2005; revised on December 21, 2005; accepted on December 23, 2005

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