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GenASAP - Generative Model for the Alternative Splicing Array Platform

Ofer Shai, Quaid D. Morris, Benjamin J. Blencowe, and Brendan J. Frey (2005)
Inferring Global Levels of Alternative Splicing Isoforms Using a Generative Model of Microarray Data
Bioinformatics, DOI: 10.1093

Abstract

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. Alternative splicing functions to enrich an organismís 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 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 3,126 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.

GenASAP is freely available as a command line executable for Windows, Linux, and Macintosh for academic research
To obtain GenASAP, email: frey @ psi .toronto .edu

Supplementary Information

33 tissue data (NEW!)

Data on an Agilent 44k microarray probing 3,707 cassette AS events across 33 mouse tissues and some related RT-PCR validation data are also available for download as a Matlab .mat file. Pre-processed microarray measurements are stored in DDfinal as a 3-D array indexed by tissue type, AS event ID and probe index while the RT-PCR validations on %AS exclusion are stored in PCR.

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