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Related Experiment Videos

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

Ofer Shai1, Quaid D Morris, Benjamin J Blencowe

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8. ofer@psi.toronto.edu

Bioinformatics (Oxford, England)
|January 13, 2006
PubMed
Summary

A new algorithm, Generative model for Alternative Splicing Array Platform (GenASAP), accurately predicts alternative splicing events from microarray data. This tool aids in understanding gene expression complexity and regulation in mammals.

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Area of Science:

  • Molecular Biology
  • Bioinformatics

Background:

  • Alternative splicing (AS) generates diverse mRNA isoforms, increasing proteomic complexity and regulating gene expression.
  • Mechanisms of AS and its global regulation remain poorly understood.

Purpose of the Study:

  • To present the Generative model for Alternative Splicing Array Platform (GenASAP) algorithm for predicting AS levels.
  • To analyze global AS patterns in mammalian cells and tissues.

Main Methods:

  • GenASAP utilizes Bayesian learning in an unsupervised model to predict AS levels from custom microarray data.
  • The algorithm models hybridization profiles, noise, and missing data.

Main Results:

  • GenASAP was applied to a microarray monitoring 3126 AS events in mouse cells and tissues.

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  • Accurate predictions were achieved for over one-third of AS events, validated by RT-PCR.

    • Conclusions:

    • GenASAP is a robust tool for the global discovery and analysis of alternative splicing.
    • The algorithm enhances understanding of gene expression regulation through AS.