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Bioinformatics Advance Access published online on January 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn004
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Evigan: A Hidden Variable Model for Integrating Gene Evidence for Eukaryotic Gene Prediction

Qian Liu 1,*, Aaron J. Mackey 2,3, David S. Roos 2,3 and Fernando C.N. Pereira 1

1Department of Computer and Information Science, University of Pennsylvania, Philadelphia PA 19104, USA
2Department of Biology, University of Pennsylvania, Philadelphia PA 19104, USA
3Penn Genomics Institute, University of Pennsylvania, Philadelphia PA 19104, USA

*To whom correspondence should be addressed. Qian Liu, E-mail: qianliu{at}seas.upenn.edu


  Abstract

Motivation: The increasing diversity and variable quality of evidence relevant to gene annotation argues for a probabilistic framework that automatically integrates such evidence to yield candidate gene models.

Results: Evigan is an automated gene annotation program for eukaryotic genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model is a dynamic Bayes network whose parameters are adjusted to maximize the probability of observed evidence. Consensus gene predictions are then derived bymaximum likelihood decoding, yielding n-best models (with probabilities for each). Evigan is capable of accommodating a variety of different types of evidence, including (but not limited to) gene models computed by diverse gene nders, BLAST hits, EST matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Because separate training data is not required (apart from the training sets used by individual gene nders), Evigan is particularly attractive for newly sequenced genomes where little or no reliable manually-curated annotation is available. The ability to produce a ranked list of alternative gene models may facilitate identi cation of alternatively spliced transcripts. Experimental application to ENCODE regions of the human genome, and the genomes of Plasmodium vivax and Arabidopsis thaliana show that Evigan achieves better performance than any of the individual data sources used as evidence.

Availability: The source code is available at http://www.seas.upenn.edu/~strctlrn/evigan/evigan.html.

Contact: qianliu{at}seas.upenn.edu

Associate Editor: Dr. Alex Bateman

Received on October 15, 2007; revised on December 13, 2007; accepted on January 3, 2008

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