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Bioinformatics Advance Access originally published online on August 2, 2005
Bioinformatics 2005 21(18):3596-3603; doi:10.1093/bioinformatics/bti609
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org

JIGSAW: integration of multiple sources of evidence for gene prediction

Jonathan E. Allen 1,3,* and Steven L. Salzberg 1,2

1Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies, University of Maryland College Park, MD 20742, USA
2Department of Computer Science, University of Maryland Institute for Advanced Computer Studies, University of Maryland College Park, MD 20742, USA
3Department of Computer Science, Johns Hopkins University 3400 N. Charles Street, Baltimore, MD 21218, USA

*To whom correspondence should be addressed.

Motivation: Computational gene finding systems play an important role in finding new human genes, although no systems are yet accurate enough to predict all or even most protein-coding regions perfectly. Ab initio programs can be augmented by evidence such as expression data or protein sequence homology, which improves their performance. The amount of such evidence continues to grow, but computational methods continue to have difficulty predicting genes when the evidence is conflicting or incomplete. Genome annotation pipelines collect a variety of types of evidence about gene structure and synthesize the results, which can then be refined further through manual, expert curation of gene models.

Results: JIGSAW is a new gene finding system designed to automate the process of predicting gene structure from multiple sources of evidence, with results that often match the performance of human curators. JIGSAW computes the relative weight of different lines of evidence using statistics generated from a training set, and then combines the evidence using dynamic programming. Our results show that JIGSAW's performance is superior to ab initio gene finding methods and to other pipelines such as Ensembl. Even without evidence from alignment to known genes, JIGSAW can substantially improve gene prediction accuracy as compared with existing methods.

Availability: JIGSAW is available as an open source software package at http://cbcb.umd.edu/software/jigsaw

Contact: jeallen{at}umiacs.umd.edu

Received on June 20, 2005; revised on July 28, 2005; accepted on July 29, 2005

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