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Bioinformatics Advance Access published online on August 2, 2005
Bioinformatics, 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@oupjournals.org
Received June 20, 2005
Revised July 28, 2005
Accepted July 29, 2005

Article

JIGSAW: integration of multiple sources of evidence for gene prediction

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

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

* To whom correspondence should be addressed.
Jonathan E. Allen, E-mail: jeallen{at}umiacs.umd.edu


  Abstract

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 to existing methods.

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


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