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Bioinformatics Advance Access published online on November 11, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti123
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 17, 2004
Revised July 12, 2004
Accepted October 29, 2004

Article

Operon prediction without a training set

B. P. Westover 1, J. D. Buhler 1*, J. L. Sonnenburg 2, and J. I. Gordon 2

1 Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
2 Department of Molecular Biology and Pharmacology and Center for Genome Sciences, Washington University School of Medicine, St. Louis, MO 63108, USA

* To whom correspondence should be addressed.
J. D. Buhler, E-mail: jbuhler{at}cse.wustl.edu


  Abstract

Motivation: Annotation of operons in a bacterial genome is an important step in determining an organism's transcriptional regulatory program. While extensive studies of operon structure have been carried out in a few species, such as E. coli, fewer resources exist to inform operon prediction in newly sequenced genomes. In particular, many extant operon finders require a large body of training examples to learn the properties of operons in the target organism. For newly sequenced genomes, such examples are generally not available; moreover, a model of operons trained on one species may not reflect the properties of other, distantly related organisms. We encountered these issues in the course of predicting operons in the genome of Bacteroides thetaiotaomicron, a common anaerobe that is a prominent component of the normal adult human intestinal microbial community.

Results: We describe an operon predictor designed to work without extensive training data.We rely on a small set of a priori assumptions about the properties of the genome being annotated that permit estimation of the probability that two adjacent genes lie in a common operon. Predictions integrate several sources of information, including intergenic distance, common functional annotation, and a novel formulation of conserved gene order. We validate our predictor both on the known operons of E. coli and on the genome of B. theta, using expression data to evaluate our predictions in the latter.

Availability: software is available online at http://www.cse.wustl.edu/~jbuhler/research/operons.


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