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Nucleic Acids Research 2005 33(3):880-892; doi:10.1093/nar/gki232
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Published online 8 February 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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

Article

A novel method for accurate operon predictions in all sequenced prokaryotes

Morgan N. Price1, Katherine H. Huang1, Eric J. Alm1,* and Adam P. Arkin1,2,3

1 Lawrence Berkeley National Lab 1 Cyclotron Road, Mailstop 939R704, Berkeley, CA 94720, USA 2 Howard Hughes Medical Institute Berkeley, CA, USA 3 Department of Bioengineering, University of California Berkeley, USA

*To whom correspondence should be addressed. Tel: +1 510 843 1794; Fax: +1 510 486 6059; Email: ejalm{at}lbl.gov

Received September 14, 2004. Revised October 25, 2004. Accepted January 20, 2005.

We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacter pylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, and its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from six phylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC 6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.


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