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Nucleic Acids Research 2005 33(10):3253-3262; doi:10.1093/nar/gki634
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Published online 7 June 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 universally applicable method of operon map prediction on minimally annotated genomes using conserved genomic context

Martin T. Edwards*, Stuart C. G. Rison1, Neil G. Stoker1 and Lorenz Wernisch

School of Crystallography, Birkbeck College London WC1E 7HX, UK 1Department of Pathology and Infectious Diseases, Royal Veterinary College London NW1 0TU, UK

*To whom correspondence should be addressed. Tel: +44 20 7631 6831; Fax: +44 20 7631 6803; Email: m.edwards{at}mail.cryst.bbk.ac.uk

Received August 10, 2004. Revised September 27, 2004. Accepted May 16, 2005.

An important step in understanding the regulation of a prokaryotic genome is the generation of its transcription unit map. The current strongest operon predictor depends on the distributions of intergenic distances (IGD) separating adjacent genes within and between operons. Unfortunately, experimental data on these distance distributions are limited to Escherichia coli and Bacillus subtilis. We suggest a new graph algorithmic approach based on comparative genomics to identify clusters of conserved genes independent of IGD and conservation of gene order. As a consequence, distance distributions of operon pairs for any arbitrary prokaryotic genome can be inferred. For E.coli, the algorithm predicts 854 conserved adjacent pairs with a precision of 85%. The IGD distribution for these pairs is virtually identical to the E.coli operon pair distribution. Statistical analysis of the predicted pair IGD distribution allows estimation of a genome-specific operon IGD cut-off, obviating the requirement for a training set in IGD-based operon prediction. We apply the method to a representative set of eight genomes, and show that these genome-specific IGD distributions differ considerably from each other and from the distribution in E.coli.


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