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Nucleic Acids Research 2006 34(8):2418-2427; doi:10.1093/nar/gkl294
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Published online 8 May 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
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Article

Detecting uber-operons in prokaryotic genomes

Dongsheng Che2, Guojun Li1,3, Fenglou Mao1, Hongwei Wu1 and Ying Xu1,2,*

1 Department of Biochemistry and Molecular Biology, University of Georgia USA 2 Department of Computer Science, University of Georgia USA 3 School of Mathematics and System Sciences, Shandong University China

*To whom correspondence should be addressed. Tel: 1 706 542 9779; Fax: 1 706 542 9751; Email: Ying Xuxyn{at}bmb.uga.edu

Received January 6, 2006. Revised February 15, 2006. Accepted April 6, 2006.

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga.edu/uber, the first of its kind.

The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors


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