Guojun Li1, 2, *, Dongsheng Che2, 3, *, & Ying Xu1, 3
1Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, US
2School of Mathematics and System Sciences, Shandong University, China 3Department of Computer Science, University of Georgia, Athens, GA 30602, US *These authors contributed equally to this work.
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AbstractIdentification of operons at the genome scale of prokaryotic organisms represents a key step in deciphering of their transcriptional regulation machinery, biological pathways and networks. While numerous computational methods have been shown to be effective in predicting operons for well-studied organisms such as Escherichia coli (E. coli) K12 and Bacillus subtilis (B. subtilis) 168, these methods generally do not generalize well to genomes other than the ones used to train the methods because they rely heavily on organism-specific information. Several methods have been explored to address this problem through utilizing only genomic structural information conserved across multiple organisms, but they all suffer from the issue of low prediction sensitivity. In this paper, we report a novel operon prediction method that is applicable to any prokaryotic genome with accurate prediction accuracy. The key idea of the method is to predict operons through identification of conserved gene clusters across multiple genomes and through deriving a key parameter relevant to the distribution of intergenic distances in genomes. We have implemented this method using a graph-theoretic approach, called a maximum cardinality bipartite matching algorithm. Computational results have shown that our method has higher prediction sensitivity as well as specificity than any published method. We have carried out a preliminary study on operons unique to archaea and bacteria, respectively, and derived a number of interesting new insights about operons between these two kingdoms. The software and predicted operons of 365 prokaryotic genomes are available at http://www.esu.edu/~dche/UNIPOP.
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ReferenceLi, G., Che D. and Xu, Y. A Universal and Accurate Operon Predictor for All Prokaryotic Genomes. Journal of Bioinformatics and Computational Biology. 7(1):19-38. ContactEmail: dche@po-box.esu.edu Dongsheng Che Last updated 11/06/2009 |