Nucleic Acids Research Advance Access originally published online on December 5, 2006
Nucleic Acids Research 2007 35(1):11-20; doi:10.1093/nar/gkl974
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Nucleic Acids Research, 2007, Vol. 35, No. 1 11-20
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Operon prediction in Pyrococcus furiosus
School of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA 30332, USA 1 Department of Biochemistry and Molecular Biology, Institute of Bioinformatics University of Georgia, Athens, GA 30602, USA
*To whom correspondence should be addressed. Tel: +1 706 542 9779; Fax: +1 706 542 9751; Email: xyn{at}bmb.uga.edu
Received July 18, 2006. Revised October 25, 2006. Accepted October 26, 2006.
Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN). These algorithms use intergenic distances, phylogenetic profiles, functional categories and gene-order conservation in their operon prediction. Our method takes as inputs the confidence scores of the three programs, and outputs a prediction of whether adjacent genes on the same strand belong to the same operon. In addition, we have applied Gene Ontology (GO) and KEGG pathway information to improve the accuracy of our algorithm. The parameters of this NN predictor are trained on a subset of all experimentally verified operon gene pairs of Bacillus subtilis. It subsequently achieved 86.5% prediction accuracy when applied to a subset of gene pairs for Escherichia coli, which is substantially better than any of the three prediction programs. Using this new algorithm, we predicted 470 operons in the P.furiosus genome. Of these, 349 were validated using DNA microarray data.
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