Nucleic Acids Research Advance Access originally published online on December 14, 2006
Nucleic Acids Research 2007 35(1):288-298; doi:10.1093/nar/gkl1018
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Nucleic Acids Research, 2007, Vol. 35, No. 1 288-298
© 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 using both genome-specific and general genomic information
1 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, University of Georgia Athens, GA, USA 2 Institute of Bioinformatics, University of Georgia Athens, GA, USA 3 Center for Bioinformatics Research, Department of Computer Science, University of North Carolina at Charlotte Charlotte, NC, USA
*To whom correspondence should be addressed. Tel: +1 706 542 9779; Fax: +1 706 542 9751; Email: xyn{at}bmb.uga.edu
Received September 7, 2006. Revised October 27, 2006. Accepted October 30, 2006.
We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.
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