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Nucleic Acids Research 2005 33(12):3699-3707; doi:10.1093/nar/gki660
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Published online 8 July 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

A sensitive, support-vector-machine method for the detection of horizontal gene transfers in viral, archaeal and bacterial genomes

Aristotelis Tsirigos1,2 and Isidore Rigoutsos2,3,*

1New York University, Computer Science New York, NY 10021, USA 2Bioinformatics and Pattern Discovery Group, Computational Biology Center, IBM Thomas J. Watson Research Center PO Box 218, Yorktown Heights, NY 10598, USA 3Department of Chemical Engineering, Massachusetts Institute of Technology Room 56-469, Cambridge, MA 02139, USA

*To whom correspondence should be addressed. Tel: +1 914 945 1384; Fax: +1 914 945 4217; Email: rigoutso{at}us.ibm.com

Received March 30, 2005. Revised May 27, 2005. Accepted May 27, 2005.

In earlier work, we introduced and discussed a generalized computational framework for identifying horizontal transfers. This framework relied on a gene's nucleotide composition, obviated the need for knowledge of codon boundaries and database searches, and was shown to perform very well across a wide range of archaeal and bacterial genomes when compared with previously published approaches, such as Codon Adaptation Index and C + G content. Nonetheless, two considerations remained outstanding: we wanted to further increase the sensitivity of detecting horizontal transfers and also to be able to apply the method to increasingly smaller genomes. In the discussion that follows, we present such a method, Wn-SVM, and show that it exhibits a very significant improvement in sensitivity compared with earlier approaches. Wn-SVM uses a one-class support-vector machine and can learn using rather small training sets. This property makes Wn-SVM particularly suitable for studying small-size genomes, similar to those of viruses, as well as the typically larger archaeal and bacterial genomes. We show experimentally that the new method results in a superior performance across a wide range of organisms and that it improves even upon our own earlier method by an average of 10% across all examined genomes. As a small-genome case study, we analyze the genome of the human cytomegalovirus and demonstrate that Wn-SVM correctly identifies regions that are known to be conserved and prototypical of all beta-herpesvirinae, regions that are known to have been acquired horizontally from the human host and, finally, regions that had not up to now been suspected to be horizontally transferred. Atypical region predictions for many eukaryotic viruses, including the {alpha}-, ß- and {gamma}-herpesvirinae, and 123 archaeal and bacterial genomes, have been made available online at http://cbcsrv.watson.ibm.com/HGT_SVM/.


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