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Nucleic Acids Research 2005 33(3):922-933; doi:10.1093/nar/gki187
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Published online 16 February 2005

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

A new computational method for the detection of horizontal gene transfer events

Aristotelis Tsirigos1,2 and Isidore Rigoutsos2,3,*

1 New York University, Computer Science New York, NY 10021, USA 2 Bioinformatics and Pattern Discovery Group, IBM Thomas J. Watson Research Center PO Box 218, Yorktown Heights, NY 10598, USA 3 Department 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 October 7, 2004. Revised December 21, 2004. Accepted December 21, 2004.

In recent years, the increase in the amounts of available genomic data has made it easier to appreciate the extent by which organisms increase their genetic diversity through horizontally transferred genetic material. Such transfers have the potential to give rise to extremely dynamic genomes where a significant proportion of their coding DNA has been contributed by external sources. Because of the impact of these horizontal transfers on the ecological and pathogenic character of the recipient organisms, methods are continuously sought that are able to computationally determine which of the genes of a given genome are products of transfer events. In this paper, we introduce and discuss a novel computational method for identifying horizontal transfers that relies on a gene's nucleotide composition and obviates the need for knowledge of codon boundaries. In addition to being applicable to individual genes, the method can be easily extended to the case of clusters of horizontally transferred genes. With the help of an extensive and carefully designed set of experiments on 123 archaeal and bacterial genomes, we demonstrate that the new method exhibits significant improvement in sensitivity when compared to previously published approaches. In fact, it achieves an average relative improvement across genomes of between 11 and 41% compared to the Codon Adaptation Index method in distinguishing native from foreign genes. Our method's horizontal gene transfer predictions for 123 microbial genomes are available online at http://cbcsrv.watson.ibm.com/HGT/.


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