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Bioinformatics Advance Access originally published online on July 12, 2006
Bioinformatics 2006 22(18):2196-2203; doi:10.1093/bioinformatics/btl369
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© 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.

Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands

Georgios S. Vernikos * and Julian Parkhill

The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA, UK

*To whom correspondence should be addressed.

Motivation: There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.

Results: We introduce a novel computational method, Interpolated Variable Order Motifs (IVOMs), which exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared with fixed-order methods. For optimal localization of the boundaries of each predicted region, a second order, two-state hidden Markov model (HMM) is implemented in a change-point detection framework. We applied the IVOM approach to the genome of Salmonella enterica serovar Typhi CT18, a well-studied prokaryote in terms of HGT events, and we show that the IVOMs outperform state-of-the-art low and high order motif methods predicting not only the already characterized Salmonella Pathogenicity Islands (SPI-1 to SPI-10) but also three novel SPIs (SPI-15, SPI-16, SPI-17) and other HGT events.

Availability: The software is available under a GPL license as a standalone application at http://www.sanger.ac.uk/Software/analysis/alien_hunter

Contact: gsv{at}sanger.ac.uk

Supplementary Information: Supplementary data are available at Bioinformatics online.

Received on May 3, 2006; revised on June 22, 2006; accepted on July 3, 2006

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