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Bioinformatics Advance Access published online on January 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btk029
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 21, 2005
Revised December 8, 2005
Accepted December 27, 2005

Article

Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures

Daniel Dalevi 1 *, Devdatt Dubhashi 1, and Malte Hermansson 2

1 Department of Computing Science, Chalmers University, SE 412 96 Göteborg, Sweden
2 Department of Cell and Molecular Biology, Microbiology, Göteborg University, 405 30 Göteborg, Sweden

* To whom correspondence should be addressed.
Daniel Dalevi, E-mail: dalevi{at}cs.chalmers.se


  Abstract

Motivation: Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier (Sandberg et al., 2001) was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in N. meningitis. In this study we extend the classifier approach by using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk).

Results: We propose a simple algorithm to lock a variable length Markov model to a certain number of parameters and show that the use of Markov models greatly increases the flexibility and accuracy in prediction to that of a naïve model. We also test the integrity of classifiers in terms of false-negatives and give estimates of the minimal sizes of training data. We end the report by proposing a method to reject a false hypothesis of horizontal gene transfer (HGT).

Availability: Software and additional information available at www.cs.chalmers.se/~dalevi/genetic_sign_classifiers/.

Associate Editor: Christos Ouzounis
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