Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures.
BACKGROUND
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 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 Neisseria meningitis. In this study we extend the classifier approach 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.


BACKGROUND
Software and Supplementary information available at www.cs.chalmers.se/~dalevi/genetic_sign_classifiers/.
Dalevi
Daniel
D
Department of Computing Science, Chalmers University, SE 412 96 Göteborg, Sweden. dalevi@cs.chalmers.se
Dubhashi
Devdatt
D
Hermansson
Malte
M
D001185:Artificial Intelligence; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods*; D016172:DNA Fingerprinting / Q000379:methods*; D004269:DNA, Bacterial / Q000235:genetics*; D022761:Gene Transfer, Horizontal / Q000235:genetics*; D016680:Genome, Bacterial / Q000235:genetics*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D013045:Species Specificity
D004269:DNA, Bacterial