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The article presents an application of Hidden Markov Models (HMMs) forpattern recognition on genome sequences. We apply HMM for identifying genesencoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosomabrucei (T. brucei) and other African trypanosomes. These are parasitic protozoacausative agents of sleeping sickness and several diseases in domestic and wildanimals. These parasites have a peculiar strategy to evade the host's immunesystem that consists in periodically changing their predominant cellularsurface protein (VSG). The motivation for using patterns recognition methods toidentify these genes, instead of traditional homology based ones, is that thelevels of sequence identity (amino acid and DNA sequence) amongst these genesis often below of what is considered reliable in these methods. Among patternrecognition approaches, HMM are particularly suitable to tackle this problembecause they can handle more naturally the determination of gene edges. Weevaluate the performance of the model using different number of states in theMarkov model, as well as several performance metrics. The model is appliedusing public genomic data. Our empirical results show that the VSG genes on T.brucei can be safely identified (high sensitivity and low rate of falsepositives) using HMM.

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome