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Pattern Analysis and Applications

, Volume 19, Issue 3, pp 793–805 | Cite as

Hidden Markov models for gene sequence classification

Classifying the VSG gene in the Trypanosoma brucei genome
  • Andrea Mesa
  • Sebastián BasterrechEmail author
  • Gustavo Guerberoff
  • Fernando Alvarez-Valin
Short Paper

Abstract

The article presents an application of hidden Markov models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the variant surface glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host’s immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.

Keywords

Hidden Markov model Classification Gene sequence classification Trypanosoma brucei Variant surface glycoprotein 

Notes

Acknowledgments

This article has been elaborated in the framework of the project New creative teams in priorities of scientific research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic and supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme, and by the Project SP2015/105 DPDM-Database of Performance and Dependability Models of the Student Grand System, VSB-Technical University of Ostrava.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Andrea Mesa
    • 1
  • Sebastián Basterrech
    • 2
    Email author
  • Gustavo Guerberoff
    • 3
  • Fernando Alvarez-Valin
    • 4
  1. 1.Departamento de Métodos Matemáticos Cuantitativos, Facultad de Ciencias Económicas y AdministraciónUniversidad de la RepúblicaMontevideoUruguay
  2. 2.National Supercomputing CenterVŠB-Technical University of OstravaOstrava-PorubaCzech Republic
  3. 3.Facultad de Ingeniería, Instituto de Matemática y EstadísticaUniversidad de la RepúblicaMontevideoUruguay
  4. 4.Sección Biomatemática-Facultad de CienciasUniversidad de la RepúblicaMontevideoUruguay

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