Hidden Markov models for gene sequence classification: Classifying the VSG gene in the Trypanosoma brucei genome

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86096557" target="_blank" >RIV/61989100:27240/16:86096557 - isvavai.cz</a>

Result on the web

<a href="http://link.springer.com/article/10.1007%2Fs10044-015-0508-9" target="_blank" >http://link.springer.com/article/10.1007%2Fs10044-015-0508-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10044-015-0508-9" target="_blank" >10.1007/s10044-015-0508-9</a>

Result language

angličtina

Original language name

Hidden Markov models for gene sequence classification: Classifying the VSG gene in the Trypanosoma brucei genome

Original language description

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. (C) 2015 Springer-Verlag London

Czech name

—

Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Pattern Analysis and Applications

ISSN

1433-7541

e-ISSN

—

Volume of the periodical

19

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

793-805

UT code for WoS article

000379266300015

EID of the result in the Scopus database

2-s2.0-84938718933