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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Pattern Analysis and Applications

ISSN

1433-7541

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

793-805

Kód UT WoS článku

000379266300015

EID výsledku v databázi Scopus

2-s2.0-84938718933