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PLoS Negl Trop Dis. 2019 May 8;13(5):e0007231. doi: 10.1371/journal.pntd.0007231. eCollection 2019 May.

A computational method for the identification of Dengue, Zika and Chikungunya virus species and genotypes.

Author information

1
Laboratório de Flavivírus, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
2
KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), College of Health Sciences, University of KwaZuluNatal, Durban, South Africa.
3
Laboratório de Genética Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
4
Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium.
5
KU Leuven-University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.
6
Department of Zoology, University of Oxford, Oxford, United Kingdom.
7
Evandro Chagas Institute, Ministry of Health, Ananindeua, Brazil.
8
Laboratório de Patologia Experimental, Fundação Oswaldo Cruz, Salvador, Brazil.
9
Center for Global Health and Tropical Medicine, Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal.
10
EMWEB (private company), Herent, Belgium.
11
Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, United states of America.
12
Coordenação de Vigilância em Saúde e Laboratórios de Referências, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

Abstract

In recent years, an increasing number of outbreaks of Dengue, Chikungunya and Zika viruses have been reported in Asia and the Americas. Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks, both aspects that are vital to develop effective prevention and treatment strategies. Hence, we developed an efficient method to classify virus sequences with respect to their species and sub-species (i.e. serotype and/or genotype). This tool provides an easy-to-use software implementation of this new method and was validated on a large dataset assessing the classification performance with respect to whole-genome sequences and partial-genome sequences. Available online: http://krisp.org.za/tools.php.

PMID:
31067235
PMCID:
PMC6527240
DOI:
10.1371/journal.pntd.0007231
[Indexed for MEDLINE]
Free PMC Article

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