A computational method for the identification of dengue, zika and chikungunya virus species and genotypes

• Autor(es): Fonseca, Vagner S. ; Libin, Pieter J. K. ; Theys, Kristof ; Faria, Nuno Rodrigues ; Nunes, Márcio Roberto Texeira ; Restovic, Maria I. ; Freire, Murilo ; Giovanetti, Marta ; Cuypers, Lize ; Nowé, Ann ; Abecasis, AB ; Deforche, Koen ; Santiago, Gilberto A. ; de Siqueira, Isadora Cristina ; San, Emmanuel J. ; Machado, Kaliane C.B. ; Azevedo, Vasco Ariston De Carvalho ; de Filippis, Ana Maria Bispo ; da Cunha, Rivaldo Venâncio ; Pybus, Oliver George ; Vandamme, AM ; Alcantara, L. C. J. ; de Oliveira, Túlio
• Data: 2019
• Identificador Persistente: http://hdl.handle.net/10362/116728
• Origem: Repositório Institucional da UNL
• Assunto(s): Computer Science Applications; Virology; Infectious Diseases; Genetics; SDG 3 - Good Health and Well-being
• Tipo de Documento: Artigo científico
• Idioma: Inglês
• Contribuidor(es): TB, HIV and opportunistic diseases and pathogens (THOP); Global Health and Tropical Medicine (GHTM); Instituto de Higiene e Medicina Tropical (IHMT); RUN

Descrição

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.