Data-intensive analysis of HIV mutations
Ozahata, Mina Cintho
Sabino, Ester Cerdeira
Diaz, Ricardo Sobhie [UNIFESP]
Cesar, Roberto M.
Ferreira, Joao Eduardo
Is part ofBmc Bioinformatics
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Background: in this study, clustering was performed using a bitmap representation of HIV reverse transcriptase and protease sequences, to produce an unsupervised classification of HIV sequences. the classification will aid our understanding of the interactions between mutations and drug resistance. 10,229 HIV genomic sequences from the protease and reverse transcriptase regions of the pol gene and antiretroviral resistant related mutations represented in an 82-dimensional binary vector space were analyzed.Results: A new cluster representation was proposed using an image inspired by microarray data, such that the rows in the image represented the protein sequences from the genotype data and the columns represented presence or absence of mutations in each protein position. the visualization of the clusters showed that some mutations frequently occur together and are probably related to an epistatic phenomenon.Conclusion: We described a methodology based on the application of a pattern recognition algorithm using binary data to suggest clusters of mutations that can easily be discriminated by cluster viewing schemes.
CitationBmc Bioinformatics. London: Biomed Central Ltd, v. 16, 23 p., 2015.
SponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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