Data-intensive analysis of HIV mutations

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. 1...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 16; no. 1; p. 35
Main Authors: Ozahata, Mina Cintho, Sabino, Ester Cerdeira, Diaz, Ricardo Sobhie, M Cesar, Roberto, Ferreira, João Eduardo
Format: Journal Article
Language:English
Published: London BioMed Central 05.02.2015
BioMed Central Ltd
Subjects:
Algorithms
Anti-HIV Agents - pharmacology
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Drug Resistance, Viral - genetics
Genotype
HIV Infections - drug therapy
HIV Infections - epidemiology
HIV Infections - virology
HIV Protease - genetics
HIV Reverse Transcriptase - genetics
HIV-1 - drug effects
HIV-1 - enzymology
HIV-1 - genetics
Humans
Life Sciences
Microarrays
Mutation - genetics
Research Article
HIV
Cluster
Mutation
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-015-0452-0