Data-intensive analysis of HIV mutations

Abstract

BackgroundIn 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.ResultsA 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.ConclusionWe 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.

Features in HIV genotypes associated with failure in the computational prediction of patients' response to antiretroviral treatment

“…The rt184 was identified as ambiguous, which corroborates our previous results (Rosa et al, 2014), where we concluded that this attribute correlates with the occurrence of MLM errors, but here we found evidence that rt184 is also informative in the prediction of patient's response to treatment. Such codon is widely cited in the literature as related to virus resistance to antiretroviral treatment (Anta et al, 2013;Kulkarni et al, 2012;Ozahata et al, 2015;Xu et al, 2013). In this study, we identified seven other codons that present ambiguous behavior like rt184; they are pr12, pr63, rt207, rt210, rt245, rt251, and rt297.…”

“…This interpretation influences the choice of the treatment scheme that will be prescribed for the patient. Also, the use of the genetic sequence of the virus has been gaining prominence by improving the performance of computational models of prediction of response to therapy, becoming a parameter of extreme importance to guide the choice of new treatment schemes (Ozahata et al, 2015).…”