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Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach
07:06

Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Algorithm-based prediction of HIV-1 subtype D coreceptor use.

Julia Dina1, Stephanie Raymond, Anne Maillard

  • 1Laboratoire de Virologie, CHU de Caen, Caen, France. Dina, dina-j@chu-caen.fr

Journal of Clinical Microbiology
|June 28, 2013
PubMed
Summary
This summary is machine-generated.

A new HIV-1 subtype D genotypic tropism prediction algorithm was compared to geno2pheno. Both algorithms showed similar concordance with phenotypic tropism determination at a 2.5% false-positivity rate.

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Area of Science:

  • * Virology
  • * Molecular Biology
  • * Bioinformatics

Background:

  • * HIV-1 tropism determination is crucial for predicting disease progression and guiding therapy.
  • * Genotypic tropism assays are increasingly used, but their accuracy varies by HIV-1 subtype.
  • * Evaluating and comparing tropism prediction algorithms is essential for clinical application.

Purpose of the Study:

  • * To compare the coreceptor tropism-predicting performance of a specific genotypic algorithm for HIV-1 subtype D with the widely used geno2pheno algorithm.
  • * To assess the concordance of these algorithms with phenotypic tropism determination.

Main Methods:

  • * Retrospective analysis of HIV-1 subtype D patient samples.
  • * Performance evaluation of a D-specific genotypic tropism algorithm.
  • * Comparison with the geno2pheno algorithm using various false-positivity rate cutoffs.
  • * Concordance assessment against established phenotypic tropism assays.

Main Results:

  • * The D-specific algorithm and geno2pheno at a 2.5% false-positivity rate cutoff demonstrated equivalent concordance with phenotypic tropism determination.
  • * Geno2pheno at the 2.5% cutoff exhibited higher sensitivity but slightly lower specificity compared to the D-specific algorithm.
  • * This suggests geno2pheno is a viable alternative for tropism prediction in HIV-1 subtype D.

Conclusions:

  • * The geno2pheno algorithm, with a 2.5% false-positivity rate cutoff, is a suitable alternative for predicting coreceptor tropism in HIV-1 subtype D.
  • * This finding supports the broader applicability of geno2pheno across different HIV-1 subtypes.
  • * Further validation in diverse clinical settings is warranted.