Comparative performance of the REGA subtyping tool version 2 versus version 1

Ana B Abecasis, Yunpeng Wang, Pieter Libin, Stijn Imbrechts, Tulio de Oliveira, Ricardo Jorge Camacho, Anne-Mieke Vandamme

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The REGA HIV-1 subtyping tool is a phylogenetic-based method for subtyping HIV-1 genomic sequences that was published in 2005. The subtyping tool combines phylogenetic approaches with recombination detection methods. Recently, version 2 was released (http://www.bioafrica.net/rega-genotype/html/index.html) as an improvement of version 1. Version 2 implements a Decision-Tree-based algorithm that was not implemented in version 1. We wanted to compare the two versions on a large sequence dataset to assess the improvements of version 2 and to verify whether features lost during updating the tool needed to be recovered. We analysed the results of the two versions in the genotyping of 4676 HIV-1 pol sequences. We compared those results to a manual approach, used in previous studies. Our results show that version 2 has an overall better sensitivity but especially for the detection of subtypes A, B, D, F, G and CRF14_BG and CRF06_CPX. For the other subtypes, no significant differences were observed in the sensitivity of versions 1 and 2. The overall increase in sensitivity was however accompanied by a decrease in the specificity for the detection of subtype B. This is the main limitation of version 2. However, while the number of false negatives decreased by 53 samples, the number of false positives increased only by 5 samples from version 1 to 2. The performance of the REGA HIV-1 subtyping tool was considerably improved from one version to the other. Our results are very valuable and allow us to make suggestions for further improvement of the tool for a version 3 release.

Original languageEnglish
Pages (from-to)380-385
Number of pages5
JournalInfection Genetics And Evolution
Volume10
Issue number3
DOIs
Publication statusPublished - Apr 2010

Keywords

  • Algorithms
  • Automatic Data Processing
  • False Negative Reactions
  • False Positive Reactions
  • Genetic Variation
  • Genome, Viral
  • HIV Infections
  • HIV-1
  • Humans
  • Pattern Recognition, Automated
  • Phylogeny
  • Recombination, Genetic
  • Sensitivity and Specificity
  • Sequence Analysis
  • pol Gene Products, Human Immunodeficiency Virus
  • Comparative Study
  • Journal Article
  • Research Support, Non-U.S. Gov't

UN Sustainable Development Goals (SDGs)

  • SDG 3 - Good Health and Well-Being

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