title: Algorithm evolution for drug resistance prediction: comparison of systems for HIV-1 genotyping.
abstract: BACKGROUND
  Different genotypic HIV resistance algorithms are based on different rules. They may therefore result in different drug-resistance interpretations for the same patient sample. In particular, for early periods of new retroviral inhibitors or classes, sequence interpretation is expected to vary. One would, however, assume that those differences between systems wane with growing experience and that different algorithms yield similar results for well-established drugs.
  
  
  METHODS
  To assess the concordance of the Agence Nationale de Recherche sur le SIDA (ANRS), Rega and Stanford-HIVdb algorithms and their evolution over time, we analysed 284 routine samples with the current versions of each algorithm in 2004 and 2013. For 446 recent clinical sequences the differences for actual drugs were analysed. Scoring as 'susceptible' by one algorithm and 'resistant' by a second one defined a discordance.
  
  
  RESULTS
  The longitudinal analysis showed similar overall discordances for both time points as well as an evolution over time. The actual analysis demonstrated a higher overall discordance rate, mainly for certain drugs. Most deviations reflected differences between the ANRS and the other two algorithms.
  
  
  CONCLUSIONS
  This study demonstrates discordances between three most commonly used interpretation tools even for long-available drugs. It thereby reveals a need for further adjustment and improvement of current interpretation tools and may point at a possibly crucial role of subtype-specific information.
authors.affiliation: Molecular Virology, Department of Biomedicine - Petersplatz, University of Basel, Basel, Switzerland.
authors.forename: Sarah
authors.initials: S
authors.lastname: Wagner
authors.forename: Mario
authors.initials: M
authors.lastname: Kurz
authors.forename: Thomas
authors.initials: T
authors.lastname: Klimkait
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D023241:Antiretroviral Therapy, Highly Active / Q000379:methods / Q000592:standards; D002648:Child; D002675:Child, Preschool; D024882:Drug Resistance, Viral / Q000235:genetics*; D005260:Female; D005838:Genotype; D060005:Genotyping Techniques; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D017320:HIV Protease Inhibitors / Q000627:therapeutic use*; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics; D006801:Humans; D008297:Male; D008875:Middle Aged; D012367:RNA, Viral / Q000037:antagonists & inhibitors* / Q000235:genetics; D012189:Retrospective Studies; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use*; D013997:Time Factors; D019562:Viral Load / Q000187:drug effects
chemical_list: D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D012367:RNA, Viral; D018894:Reverse Transcriptase Inhibitors