A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data

Niko Beerenwinkel, Mathias Drton

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

RNA viruses provide prominent examples of measurably evolving populations. In human immunodeficiency virus (HIV) infection, the development of drug resistance is of particular interest because precise predictions of the outcome of this evolutionary process are a prerequisite for the rational design of antiretroviral treatment protocols. We present a mutagenetic tree hidden Markov model for the analysis of longitudinal clonal sequence data. Using HIV mutation data from clinical trials, we estimate the order and rate of occurrence of seven amino acid changes that are associated with resistance to the reverse transcriptase inhibitor efavirenz.

Original languageEnglish
Pages (from-to)53-71
Number of pages19
JournalBiostatistics
Volume8
Issue number1
DOIs
StatePublished - Jan 2007
Externally publishedYes

Keywords

  • EM algorithm
  • Graphical model
  • HIV drug resistance
  • Hidden Markov model
  • Longitudinal data
  • Measurably evolving populations
  • Mutagenetic tree

Fingerprint

Dive into the research topics of 'A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data'. Together they form a unique fingerprint.

Cite this