A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data.
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.
Department of Mathematics, University of California, 1073 Evans Hall, Berkeley, CA 94720 USA. niko@math.berkeley.edu
Niko
N
Beerenwinkel
Mathias
M
Drton
D000480:Alkynes; D048588:Benzoxazines; D002999:Clone Cells; D003521:Cyclopropanes; D004279:DNA, Viral / Q000737:chemistry / Q000235:genetics; D024882:Drug Resistance, Viral / Q000235:genetics; D006678:HIV / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology*; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D008957:Models, Genetic; D010078:Oxazines / Q000627:therapeutic use; D017354:Point Mutation; D016133:Polymerase Chain Reaction; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use; D017422:Sequence Analysis, DNA
D000480:Alkynes; D048588:Benzoxazines; D003521:Cyclopropanes; D004279:DNA, Viral; D010078:Oxazines; D018894:Reverse Transcriptase Inhibitors; C098320:efavirenz