A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data
Populations and Evolution
2010-03-04 v1 Statistics Theory
Statistics Theory
Abstract
RNA viruses provide prominent examples of measurably evolving populations. In 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.
Cite
@article{arxiv.q-bio/0603031,
title = {A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data},
author = {Niko Beerenwinkel and Mathias Drton},
journal= {arXiv preprint arXiv:q-bio/0603031},
year = {2010}
}
Comments
20 pages, 6 figures