A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data
Niko Beerenwinkel, Mathias Drton
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.
Create a lesson
Related papers
Reservoir: A Large-Scale Simulated Dataset for Training and Evaluating Epidemiological Models
Carson Dudley, Reiden Magdaleno, Marisa Eisenberg
What sets the critical genome length for sympatric speciation? A closed form and asymptotic theory
Dan Braha, Marcus A. M. de Aguiar, Vitor M. Marquioni
The emergence and evolution of a referential code in populations of bee-like agents
Grzegorz Chrupała
Tree Buckets and the Reconstruction of Pairs of Phylogenetic Trees
Sky Basire, Michael Hendriksen
Global geometry of the genotype-phenotype map illuminates a trade-off between penetrance and mutational adaptability
Yutaro Ikeda, Kunihiko Kaneko, Tetsuhiro S. Hatakeyama
The Informational Model of the Holobiont: Statistical Tests for Selection and Extension to a Theory of Variable Interactions
Antonio Carvajal-Rodríguez