RT Journal Article
SR Electronic
T1 TreeTime: maximum likelihood phylodynamic analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 153494
DO 10.1101/153494
A1 Pavel Sagulenko
A1 Vadim Puller
A1 Richard A. Neher
YR 2017
UL http://biorxiv.org/content/early/2017/07/18/153494.abstract
AB Mutations that accumulate in the genome of replicating biological organisms can be used to infer their evolutionary history. In the case of measurably evolving organisms genomes often reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. The number of genome sequences available for different pathogens, however, has increased dramatically over the last couple of years and traditional methods for phylodynamic analysis scale poorly with growing data sets. Here, we present TreeTime, a Python based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The run time of TreeTime scales linearly with data set size.