Pavel Sagulenko, Vadim Puller and Richard Neher
bioRxiv, vol. , 153494, 2017
Mutations that accumulate in the genome of replicating biological organisms can be used to infer their evolutionary history. In case of measurably evolving organisms genomes often reveal their detailed spatio-temporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. The volume of genome sequences available for different pathogens, however, have 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.