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Leto 2020

Pavel Sagulenko, Vadim Puller, Richard A. Neher. TreeTime: Maximum-likelihood phylodynamic analysis. Virus Evol, 4(1):vex042. 2018.

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Mutations that accumulate in the genome of cells or viruses can be used to infer 
their evolutionary history. In the case of rapidly evolving organisms, genomes
can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are
particularly useful to understand the epidemiology of rapidly evolving viral
pathogens. As the number of genome sequences available for different pathogens
has increased dramatically over the last years, phylodynamic analysis with
traditional methods becomes challenging as these methods scale poorly with
growing datasets. 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 runtime of TreeTime scales linearly with dataset size.