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Virus Evol. 2018 Jan 8;4(1):vex042. doi: 10.1093/ve/vex042. eCollection 2018 Jan.

TreeTime: Maximum-likelihood phylodynamic analysis.

Author information

1
Max Planck Institute for Developmental Biology, Spemannstrasse 35, Tübingen 72076, Germany.
2
Biozentrum, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland.
3
SIB Swiss Institute of Bioinformatics, Klingelbergstrasse 50, 4056 Basel, Switzerland.

Abstract

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.

KEYWORDS:

molecular clock phylogenies; phylodynamics; python

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