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TreeTime: Maximum-likelihood phylodynamic analysis

Sagulenko, Pavel and Puller, Vadim and Neher, Richard A.. (2018) TreeTime: Maximum-likelihood phylodynamic analysis. Virus Evolution, 4 (1, vex042). pp. 1-9.

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Official URL: https://edoc.unibas.ch/65359/

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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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Computational Modeling of Biological Processes (Neher)
UniBasel Contributors:Neher, Richard A
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Oxford University Press
e-ISSN:2057-1577
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:04 Nov 2019 14:50
Deposited On:12 Sep 2019 14:40

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