Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

TreeTime: maximum likelihood phylodynamic analysis

Pavel Sagulenko, Vadim Puller, View ORCID ProfileRichard A. Neher
doi: https://doi.org/10.1101/153494
Pavel Sagulenko
1Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vadim Puller
1Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
2Biozentrum, University of Basel, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard A. Neher
1Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
2Biozentrum, University of Basel, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Richard A. Neher
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

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 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted June 22, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
TreeTime: maximum likelihood phylodynamic analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
TreeTime: maximum likelihood phylodynamic analysis
Pavel Sagulenko, Vadim Puller, Richard A. Neher
bioRxiv 153494; doi: https://doi.org/10.1101/153494
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
TreeTime: maximum likelihood phylodynamic analysis
Pavel Sagulenko, Vadim Puller, Richard A. Neher
bioRxiv 153494; doi: https://doi.org/10.1101/153494

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3479)
  • Biochemistry (7318)
  • Bioengineering (5296)
  • Bioinformatics (20196)
  • Biophysics (9976)
  • Cancer Biology (7701)
  • Cell Biology (11249)
  • Clinical Trials (138)
  • Developmental Biology (6417)
  • Ecology (9915)
  • Epidemiology (2065)
  • Evolutionary Biology (13276)
  • Genetics (9352)
  • Genomics (12551)
  • Immunology (7673)
  • Microbiology (18937)
  • Molecular Biology (7417)
  • Neuroscience (40887)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2125)
  • Physiology (3140)
  • Plant Biology (6837)
  • Scientific Communication and Education (1270)
  • Synthetic Biology (1891)
  • Systems Biology (5296)
  • Zoology (1084)