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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Dating phylogenies with sequentially sampled tips.

Tanja Stadler1, Ziheng Yang

  • 1Institut für Integrative Biologie, Eidgenössiche Technische Hochschule Zürich, 8092 Zürich, Switzerland; Department of Biology, University College London, London WC1E 6BT, UK.

Systematic Biology
|May 1, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian algorithm for estimating divergence times from sequentially sampled molecular sequences. This method improves accuracy by using a flexible birth-death-sequential-sampling prior, crucial for viral evolution and ancient DNA studies.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genetics

Background:

  • Estimating divergence times is crucial for understanding evolutionary history.
  • Sequentially sampled molecular sequences, common in viral epidemics and ancient DNA, present unique challenges for dating.
  • Existing Bayesian methods may lack flexibility in prior distributions for divergence times.

Purpose of the Study:

  • To develop a novel Bayesian Markov chain Monte Carlo (MCMC) algorithm for accurate divergence time estimation.
  • To introduce a flexible birth-death-sequential-sampling (BDSS) model as a prior for divergence dating.
  • To assess the impact of priors and tree topology on divergence time estimates.

Main Methods:

  • Developed a Bayesian MCMC algorithm for divergence time estimation.
  • Derived the node age distribution under a BDSS model to serve as a flexible prior.
  • Implemented the BDSS prior in the MCMCtree program within the PAML package.
  • Applied the method to SIV/HIV-2 and influenza H1 gene datasets, comparing results with existing methods (likelihood-based and BEAST).

Main Results:

  • The BDSS prior demonstrated flexibility in generating diverse tree shapes, enabling sensitivity analyses.
  • Comparison with other methods on SIV/HIV-2 and influenza H1 datasets validated the approach.
  • Analysis revealed that multifurcating consensus trees can negatively impact time estimates.
  • Posterior time estimates for ancient nodes were sensitive to priors on times and rates.

Conclusions:

  • The developed Bayesian MCMC algorithm with the BDSS prior offers a robust method for divergence time estimation.
  • Careful consideration of priors and avoidance of multifurcating trees are recommended for reliable divergence dating.
  • Previous Bayesian dating studies might have overestimated confidence in their estimates for ancient divergences.