Relaxed Phylogenetics and Dating with Confidence
TLDR
In this paper, the authors introduce a new approach to perform relaxed phylogenetic analysis, which can be used to estimate phylogenies and divergence times in the face of uncertainty in evolutionary rates and calibration times.Abstract:
In phylogenetics, the unrooted model of phylogeny and the strict molecular clock model are two extremes of a continuum. Despite their dominance in phylogenetic inference, it is evident that both are biologically unrealistic and that the real evolutionary process lies between these two extremes. Fortunately, intermediate models employing relaxed molecular clocks have been described. These models open the gate to a new field of “relaxed phylogenetics.” Here we introduce a new approach to performing relaxed phylogenetic analysis. We describe how it can be used to estimate phylogenies and divergence times in the face of uncertainty in evolutionary rates and calibration times. Our approach also provides a means for measuring the clocklikeness of datasets and comparing this measure between different genes and phylogenies. We find no significant rate autocorrelation among branches in three large datasets, suggesting that autocorrelated models are not necessarily suitable for these data. In addition, we place these datasets on the continuum of clocklikeness between a strict molecular clock and the alternative unrooted extreme. Finally, we present analyses of 102 bacterial, 106 yeast, 61 plant, 99 metazoan, and 500 primate alignments. From these we conclude that our method is phylogenetically more accurate and precise than the traditional unrooted model while adding the ability to infer a timescale to evolution.read more
Citations
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Journal ArticleDOI
MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space
Fredrik Ronquist,Maxim Teslenko,Paul van der Mark,Daniel L. Ayres,Aaron E. Darling,Sebastian Höhna,Bret Larget,Liang Liu,Marc A. Suchard,John P. Huelsenbeck +9 more
TL;DR: The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly, and provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates.
Journal ArticleDOI
BEAST: Bayesian evolutionary analysis by sampling trees
TL;DR: BEAST is a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree that provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions.
Journal ArticleDOI
BEAST 2: A Software Platform for Bayesian Evolutionary Analysis
Remco R. Bouckaert,Joseph Heled,Denise Kühnert,Timothy G. Vaughan,Chieh-Hsi Wu,Dong Xie,Marc A. Suchard,Andrew Rambaut,Alexei J. Drummond +8 more
TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.
Journal ArticleDOI
Bayesian Inference of Species Trees from Multilocus Data
Joseph Heled,Alexei J. Drummond +1 more
TL;DR: It is demonstrated that both BEST and the new Bayesian Markov chain Monte Carlo method for the multispecies coalescent have much better estimation accuracy for species tree topology than concatenation, and the method outperforms BEST in divergence time and population size estimation.
Journal ArticleDOI
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.
Remco R. Bouckaert,Remco R. Bouckaert,Timothy G. Vaughan,Timothy G. Vaughan,Joëlle Barido-Sottani,Joëlle Barido-Sottani,Sebastián Duchêne,Mathieu Fourment,Alexandra Gavryushkina,Joseph Heled,Graham Jones,Denise Kühnert,Nicola De Maio,Michael Matschiner,Fábio K. Mendes,Nicola F. Müller,Nicola F. Müller,Huw A. Ogilvie,Louis du Plessis,Alex Popinga,Andrew Rambaut,David A. Rasmussen,Igor Siveroni,Marc A. Suchard,Chieh-Hsi Wu,Dong Xie,Chi Zhang,Tanja Stadler,Tanja Stadler,Alexei J. Drummond +29 more
TL;DR: A series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release are described.
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