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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.

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Journal ArticleDOI

MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space

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

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

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.
References
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Journal ArticleDOI

Equation of state calculations by fast computing machines

TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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MRBAYES: Bayesian inference of phylogenetic trees

TL;DR: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo, and an executable is available at http://brahms.rochester.edu/software.html.
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Monte Carlo Sampling Methods Using Markov Chains and Their Applications

TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
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Evolutionary trees from DNA sequences: A maximum likelihood approach

TL;DR: A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available that allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests.
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