Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method |
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Authors: | Yang Z; Rannala B |
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Institution: | Department of Integrative Biology, University of California, Berkeley 94720-3140, USA. |
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Abstract: | An improved Bayesian method is presented for estimating phylogenetic trees
using DNA sequence data. The birth-death process with species sampling is
used to specify the prior distribution of phylogenies and ancestral
speciation times, and the posterior probabilities of phylogenies are used
to estimate the maximum posterior probability (MAP) tree. Monte Carlo
integration is used to integrate over the ancestral speciation times for
particular trees. A Markov Chain Monte Carlo method is used to generate the
set of trees with the highest posterior probabilities. Methods are
described for an empirical Bayesian analysis, in which estimates of the
speciation and extinction rates are used in calculating the posterior
probabilities, and a hierarchical Bayesian analysis, in which these
parameters are removed from the model by an additional integration. The
Markov Chain Monte Carlo method avoids the requirement of our earlier
method for calculating MAP trees to sum over all possible topologies (which
limited the number of taxa in an analysis to about five). The methods are
applied to analyze DNA sequences for nine species of primates, and the MAP
tree, which is identical to a maximum-likelihood estimate of topology, has
a probability of approximately 95%.
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Keywords: | |
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