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Phylogenetic analyses of macroevolutionary processes require estimates of species divergence times. Critically, this requires a framework for modeling lineage-specific substitution rates and speciation times while accounting for uncertainty in the tree topology. Bayesian inference methods are well suited to such analyses. However, implementations of these methods have historically been limited by the available models and priors in each program. RevBayes is a new statistical programming environment that provides a flexible framework for phylogenetic inference. We have implemented phylogenetic inference under a diverse set of relaxed-clock and branching-process models in RevBayes. The user specifies the model and analysis details in Rev -- an interpreted programming language based on R. I will present the theory behind the implementation of phylogenetic models in RevBayes that gives the software its flexibility and show the results of empirical analyses.
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Tracy A. HeathUniversity of Kansas
University of California, BerkeleyIowa State University (in 2015)
@trayc7
Sebastian HöhnaUniversity of California, Davis
iEvoBio 2014 – Raleigh, NC
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Prior options in MrBayes v3.2
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Prior options in MrBayes v3.2
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Several software packages inphylogenetics are moving toward amore modular framework
• reuse code
• easier to extend existing modelsand implement new modelsthrough a rich, language-basedinterface
• provides a unified framework foranalyses under complex models
RevBayes
Bali-Phy
BEAST2
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Fully integrative Bayesian inference ofphylogenetic parameters
http://sourceforge.net/projects/revbayes/
Development team
Höhna
Landis Lartillot Ronquist
Boussau Heath Huelsenbeck
& others...
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Graphical models provide tools forvisually & computationally representingcomplex, parameter-rich probabilisticmodels
We can depict the conditionaldependence structure of variousparameters and other random variables
Höhna, Heath, Boussau, Landis, Ronquist, Huelsenbeck. 2014.Probabilistic Graphical Model Representation in Phylogenetics.
Systematic Biology in press. (doi: 10.1093/sysbio/syu039)
G M RBModels are represented by directed acyclic graphs (DAGs)
G M RBModels are represented by directed acyclic graphs (DAGs)
G M RBModels are represented by directed acyclic graphs (DAGs)
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A phylogenetic graphical model:GTR, birth-death process, global molecular clock
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C M RB
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Single Rate Model (Zuckerkandl & Pauling, 1962)
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Uncorrelated Lognormal Branch-Rate Model (Drummond et al. 2006)
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Dirichlet Process Prior on Branch Rates (Heath, Holder, Huelsenbeck 2012)
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Autocorrelated Lognormal Branch-Rate Model (Thorne, Kishino & Painter 1998; Kishino, Thorne & Bruno 2001)
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Autocorrelated Lognormal Branch-Rate Model — with a Dirichlet Process Prior on the Variance
T R LAn R-like language for specifying models and MCMC
# An example (partial)
# read the data
D <- readCharacterData("data/primates.nex")[1]
# lognormal prior on strict clock rate
theta <- 0.5
sigma <- 0.75
mu ∼ exponential(theta)
clock_rate ∼ lognormal(mu, sigma)
RealPos branch_rates
for (i in 1:n) {
branch_rates[i] := clock_rate
}
# Moves on model parameters
moves[1] <- mScale(mu, 0.5, true, 5.0)
moves[2] <- mScale(clock_rate, 1.0, true, 2.0)
. . .
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Graphical-model framework allowsfor implementation of complexmodels
Test and develop new proposalmechanisms for models withcorrelated parameters
Joint estimation of allphylogenetic parameters
Simulation under any definedmodel
1.0 0.00.250.50.75
Relative time
A
B
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D
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G
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true age
age 95% CI
DPP Relative Time Tree
ATrilogenetics: Likelihood Methods for Phylogenetics ofFossil Taxa: http://phylo.bio.ku.edu/fossil/
Thanks to:
• Michael Landis• Bastien Boussau• Johan Dunfalk• Chi Zhang• John Huelsenbeck
• Fredrik Ronquist• Nicolas Lartillot• Mark Holder• Tanja Stadler• Brian Moore
Support:
• NSF DEB-1256993• NIH GM-069801 & GM-086887• NESCent Academy (http://bit.ly/P11bhy)
Twitter: @trayc7 Slides: www.slideshare.net/trayc7
Thanks!
