Estimating Species Divergence Times in RevBayes – iEvoBio 2014

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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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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

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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!