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Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST) Rebecca R. Gray, Ph.D. Department of Pathology University of Florida

Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

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Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST). Rebecca R. Gray, Ph.D. Department of Pathology University of Florida. BEAST: is a cross-platform program for Bayesian MCMC analysis of molecular sequences - PowerPoint PPT Presentation

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Page 1: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Rebecca R. Gray, Ph.D.Department of Pathology

University of Florida

Page 2: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

• BEAST:– is a cross-platform program for Bayesian MCMC

analysis of molecular sequences– entirely orientated towards rooted, time-measured

phylogenies inferred using strict or relaxed molecular clock models

– can be used as a method of reconstructing phylogenies, but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology

– uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability

Page 3: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Citations• The recommended citation for this program is:

– Drummond AJ, Rambaut A (2007) "BEAST: Bayesian evolutionary analysis by sampling trees." BMC Evolutionary Biology 7:214

• To cite the relaxed clock model in BEAST: – Drummond AJ, Ho SYW, Phillips MJ & Rambaut A (2006)

PLoS Biology 4, e88• To cite the Bayesian Skyline model in BEAST:

– Drummond AJ, Rambaut A & Shapiro B and Pybus OG (2005) Mol Biol Evol 22, 1185-1192

• The original MCMC paper was: – Drummond AJ, Nicholls GK, Rodrigo AG & Solomon W

(2002) Genetics 161, 1307-1320

Page 4: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Basic Pipeline• 1) setting up xml file (beauti)• 2) running xml file (beast)• 3) evaluating the performance of the run

(Tracer)• 4) comparing models, obtaining

estimates of parameters (Tracer)• 5) summarizing the tree distribution

(TreeAnnotator)• 6) viewing MCC tree (Figtree)

Page 5: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Downloading programs• http://beast.bio.ed.ac.uk/Main_Page\

– Download contains beauti, BEAST, TreeAnnotator

• http://beast.bio.ed.ac.uk/Tracer• http://beast.bio.ed.ac.uk/FigTree

Page 6: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

PRACTICAL: RIFT VALLEY FEVER VIRUS

Page 7: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Epidemiology of RVF• The virus was first identified in 1931 in the Rift

Valley of Kenya

• Mosquito vector, primarily infects livestock

• 1997–1998, a major outbreak occurred in Kenya, Somalia and the United Republic of Tanzania

• September 2000 cases were confirmed in Saudi Arabia and Yemen (first reported occurrence of the disease outside the African continent)

Page 8: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Setting up xml file in beauti• Requires a nexus file

– Helpful to have dates with the sample name

– Use the finest resolution available• GUI interface allows basic selection

of parameters • Xml file can be manually edited to

test specific hypotheses/tweak run

Page 9: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Beauti practical• Import alignment (g_63.nex)• Tip dates – use tipdates, guess dates (years

since some time in the past)• Site models – use GTR + G, empirical base

frequencies• Test hypothesis of strict vs. relaxed

molecular clock• Trees – coalescent tree prior – constant size• 5 x 107 generations

Page 10: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

BEAST

• Open xml file with text editor• Run in beast• Check mixing of the MCMC chain• Open S log files in Tracer• Open L and G2 log files• What can we do about the trace??

Page 11: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Proper mixing• First step – run chain longer

– Open L200 files• Other steps to try:

– Over parameterization – reduce complexity

– Temporal/phylogenetic signal– Priors are inappropriate

Page 12: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Model testing• Bayes factors:

– Compare estimates of the marginal likelihoods of the models of interest

– 2*(ln marginal likelihood model 1 – ln marginal likelihood model 2)

– >10, strong support for alternative (more complex model)

• Strict clock vs. relaxed clock– Also consider the coefficient of variation

Page 13: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Summarizing tree• TreeAnnotator

– Burnin 10% (501 samples)– Keep median heights– MCC tree

• Visualizing tree: FigTree– Posterior probabilities for branches– Median heights for clades of interest

Page 14: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Advanced analyses• Different coalescent priors

– Parametric models (exponential, logistic)– Bayesian skyline plots

• Phylogeography– Lemey et al, 2009, Plos Computational

Biology• Site specific rates of variation

Page 15: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Change in effective population size over time

Log1

0 N

e

Log1

0 N

e

Page 16: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

16

Bayesian Genealogy Of G Gene

1916 (1868-1942)

Page 17: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)
Page 18: Practical Session: Bayesian evolutionary analysis by sampling trees (BEAST)

Additional resources• Tutorials on the beast website,

google group• 16th International BioInformatics

Workshop on Virus Evolution and Molecular Epidemiology– Johns Hopkins University, Baltimore– 29 August - 03 September 2010,

Bethesda, USA– http://www.rega.kuleuven.be/cev/

workshop/