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

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

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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


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


Citations

Citations

  • The recommended citation for this program is:

    • Drummond AJ, Rambaut A (2007) "BEAST: Bayesian evolutionary analysis by sampling trees." BMC Evolutionary Biology7:214

  • To cite the relaxed clock model in BEAST:

    • Drummond AJ, Ho SYW, Phillips MJ & Rambaut A (2006) PLoS Biology4, e88

  • To cite the Bayesian Skyline model in BEAST:

    • Drummond AJ, Rambaut A & Shapiro B and Pybus OG (2005) Mol BiolEvol22, 1185-1192

  • The original MCMC paper was:

    • Drummond AJ, Nicholls GK, Rodrigo AG & Solomon W (2002) Genetics161, 1307-1320


Basic pipeline

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)


Downloading programs

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


Practical rift valley fever virus

Practical: Rift Valley fever virus


Epidemiology of rvf

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)


Setting up xml file in beauti

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


Beauti practical

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


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??


Proper mixing

Proper mixing

  • First step – run chain longer

    • Open L200 files

  • Other steps to try:

    • Over parameterization – reduce complexity

    • Temporal/phylogenetic signal

    • Priors are inappropriate


Model testing

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


Summarizing tree

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


Advanced analyses

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


Change in effective population size over time

Change in effective population size over time

Log10 Ne

Log10 Ne


Bayesian genealogy of g gene

1916 (1868-1942)

Bayesian Genealogy Of G Gene


Additional resources

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/


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