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Bioinformatics Advance Access originally published online on May 22, 2007
Bioinformatics 2007 23(15):1962-1968; doi:10.1093/bioinformatics/btm264
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

The evolutionary forest algorithm

Scotland C. Leman 1,*, Marcy K. Uyenoyama 2, Michael Lavine 1 and Yuguo Chen 3

1Institute of Statistics and Decision Sciences and 2Department of Biology, Duke University and 3Department of Statistics, University of Illinois at Urbana-Champaign, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.

Results: The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities.

Availability: A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13

Contact: scotland{at}stat.duke.edu

Associate Editor: Keith Crandall

Received on December 3, 2006; revised on April 5, 2007; accepted on May 10, 2007

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