The evolutionary forest algorithm.

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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.


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.


A C++ implementation of this method is freely accessible at





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Leman, Scotland C, Marcy K Uyenoyama, Michael Lavine and Yuguo Chen (2007). The evolutionary forest algorithm. Bioinformatics (Oxford, England), 23(15). pp. 1962–1968. 10.1093/bioinformatics/btm264 Retrieved from

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Marcy K. Uyenoyama

Professor of Biology

Marcy Uyenoyama studies mechanisms of evolutionary change at the molecular and population levels. Among the questions under study include the prediction and detection of the effects of natural selection on genomic structure. A major area of research addresses the development of maximum-likelihood and Bayesian methods for inferring evolutionary processes from the pattern of molecular variation. Evolutionary processes currently under study include characterization of population structure across genomes.

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