Nov 26, 2014

DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent

Bioinformatics
Graham JonesBengt Oxelman

Abstract

The multispecies coalescent model provides a formal framework for the assignment of individual organisms to species, where the species are modeled as the branches of the sp tree. None of the available approaches so far have simultaneously co-estimated all the relevant parameters in the model, without restricting the parameter space by requiring a guide tree and/or prior assignment of individuals to clusters or species. We present DISSECT, which explores the full space of possible clusterings of individuals and species tree topologies in a Bayesian framework. It uses an approximation to avoid the need for reversible-jump Markov Chain Monte Carlo, in the form of a prior that is a modification of the birth-death prior for the species tree. It incorporates a spike near zero in the density for node heights. The model has two extra parameters: one controls the degree of approximation and the second controls the prior distribution on the numbers of species. It is implemented as part of BEAST and requires only a few changes from a standard *BEAST analysis. The method is evaluated on simulated data and demonstrated on an empirical dataset. The method is shown to be insensitive to the degree of approximation, but quite sensitive to the s...Continue Reading

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Citations

Mentioned in this Paper

In Silico
Bio-Informatics
Phylogeny
Monte Carlo Method
Silene
Bayesian Prediction
Markov Chains
Silo (Dataset)
Computational Molecular Biology
Gophers

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