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Bioinformatics. 2015 Apr 1;31(7):991-8. doi: 10.1093/bioinformatics/btu770. Epub 2014 Nov 23.

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

Author information

1
Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden and Department of Biology, Faculty of Sciences, University of Dicle, 21280 Diyarbakir, Turkey.
2
Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden and Department of Biology, Faculty of Sciences, University of Dicle, 21280 Diyarbakir, Turkey Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden and Department of Biology, Faculty of Sciences, University of Dicle, 21280 Diyarbakir, Turkey.

Abstract

MOTIVATION:

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.

RESULTS:

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 second parameter, suggesting that large numbers of sequences are needed to draw firm conclusions.

AVAILABILITY AND IMPLEMENTATION:

http://tree.bio.ed.ac.uk/software/beast/, http://www.indriid.com/dissectinbeast.html.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25422051
DOI:
10.1093/bioinformatics/btu770
[Indexed for MEDLINE]

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