RT Journal Article SR Electronic T1 DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent JF bioRxiv FD Cold Spring Harbor Laboratory SP 003178 DO 10.1101/003178 A1 Graham Jones A1 Bengt Oxelman YR 2014 UL http://biorxiv.org/content/early/2014/12/11/003178.abstract AB 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 species 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 MCMC, 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 data set. 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 http://code.google.com/p/beast-mcmc/, http://www.indriid.com/dissectinbeast.htmlContact art{at}gjones.name, http://www.indriid.comSupplementary information Supplementary material is available.