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

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dc.contributor.authorJones, Graham
dc.contributor.authorAydin, Zeynep
dc.contributor.authorOxelman, Bengt
dc.date.accessioned2024-04-24T17:08:04Z
dc.date.available2024-04-24T17:08:04Z
dc.date.issued2015
dc.departmentDicle Üniversitesien_US
dc.description.abstractMotivation: 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.en_US
dc.description.sponsorshipSwedish Research Council [2012-3719]en_US
dc.description.sponsorshipThis work was supported by the Swedish Research Council [grant 2012-3719 to B.O.].en_US
dc.identifier.doi10.1093/bioinformatics/btu770
dc.identifier.endpage998en_US
dc.identifier.issn1367-4803
dc.identifier.issn1367-4811
dc.identifier.issue7en_US
dc.identifier.pmid25422051
dc.identifier.scopus2-s2.0-84929142758
dc.identifier.scopusqualityQ1
dc.identifier.startpage991en_US
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btu770
dc.identifier.urihttps://hdl.handle.net/11468/17183
dc.identifier.volume31en_US
dc.identifier.wosWOS:000352269500003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherOxford Univ Pressen_US
dc.relation.ispartofBioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keyword]en_US
dc.titleDISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescenten_US
dc.titleDISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent
dc.typeArticleen_US

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