BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

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dc.contributor.author Bouckaert, Remco en
dc.contributor.author Vaughan, Timothy G en
dc.contributor.author Barido-Sottani, Joëlle en
dc.contributor.author Duchêne, Sebastián en
dc.contributor.author Fourment, Mathieu en
dc.contributor.author Gavryushkina, Alexandra en
dc.contributor.author Heled, Joseph en
dc.contributor.author Jones, Graham en
dc.contributor.author Kühnert, Denise en
dc.contributor.author De Maio, Nicola en
dc.contributor.author Matschiner, Michael en
dc.contributor.author Kuriki Mendes, Fabio Henrique en
dc.contributor.author Müller, Nicola F en
dc.contributor.author Ogilvie, Huw A en
dc.contributor.author du Plessis, Louis en
dc.contributor.author Popinga, Alex en
dc.contributor.author Rambaut, Andrew en
dc.contributor.author Rasmussen, David en
dc.contributor.author Siveroni, Igor en
dc.contributor.author Suchard, Marc A en
dc.contributor.author Wu, Chieh-Hsi en
dc.contributor.author Xie, Dong en
dc.contributor.author Zhang, Chi en
dc.contributor.author Stadler, Tanja en
dc.contributor.author Drummond, Alexei en
dc.date.accessioned 2019-10-29T01:53:31Z en
dc.date.issued 2019-04-08 en
dc.identifier.citation PLoS computational biology 15(4):e1006650 08 Apr 2019 en
dc.identifier.issn 1553-734X en
dc.identifier.uri http://hdl.handle.net/2292/48713 en
dc.description.abstract Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release. en
dc.format.medium Electronic-eCollection en
dc.language eng en
dc.relation.ispartofseries PLoS computational biology en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.subject Animals en
dc.subject Humans en
dc.subject Monte Carlo Method en
dc.subject Bayes Theorem en
dc.subject Markov Chains en
dc.subject Computational Biology en
dc.subject Evolution, Molecular en
dc.subject Phylogeny en
dc.subject Models, Genetic en
dc.subject Computer Simulation en
dc.subject Software en
dc.subject Biological Evolution en
dc.title BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. en
dc.type Journal Article en
dc.identifier.doi 10.1371/journal.pcbi.1006650 en
pubs.issue 4 en
pubs.begin-page e1006650 en
pubs.volume 15 en
dc.rights.holder Copyright: The authors en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Research Support, Non-U.S. Gov't en
pubs.subtype research-article en
pubs.subtype Validation Studies en
pubs.subtype Journal Article en
pubs.subtype Research Support, N.I.H., Extramural en
pubs.elements-id 769035 en
pubs.org-id Science en
pubs.org-id Biological Sciences en
pubs.org-id School of Computer Science en
dc.identifier.eissn 1553-7358 en
pubs.record-created-at-source-date 2019-04-09 en
pubs.dimensions-id 30958812 en


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