BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
File(s)
Author(s)
Type
Journal Article
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.
Date Issued
2019-04-08
Date Acceptance
2019-02-04
Citation
PLoS Computational Biology, 2019, 15 (4), pp.1-28
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
28
Journal / Book Title
PLoS Computational Biology
Volume
15
Issue
4
Copyright Statement
© 2019 Bouckaert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000467530600013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
NUCLEOTIDE SUBSTITUTION
LIKELIHOOD-ESTIMATION
SPECIES TREES
GENE TREES
INFERENCE
MODELS
TIME
COALESCENT
SPECIATION
RADIATION
Publication Status
Published
Article Number
ARTN e1006650
Date Publish Online
2019-04-08