Abstract

The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.

Details

Title
Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10
Author
Suchard, Marc A 1 ; Lemey, Philippe 2 ; Baele, Guy 2 ; Ayres, Daniel L 3 ; Drummond, Alexei J 4 ; Rambaut, Andrew 5 

 Department of Biomathematics, David Geffen School of MedicineUniversity of California, Los Angeles, 621 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA; Department of Biostatistics, Fielding School of Public HealthUniversity of California, Los Angeles, 650 Charles E, Young Dr., South, Los Angeles, CA, 90095 USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA 
 Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium 
 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 125 Biomolecular Science Bldg #296, College Park, MD 20742 USA 
 Department of Computer Science, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ; Centre for Computational Evolution, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ 
 Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK 
Publication year
2018
Publication date
Jan 2018
Publisher
Oxford University Press
e-ISSN
20571577
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171773087
Copyright
© The Author(s) 2018. Published by Oxford University Press. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.