Literature DB >> 28673048

Taming the BEAST-A Community Teaching Material Resource for BEAST 2.

Joëlle Barido-Sottani1,2, Veronika Bošková1,2, Louis Du Plessis1,3, Denise Kühnert1,2,4, Carsten Magnus1,2, Venelin Mitov1,2, Nicola F Müller1,2, Julija PecErska1,2, David A Rasmussen1,2, Chi Zhang1,2, Alexei J Drummond5, Tracy A Heath6, Oliver G Pybus3, Timothy G Vaughan5, Tanja Stadler1,2.   

Abstract

Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the "Taming the Beast" (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.
© The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Entities:  

Keywords:  Bayesian inference; MCMC; phylodynamics; phylogenetics

Mesh:

Year:  2018        PMID: 28673048      PMCID: PMC5925777          DOI: 10.1093/sysbio/syx060

Source DB:  PubMed          Journal:  Syst Biol        ISSN: 1063-5157            Impact factor:   15.683


  16 in total

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Authors:  A J Drummond; A Rambaut; B Shapiro; O G Pybus
Journal:  Mol Biol Evol       Date:  2005-02-09       Impact factor: 16.240

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Journal:  Mol Biol Evol       Date:  2010-03-04       Impact factor: 16.240

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Journal:  Syst Biol       Date:  2016-05-28       Impact factor: 15.683

4.  Relaxed phylogenetics and dating with confidence.

Authors:  Alexei J Drummond; Simon Y W Ho; Matthew J Phillips; Andrew Rambaut
Journal:  PLoS Biol       Date:  2006-03-14       Impact factor: 8.029

5.  Efficient Bayesian inference under the structured coalescent.

Authors:  Timothy G Vaughan; Denise Kühnert; Alex Popinga; David Welch; Alexei J Drummond
Journal:  Bioinformatics       Date:  2014-04-20       Impact factor: 6.937

6.  Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration.

Authors:  Alexandra Gavryushkina; David Welch; Tanja Stadler; Alexei J Drummond
Journal:  PLoS Comput Biol       Date:  2014-12-04       Impact factor: 4.475

7.  Bayesian inference of population size history from multiple loci.

Authors:  Joseph Heled; Alexei J Drummond
Journal:  BMC Evol Biol       Date:  2008-10-23       Impact factor: 3.260

8.  Bayesian inference of species trees from multilocus data.

Authors:  Joseph Heled; Alexei J Drummond
Journal:  Mol Biol Evol       Date:  2009-11-11       Impact factor: 16.240

9.  BEAST 2: a software platform for Bayesian evolutionary analysis.

Authors:  Remco Bouckaert; Joseph Heled; Denise Kühnert; Tim Vaughan; Chieh-Hsi Wu; Dong Xie; Marc A Suchard; Andrew Rambaut; Alexei J Drummond
Journal:  PLoS Comput Biol       Date:  2014-04-10       Impact factor: 4.475

10.  Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data.

Authors:  Denise Kühnert; Tanja Stadler; Timothy G Vaughan; Alexei J Drummond
Journal:  Mol Biol Evol       Date:  2016-04-09       Impact factor: 16.240

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