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Protocols

BEAST specifications

Information


Unique identifier OMICS_04233
Name BEAST
Alternative name Bayesian Evolutionary Analysis by Sampling Trees
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
License GNU General Public License version 2.0
Computer skills Advanced
Version 2.4.1
Stability Stable
Maintained Yes

Subtools


  • Tracer
  • TreeAnnotator

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Documentation


Publications for Bayesian Evolutionary Analysis by Sampling Trees

BEAST citations

 (3150)
call_split

Asynchronous evolution of interdependent nest characters across the avian phylogeny

2018
Nat Commun
PMCID: 5951845
PMID: 29760391
DOI: 10.1038/s41467-018-04265-x
call_split See protocol

[…] 200 million iterations and a burn-in of 20 million iterations. We sampled the parameter estimates every 10,000 iterations and reported the averages of the three analyses. We used Tracer v1.6 (http://beast.bio.ed.ac.uk/Tracer) to ensure adequate mixing and properly high effective sample sizes (ESS > 500) for posterior probability distributions. We used Bayes factors calculated from the harmonic me […]

library_books

Analysis of the canid Y chromosome phylogeny using short read sequencing data reveals the presence of distinct haplogroups among Neolithic European dogs

2018
BMC Genomics
PMCID: 5946424
PMID: 29747566
DOI: 10.1186/s12864-018-4749-z

[…] he best fitting substitution model of 12 candidates as determined by the lowest AIC []. We estimated the time of the most recent common ancestor (TMRCA) for branches of interest using the MCMC method BEAST using both a strict and relaxed molecular clock with a log-normal prior distribution on the dog-coyote TMRCA [, ]. We merged two independent BEAST runs performed for 10,000,000 iterations with s […]

library_books

Global evolution and phylogeography of Brucella melitensis strains

2018
BMC Genomics
PMCID: 5946514
PMID: 29747573
DOI: 10.1186/s12864-018-4762-2

[…] 0 generations. An estimate of the convergence of the MCMC topology and parameters was carried out in the Tracer v1.6 program []. All ESS parameter values were > 200. The trees were combined using the TreeAnnotator component from the BEAST 2.3.0 package to obtain a consensus tree, the burn-in parameter for each chain was defined as 20%, the resulting phylogenetic tree file was visualized in the Fig […]

library_books

NS1 codon usage adaptation to humans in pandemic Zika virus

2018
PMCID: 5942634
PMID: 29768530
DOI: 10.1590/0074-02760170385

[…] (µ) using Path-O-Gen v1.4 (http://tree.bio.ed.ac.uk/soſtware/patho- gen/) with the ML tree for non-recombinant genomic sequences (alignment available from: https://github.com/CaioFreire/CUB). We used BEAST program v1.82 (), with the estimated evolutionary rate prior (m = 4.8E-4 ± 2E-4 substitution/site/year) under a relaxed lognormal molecular clock and a GTR+G+I substitution model to infer a time […]

library_books

Computational analysis of the receptor binding specificity of novel influenza A/H7N9 viruses

2018
BMC Genomics
PMCID: 5954268
PMID: 29764421
DOI: 10.1186/s12864-018-4461-z

[…] ct molecular clock model to produce 2000 tree samples logged every 25,000 generations. Beast’s outputs were assessed with TRACER v1.6 [] and summarized as a maximum clade credibility (MCC) tree using TreeAnnotator v2.4.4 with 10% burn-in. The time-tree for each gene was visualized using ggtree package []. […]

library_books

Re exploration of U’s Triangle Brassica Species Based on Chloroplast Genomes and 45S nrDNA Sequences

2018
Sci Rep
PMCID: 5943242
PMID: 29743507
DOI: 10.1038/s41598-018-25585-4

[…] us sativus was obtained from GenBank.Chloroplast and 45SnrDNA sequences from 28 genotypes were subjected to tree topology analysis and divergence time estimation using Bayesian methods implemented in BEAST (http://beast.bio.ed.ac.uk/). The BEAST program assumes auto-correlation, and is widely used to estimate the uncertainty of divergence dates and branch lengths, to estimate divergence using know […]

Citations

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BEAST institution(s)
Computational Evolution Group, Department of Computer Science, University of Auckland, Auckland, New Zealand; Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland; Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand; Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA; Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK; Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Auckland, New Zealand
BEAST funding source(s)
A Rutherford Discovery Fellowship from the Royal Society of New Zealand; Marsden grant UOA0809 from the Royal Society of New Zealand; National Institutes of Health grant R01 HG006139; National Science Foundation grants DMS-0856099 and DMS-1264153

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