Bayesian Phylogenetics with BEAUti and the BEAST 1.7
TLDR
The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7 is presented, which implements a family of Markov chain Monte Carlo algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses.Abstract:
Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.ukread more
Citations
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Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7.
TL;DR: The software package Tracer is presented, for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference, which provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more.
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BEAST 2: A Software Platform for Bayesian Evolutionary Analysis
Remco R. Bouckaert,Joseph Heled,Denise Kühnert,Timothy G. Vaughan,Chieh-Hsi Wu,Dong Xie,Marc A. Suchard,Andrew Rambaut,Alexei J. Drummond +8 more
TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.
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Ultrafast Approximation for Phylogenetic Bootstrap
TL;DR: This work proposes an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees and offers an efficient and easy-to-use software to perform the UFBoot analysis with ML tree inference.
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Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10
TL;DR: The BEAST software package 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.
Journal ArticleDOI
Phylogenomics resolves the timing and pattern of insect evolution
Bernhard Misof,Shanlin Liu,Karen Meusemann,Ralph S. Peters,Alexander Donath,Christoph Mayer,Paul B. Frandsen,Jessica L. Ware,Tomas Flouri,Rolf G. Beutel,Oliver Niehuis,Malte Petersen,Fernando Izquierdo-Carrasco,Torsten Wappler,Jes Rust,Andre J. Aberer,Ulrike Aspöck,Ulrike Aspöck,Horst Aspöck,Daniela Bartel,Alexander Blanke,Simon Berger,Alexander Böhm,Thomas R. Buckley,Brett Calcott,Junqing Chen,Frank Friedrich,Makiko Fukui,Mari Fujita,Carola Greve,Peter Grobe,Shengchang Gu,Ying Huang,Lars S. Jermiin,Akito Y. Kawahara,Lars Krogmann,Martin Kubiak,Robert Lanfear,Robert Lanfear,Robert Lanfear,Harald Letsch,Yiyuan Li,Zhenyu Li,Jiguang Li,Haorong Lu,Ryuichiro Machida,Yuta Mashimo,Pashalia Kapli,Pashalia Kapli,Duane D. McKenna,Guanliang Meng,Yasutaka Nakagaki,José Luis Navarrete-Heredia,Michael Ott,Yanxiang Ou,Günther Pass,Lars Podsiadlowski,Hans Pohl,Björn M. von Reumont,Kai Schütte,Kaoru Sekiya,Shota Shimizu,Adam Slipinski,Alexandros Stamatakis,Alexandros Stamatakis,Wenhui Song,Xu Su,Nikolaus U. Szucsich,Meihua Tan,Xuemei Tan,Min Tang,Jingbo Tang,Gerald Timelthaler,Shigekazu Tomizuka,Michelle D. Trautwein,Xiaoli Tong,Toshiki Uchifune,Manfred Walzl,Brian M. Wiegmann,Jeanne Wilbrandt,Benjamin Wipfler,Thomas K. F. Wong,Qiong Wu,Gengxiong Wu,Yinlong Xie,Shenzhou Yang,Qing Yang,David K. Yeates,Kazunori Yoshizawa,Qing Zhang,Rui Zhang,Wenwei Zhang,Yunhui Zhang,Jing Zhao,Chengran Zhou,Lili Zhou,Tanja Ziesmann,Shijie Zou,Yingrui Li,Xun Xu,Yong Zhang,Huanming Yang,Jian Wang,Jun Wang,Karl M. Kjer,Xin Zhou +105 more
TL;DR: The phylogeny of all major insect lineages reveals how and when insects diversified and provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.
References
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Journal ArticleDOI
BEAST: Bayesian evolutionary analysis by sampling trees
TL;DR: BEAST is a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree that provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions.
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Bayesian Inference of Species Trees from Multilocus Data
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TL;DR: It is demonstrated that both BEST and the new Bayesian Markov chain Monte Carlo method for the multispecies coalescent have much better estimation accuracy for species tree topology than concatenation, and the method outperforms BEST in divergence time and population size estimation.
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TL;DR: It is concluded that the Bayesian phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
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Bayesian inference of population size history from multiple loci
Joseph Heled,Alexei J. Drummond +1 more
TL;DR: The Extended Bayesian Skyline Plot is presented, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci, demonstrating the essential role of multiple loco in recovering population size dynamics.
Journal ArticleDOI
Smooth Skyride through a Rough Skyline: Bayesian Coalescent-Based Inference of Population Dynamics
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