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E-grāmata: Bayesian Evolutionary Analysis with BEAST

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(University of Auckland), (University of Auckland)
  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Aug-2015
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316308486
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Bayesian Evolutionary Analysis with BEASTPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Aug-2015
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316308486
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A comprehensive overview of Bayesian approaches to phylogenetics using Markov chain Monte Carlo (MCMC) methods, offering theoretical insight, pragmatic advice and tools to develop further models. This title is a one-stop reference to applying the latest phylogenetic models in BEAST 2, interpreting the analyses and extending these models further.

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: • Addresses the theoretical aspects of the field • Advises on how to prepare and perform phylogenetic analysis • Helps with interpreting analyses and visualisation of phylogenies • Describes the software architecture • Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users – from those using phylogenetic tools, to computational biologists and Bayesian statisticians.

Recenzijas

'Want to construct a phylogeny, add in calibrated time points or work out the past history of an epidemic? The open source package BEAST has established itself as the industry standard for all this and more. This definitive book, explaining what is under the hood, how the user can customize extensions and, most critically, a simple 'how to' users guide, is necessary reading for beginners and specialists alike.' Laurence D. Hurst, University of Bath 'In concert with the dramatic improvements to DNA sequencing technology, Bayesian inference has revolutionized population genetics, phylogenetics, and divergence time estimation. A similar impact on epidemiology appears imminent via a suite of new Bayesian methods that incorporate host and pathogen DNA sequence data into established mathematical frameworks. This book is an accessible and thorough introduction to these Bayesian procedures. However, the book does far more than explain the theory. It also includes clear guides on how to use the BEAST 2 software for performing Bayesian analyses, and how to visualize the results. Because the software is designed to be extensible, the book instructs users to write their own code to supplement the diverse methods that are already implemented in BEAST 2. This book is timely and is written by two of the leaders of the field. I highly recommend it.' Jeff Thorne, North Carolina State University

Papildus informācija

Covers theory, practice and programming in Bayesian phylogenetics with BEAST. The why, how and what of BEAST 2.
Preface ix
Acknowledgements x
Summary of most significant capabilities of BEAST 2 xi
Part I Theory 1(76)
1 Introduction
3(18)
1.1 Molecular phylogenetics
4(2)
1.2 Coalescent theory
6(2)
1.3 Virus evolution and phylodynamics
8(1)
1.4 Before and beyond trees
8(2)
1.5 Probability and Bayesian inference
10(11)
2 Evolutionary trees
21(23)
2.1 Types of trees
21(3)
2.2 Counting trees
24(3)
2.3 The coalescent
27(9)
2.4 Birth-death models
36(4)
2.5 Trees within trees
40(3)
2.6 Exercise
43(1)
3 Substitution and site models
44(14)
3.1 Continuous-time Markov process
45(1)
3.2 DNA models
46(5)
3.3 Codon models
51(1)
3.4 Microsatellite models
52(1)
3.5 Felsenstein's likelihood
52(2)
3.6 Rate variation across sites
54(1)
3.7 Felsenstein's pruning algorithm
55(2)
3.8 Miscellanea
57(1)
4 The molecular clock
58(10)
4.1 Time-trees and evolutionary rates
58(1)
4.2 The molecular clock
58(2)
4.3 Relaxing the molecular clock
60(5)
4.4 Calibrating the molecular clock
65(3)
5 Structured trees and phylogeography
68(9)
5.1 Statistical phylogeography
68(1)
5.2 Multi-type trees
69(2)
5.3 Mugration models
71(1)
5.4 The structured coalescent
71(2)
5.5 Structured birth-death models
73(1)
5.6 Phylogeography in a spatial continuum
73(1)
5.7 Phylodynamics with structured trees
74(2)
5.8 Conclusion
76(1)
Part II Practice 77(90)
6 Bayesian evolutionary analysis by sampling trees
79(18)
6.1 BEAUti
80(6)
6.2 Running BEAST
86(3)
6.3 Analysing the results
89(1)
6.4 Marginal posterior estimates
90(1)
6.5 Obtaining an estimate of the phylogenetic tree
91(3)
6.6 Visualising the tree estimate
94(1)
6.7 Comparing your results to the prior
94(3)
7 Setting up and running a phylogenetic analysis
97(19)
7.1 Preparing alignments
97(3)
7.2 Choosing priors/model set-up
100(12)
7.3 Miscellanea
112(2)
7.4 Running BEAST
114(2)
8 Estimating species trees from multilocus data
116(11)
8.1 Darwin's finches
116(3)
8.2 Bayesian multispecies coalescent model from sequence data
119(1)
8.3 *BEAST
119(4)
8.4 SNAPP
123(4)
9 Advanced analysis
127(12)
9.1 Sampling from the prior
127(1)
9.2 Serially sampled data
128(1)
9.3 Demographic reconstruction
129(5)
9.4 Ancestral reconstruction and phylogeography
134(1)
9.5 Bayesian model comparison
135(3)
9.6 Simulation studies
138(1)
10 Posterior analysis and post-processing
139(15)
10.1 Trace log file interpretation
140(5)
10.2 Model selection
145(3)
10.3 Troubleshooting
148(6)
11 Exploring phylogenetic tree space
154(13)
11.1 Tree space
154(2)
11.2 Methods of exploring tree space
156(1)
11.3 Tree set analysis methods
157(2)
11.4 Summary trees
159(4)
11.5 DensiTree
163(4)
Part III Programming 167(53)
12 Getting started with BEAST 2
169(15)
12.1 A quick tour of BEAST 2
170(3)
12.2 BEAST core: BEAST-objects and inputs
173(1)
12.3 MCMC library
174(6)
12.4 The evolution library
180(2)
12.5 Other bits and pieces
182(1)
12.6 Exercise
183(1)
13 BEAST XML
184(11)
13.1 What is XML?
184(2)
13.2 BEAST file format and the parser processing model
186(4)
13.3 An annotated example
190(3)
13.4 Exercise
193(2)
14 Coding and design patterns
195(12)
14.1 Basic patterns
195(3)
14.2 Input patterns
198(2)
14.3 initAndValidate patterns
200(1)
14.4 Calculat ionNode patterns
201(2)
14.5 Common extensions
203(1)
14.6 Tips
204(1)
14.7 Known ways to get into trouble
205(1)
14.8 Exercise
206(1)
15 Putting it all together
207(13)
15.1 Introduction
207(1)
15.2 What is a package?
208(1)
15.3 BEAUti
209(5)
15.4 Variable selection-based substitution model package example
214(5)
15.5 Exercise
219(1)
References 220(20)
Index of authors 240(4)
Index of subjects 244
Alexei J. Drummond is Professor of Computational Biology and Principal Investigator at the Allan Wilson Centre of Molecular Ecology and Evolution at the University of Auckland, New Zealand. He is the lead author of the BEAST software package and has gained a reputation in the field as one of the most knowledgeable experts for Bayesian evolutionary analyses. Remco R. Bouckaert is a computer scientist with a strong background in Bayesian methods. He is the main architect of version 2 of BEAST and has been working on extensions to the BEAST software and other phylogenetics projects in Alexei Drummond's group at the University of Auckland.
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