Statistical methods in molecular evolution

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January 17, 2025 | History

Statistical methods in molecular evolution

In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice.

The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D.

form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book.

Publish Date
Publisher
Springer
Language
English
Pages
504

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Edition Availability
Cover of: Statistical Methods in Molecular Evolution
Statistical Methods in Molecular Evolution
2006, Springer London, Limited
in English
Cover of: Statistical methods in molecular evolution
Statistical methods in molecular evolution
2005, Springer
in English

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Book Details


Table of Contents

Markov models in molecular evolution / Nicolas Galtier, Olivier Gascuel, and Alain Jean-Marie
Introduction to applications of the likelihood function in molecular evolution / Jutta Buschbom and Arndt von Haeseler
Introduction to Markov chain Monte Carlo methods in molecular evolution / Bret Larget
Population genetics of molecular evolution / Carlos D. Bustamante
Maximum likelilhood methods for detecting adaptive protein evolution / Joseph P. Bielawski and Ziheng Yang
HyPhy: hypothesis testing using phylogenies / Sergei L. Kosakovsky Pond and Spencer V. Muse
Bayesian analysis of molecular evolution using MrBayes / John P. Huelsenbeck and Fredrik Ronquist
Estimation of divergence times from molecular sequence data / Jeffrey L. Thorne and Hirohisa Kishino
Markov models of protein sequence evolution / Matthew W. Dimmic
Models of microsatellite evolution / Peter Calabrese and Raazesh Sainudiin
Genome rearrangement / Rick Durrett
Phylogenetic hidden Markov models / Adam Siepel and David Haussler
The evolutionary causes and consequences of base composition variation / Gilean A.T. McVean
Statistical alignment: recent progress, new applications, and challenges / Gerton Lunter ... [et al.]
Estimating substitution matrices / Von Bing Yap and Terry Speed
Posterior mapping and posterior predictive distributions / Jonathan P. Bollback
Assessing the uncertainty in phylogenetic inference / Hidetoshi Shimodaira and Masami Hasegawa.

Edition Notes

Includes bibliographical references and index.

Published in
New York
Series
Statistics for biology and health

Classifications

Dewey Decimal Class
572.838
Library of Congress
QH371.3.M37 S73 2005, QH359-425

The Physical Object

Pagination
xii, 504 p. :
Number of pages
504

Edition Identifiers

Open Library
OL3435487M
Internet Archive
statisticalmetho00niel
ISBN 10
0387223339
LCCN
2005278438
OCLC/WorldCat
60496853
LibraryThing
4562773
Goodreads
1149206

Work Identifiers

Work ID
OL18236990W

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January 17, 2025 Edited by MARC Bot import existing book
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February 25, 2022 Edited by ImportBot import existing book
April 1, 2008 Created by an anonymous user Imported from Scriblio MARC record