Statistical Methods in Molecular Evolution

by Nielsen, Rasmus

• ISBN13: 9780387223339
• ISBN10: 0387223339
• Format: Hardcover
• Copyright: 2005-06-01
• Publisher: Springer Verlag

Summary

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 of 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 R??mer 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. From the reviews: "...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006

Table of Contents

Part I Introduction
Markov Models in Molecular Evolution	3	(22)
Nicolas Galtier
Olivier Gascuel
Alain Jean-Marie
Introduction to Applications of the Likelihood Function in Molecular Evolution	25	(20)
Jutta Buschbom
Arndt von Haeseler
Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution	45	(18)
Bret Larget
Population Genetics of Molecular Evolution	63	(40)
Carlos D. Bustamante
Part II Practical Approaches for Data Analysis
Maximum Likelihood Methods for Detecting Adaptive Protein Evolution	103	(22)
Joseph P. Bielawski
Ziheng Yang
HyPhy: Hypothesis Testing Using Phylogenies	125	(58)
Sergei L. Kosakovsky Pond
Spencer V. Muse
Bayesian Analysis of Molecular Evolution Using MrBayes	183	(50)
John P. Huelsenbeck
Fredrik Ronquist
Estimation of Divergence Times from Molecular Sequence Data	233	(26)
Jeffrey L. Thorne
Hirohisa Kishino
Part III Models of Molecular Evolution
Markov Models of Protein Sequence Evolution	259	(30)
Matthew W. Dimmic
Models of Microsatellite Evolution	289	(18)
Peter Calabrese
Raazesh Sainudiin
Genome Rearrangement	307	(18)
Rick Durrett
Phylogenetic Hidden Markov Models	325	(30)
Adam Siepel
David Haussler
Part IV Inferences on Molecular Evolution
The Evolutionary Causes and Consequences of Base Composition Variation	355	(20)
Gilean A. T. McVean
Statistical Alignment: Recent Progress, New Applications, and Challenges	375	(32)
Gerton Lunter
Alexei J. Drummond
Istvan Miktos
Jotun Hein
Estimating Substitution Matrices	407	(32)
Von Bing Yap
Terry Speed
Posterior Mapping and Posterior Predictive Distributions	439	(24)
Jonathan P. Bollback
Assessing the Uncertainty in Phylogenetic Inference	463	(32)
Hidetoshi Shimodaira
Masami Hasegawa
Index	495