Statistical Methods in Molecular Evolution

  • Rasmus Nielsen

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages II-XII
  2. Introduction

    1. Nicolas Galtier, Olivier Gascuel, Alain Jean-Marie
      Pages 3-24
    2. Carlos D. Bustamante
      Pages 63-99
  3. Practical Approaches for Data Analysis

    1. Joseph P. Bielawski, Ziheng Yang
      Pages 103-124
    2. Sergei L. Kosakovsky Pond, Spencer V. Muse
      Pages 125-181
    3. John P. Huelsenbeck, Fredrik Ronquist
      Pages 183-226
    4. Jeffrey L. Thorne, Hirohisa Kishino
      Pages 233-256
  4. Models of Molecular Evolution

    1. Matthew W. Dimmic
      Pages 259-287
    2. Peter Calabrese, Raazesh Sainudiin
      Pages 290-305
    3. Rick Durrett
      Pages 307-323
    4. Adam Siepel, David Haussler
      Pages 325-351
  5. Inferences on Molecular Evolution

    1. Gerton Lunter, Alexei J. Drummond, István Miklós, Jotun Hein
      Pages 375-405
    2. Von Bing Yap, Terry Speed
      Pages 407-438
    3. Hidetoshi Shimodaira, Masami Hasegawa
      Pages 463-493
  6. Back Matter
    Pages 495-504

About this book

Introduction

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.

Keywords

Likelihood Markov chain Phylogenie Probability theory bioinformatics biology data analysis evolution genetics genome genomics hidden markov model molecular evolution protein

Authors and affiliations

  • Rasmus Nielsen
    • 1
  1. 1.Dept. of Biological Sciences and Computational BiologyCornell UniversityIthacaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/0-387-27733-1
  • Copyright Information Springer Science+Business Media, Inc. 2005
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-22333-9
  • Online ISBN 978-0-387-27733-2
  • Series Print ISSN 1431-8776
  • About this book