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Statistical Methods in Molecular Evolution (Paperback, Softcover reprint of hardcover 1st ed. 2005)
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Statistical Methods in Molecular Evolution (Paperback, Softcover reprint of hardcover 1st ed. 2005)
Series: Statistics for Biology and Health
Expected to ship within 18 - 22 working days
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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 Romer 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 "Who should read this book?
We suggest that anyone who deals with molecular data (who does
not?) and anyone who asks evolutionary questions (who should not?)
ought to consult the relevant chapters in this book." Dan Graur and
Dror Berel for Biometrics, September 2006 "Coalescence theory
facilitates the merger of population genetics theory with
phylogenetic approaches, but still, there are mostly two camps:
phylogeneticists and population geneticists. Only a few people are
moving freely between them. Rasmus Nielsen is certainly one of
these researchers, and his work so far has merged many population
genetic and phylogenetic aspects of biological research under the
umbrella of molecular evolution. Although Nielsen did not
contribute a chapter to his book, his work permeates all its
chapters. This book gives an overview of his interests and current
achievements in molecular evolution. In short, this book should be
on your bookshelf." Peter Beerli for Evolution, 60(2), 2006
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