Ziheng Yang, 2014, Oxford University Press
Yang has produced a book that could be read and enjoyed by different
audiences for different purposes. I think that the book will be
welcomed by biologists who want some mathematical intuition for what's
underneath the hood of the methods they rely on. Yang has made a
serious effort to keep the book understandable to non-mathematicians,
and as a result the book does not feel overly mathematical or dense
with notation despite the fact that it covers a lot of mathematical
I think Molecular Evolution: A Statistical Approach would also work very well as a text for a graduate level course in statistical phylogenetics. It is hard to think of a topic better than phylogenetics to provide a broad tour of mathematics and statistics...
Barbara R. Holland, in Systematic Biology, 64:545-546 (2015)
David Hillis, in The Quarterly Review of Biology, 90:89-90 (2015)
Please read the README files if they exist.
If I can find the time, I will try to develop a web page for the problems and programs in the book.
If you use the book for teaching and want the slides, please write to me. I may have some powerpoint slides based on figures in the book.
Molecular Evolution: a statistical approach presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. Written by an expert in the field, the book emphasizes conceptual understanding rather than mathematical proofs. The text is enlivened with numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative.
This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.
Readership: Graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, mathematics and statistics.
Table of Contents (Extended Table of Contents).
1: Models of nucleotide substitution
2: Models of amino acid and codon substitution
3: Phylogeny reconstruction: overview
4: Maximum likelihood methods
5: Comparison of phylogenetic methods and tests on trees
6: Bayesian theory
7: Bayesian computation (MCMC)
8: Bayesian phylogenetics
9: Coalescent theory and species trees
10: Molecular clock and estimation of species divergence times
11: Neutral and adaptive protein evolution
12: Simulating molecular evolution