The book has been used as the main textbook in a few graduate courses. If you are an instructor and would like to have a copy of the slides, please send me an email. I have powerpoint slides for some of the chapters.
Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology.
Readership: An advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, mathematics and statistics.
Contents (Extensive table of sontents)
Preface
1. Models of Nucleotide Substitution
2. Models of Amino Acid and Codon Substitution
3. Phylogeny Reconstruction: Overview
4. Maximum Likelihood Methods
5. Bayesian Methods
6. Comparison of Methods and Tests on Trees
7. Molecular Clock and Estimation of Species Divergence Times
8. Neutral and Adaptive Protein Evolution
9. Simulating Molecular Evolution
10. Perspectives
Appendixes
Reference