Computational Molecular Evolution

This book explores essential models, methods, and algorithms for analyzing molecular sequence data, addressing the challenges of large genomic datasets and enhancing our understanding of gene and genome evolution.

Computational Molecular Evolution offers an in-depth exploration of the models, methods, and algorithms essential for analyzing the growing body of molecular sequence data. As the field of molecular evolution has rapidly advanced, fueled by the influx of genetic sequence data and advancements in computational technologies, this book addresses the challenges and opportunities presented by these developments. The author, Yang, provides insights into how sophisticated analytical techniques can enhance our understanding of gene and genome evolution.

The text emphasizes modern statistical approaches, including maximum likelihood and Bayesian statistics, which are crucial for interpreting large genomic datasets. Rather than focusing on complex mathematical proofs, the book prioritizes essential concepts, making it accessible to a wide audience. It includes detailed derivations and practical implementation guidance, supplemented by illustrations, worked examples, and exercises that reinforce the material presented.

Targeted at both students and professional researchers across various fields—such as molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics, and computer science—this book serves as a valuable resource. Biologists who have utilized phylogenetic software to analyze their data will find it particularly beneficial. Overall, Computational Molecular Evolution stands as an authoritative guide for anyone interested in the dynamic and evolving field of computational biology.