Statistical Analysis of Molecular and Genomic Evolution

Abstract This book introduces up-to-date methods in statistics and bioinformatics for the study of molecular and genome evolution. It first provides a concise overview of molecular evolutionary analysis and phylogenetic inference. The following chapters cover four research themes: evolution of protein functionality and functional divergence (Chapters 3 and 4); effective gene pleiotropy estimation under the genotype-phenotype mapping model of protein evolution (Chapter 5); evolution of genetic robustness after gene duplication (Chapter 6), and the statistical models of transcriptome evolution (Chapter 7 for phylogenetic transcriptome analysis, Chapter 8 for ancestral transcriptome inference along a phylogeny, and Chapter 9 for Bayesian estimation of selection strength imposed on transcriptome evolution). The book focuses on how the underlying evolutionary mechanisms can be reasonably modeled so that they can be statistically tested by the current high throughput data. Meanwhile, the book avoids the cumbersome description of technical procedures for specific data types such as normalization or bias correction. The author believes that this book will help new-generation researchers to advance this research field as more data of much higher quality are available.