Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modeling and bioinformatics, and the procedure of phylogeny-based analysis.

Statistical Theory and Methods for Evolutionary Genomics summarizes the statistical framework of evolutionary genomics, and illustrates how statistical modeling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the author includes extensive discussion on how new types of functional genomic data (e.g. microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.

About the Author

Xun Gu obtained his Ph.D from the University of Texas in 1996 and is now Professor in the Department of Genetics, Development and Cell Biology at Iowa State University. His research has been focused on statistical and computational methods for understanding genome complexity and evolution, and high throughput comparative genomics analyses and applications. In his research career, Dr. Gu has published over 100 papers in peer-reviewed scientific journals, and he was the 2001 recipient of the Dupont Young Professor Award. In addition, Dr. Gu has served as the associate editor, guest editor, and member of editorial board in a number of scientific journals.

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