Computational Genome Analysis

Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field. This book features: Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation Presentation of fundamentals of probability, statistics, and algorithms Implementation of computational methods with numerous examples based upon the R statistics package Extensive descriptions and explanations to complement the analytical development More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature Exercises at the end of chapters Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method. Simon...