This book 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 features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.

This book is devoted to the collection, interpretation and analysis of population genetic data. Among the topics included here are studies on human evolutionary history, molecular techniques for generating data, statistical and computational techniques for the interpretation of such data, and stochastic models for genealogy and population structure. The chapters reflect the close interaction between experimental molecular biologists and theoreticians. The book will be useful for specialists in the area, as well as mathematicians, statisticians, computer scientists and biologists wanting a brief overview of current problems in the field.

This book is devoted to the collection, interpretation and analysis of population genetic data. Among the topics included here are studies on human evolutionary history, molecular techniques for generating data, statistical and computational techniques for the interpretation of such data, and stochastic models for genealogy and population structure. The chapters reflect the close interaction between experimental molecular biologists and theoreticians. The book will be useful for specialists in the area, as well as mathematicians, statisticians, computer scientists and biologists wanting a brief overview of current problems in the field.

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 Tavare holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavare's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavare is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels. "

This volume contains lectures given at the 31st Probability Summer School in Saint-Flour (July 8-25, 2001). Simon Tavare's lectures serve as an introduction to the coalescent, and to inference for ancestral processes in population genetics. The stochastic computation methods described include rejection methods, importance sampling, Markov chain Monte Carlo, and approximate Bayesian methods. Ofer Zeitouni's course on "Random Walks in Random Environment" presents systematically the tools that have been introduced to study the model. A fairly complete description of available results in dimension 1 is given. For higher dimension, the basic techniques and a discussion of some of the available results are provided. The contribution also includes an updated annotated bibliography and suggestions for further reading. Olivier Catoni's course appears separately."