Simon Tavaré

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 From the reviews: "The book is useful for its breadth. An impressive variety of topics are surveyed...." Short Book Reviews of the ISI, June 2006 "It is a very good book indeed and I would strongly recommend it both to the student hoping to take this study further and to the general reader who wants to know what computational genome analysis is all about." Mark Bloom for the JRSS, Series A, Volume 169, p. 1006, October 2006 "Richard C. Deonier, Simon Tavare and Michael S. Waterman provide us wtih a 'roll up your sleeves and get dirty' (as the authors phrase it in their preface) introduction to the field of computational genome analysis...The book is carefully written and carefully edited..." Ralf Schmid for Genetic Research, Volume 87, p. 218, 2006

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 From the reviews: "The book is useful for its breadth. An impressive variety of topics are surveyed...." Short Book Reviews of the ISI, June 2006 "It is a very good book indeed and I would strongly recommend it both to the student hoping to take this study further and to the general reader who wants to know what computational genome analysis is all about." Mark Bloom for the JRSS, Series A, Volume 169, p. 1006, October 2006 "Richard C. Deonier, Simon Tavare and Michael S. Waterman provide us wtih a 'roll up your sleeves and get dirty' (as the authors phrase it in their preface) introduction to the field of computational genome analysis...The book is carefully written and carefully edited..." Ralf Schmid for Genetic Research, Volume 87, p. 218, 2006

This volume contains lectures given at the 31st Probability Summer School in Saint-Flour (July 8-25, 2001). Simon Tavaré’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.