Principles Of Computational Genomics
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Principles of Computational Genomics
The advent of high-throughput experimental assays, and in particular of next-generation sequencing, has revolutionized life sciences by enabling the generation of data at the scale of the whole genome. Extracting biologically useful or clinically actionable information from this data requires analytical methods quite different from the ones used to analyze low-throughput experimental results. The development of these methods is the goal of computational biology. Understanding the principles on which these methods are based is thus necessary for all students and researchers in life sciences. This book provides the conceptual framework needed to understand computational genomics enough to be able to follow the arguments in recent papers, or to collaborate with computational scientists in research projects. In particular, it introduces the mathematical and statistical basis of the methods in some depth. The main focus is on the analysis of next-generation-sequencing assays, both for the interpretation of the DNA sequence per se (sequence alignment, phylogenetic tree reconstruction, genetic variants) and for the study of gene regulation and epigenomics (gene expression, transcription factor binding, chromatin states, 3D structure of the genome). The final chapter discusses the associations of genetic variants with phenotypes and diseases, and their biological interpretation. Principles of Computational Genomics provides a solid foundation for understanding the many parts of computational genomics, including those not treated directly in the book. It will be of great benefit to students and researchers across the life sciences.
Computational Genome Analysis
Author: Richard C. Deonier
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-12-27
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.
Computing for Biologists
Author: Ran Libeskind-Hadas
language: en
Publisher: Cambridge University Press
Release Date: 2014-09-22
Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by numerous programming exercises, available at www.cs.hmc.edu/CFB.