With the first draft of the human genome project in the publicdomain and full analyses of model genomes now available, thesubject matter of 'Principles of Genome Analysis and Genomics' iseven 'hotter' now than when the first two editions were publishedin 1995 and 1998. In the new edition of this very practical guideto the different techniques and theory behind genomes and genomeanalysis, Sandy Primrose and new author Richard Twyman provide afresh look at this topic. In the light of recent excitingadvancements in the field, the authors have completely revised andrewritten many parts of the new edition with the addition of fivenew chapters. Aimed at upper level students, it is essential thatin this extremely fast moving topic area the text is up to date andrelevant. Completely revised new edition of an establishedtextbook. Features new chapters and examples from exciting new researchin genomics, including the human genome project. Excellent new co-author in Richard Twyman, also co-author ofthe new edition of hugely popular Principles of GeneManipulation. Accompanying web-page to help students deal with this difficulttopic at www.blackwellpublishing.com/primrose
Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.
This comprehensive reference covers the comparative methodologyinvolved in studying molecular evolution. Providing a practicalintroduction to the role of bioinformatics in comparative genomics,this publication further discusses the basic technology used ingenome sequencing projects and provides an overview of genomestorage databases currently in use. This timely and cutting-edge text also: Reviews the basic principles of genomics and gene expressionanalysis Discusses analytic methods in proteomics andtranscriptomics Includes a comprehensive list of Web resource
There is growing enthusiasm in the scientific community about the prospect of mapping and sequencing the human genome, a monumental project that will have far-reaching consequences for medicine, biology, technology, and other fields. But how will such an effort be organized and funded? How will we develop the new technologies that are needed? What new legal, social, and ethical questions will be raised? Mapping and Sequencing the Human Genome is a blueprint for this proposed project. The authors offer a highly readable explanation of the technical aspects of genetic mapping and sequencing, and they recommend specific interim and long-range research goals, organizational strategies, and funding levels. They also outline some of the legal and social questions that might arise and urge their early consideration by policymakers.
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach. Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data. Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
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.
A unique exploration of the principles and methods underlying the Human Genome Project and modern molecular genetics and biotechnology-from two top researchers In Genomics, Charles R. Cantor, former director of the Human Genome Project, and Cassandra L. Smith give the first integral overview of the strategies and technologies behind the Human Genome Project and the field of molecular genetics and biotechnology. Written with a range of readers in mind-from chemists and biologists to computer scientists and engineers-the book begins with a review of the basic properties of DNA and the chromosomes that package it in cells. The authors describe the three main techniques used in DNA analysis-hybridization, polymerase chain reaction, and electrophoresis-and present a complete exploration of DNA mapping in its many different forms. By explaining both the theoretical principles and practical foundations of modern molecular genetics to a wide audience, the book brings the scientific community closer to the ultimate goal of understanding the biological function of DNA. Genomics features: * Topical organization within chapters for easy reference * A discussion of the developing methods of sequencing, such as sequencing by hybridization (SBH) in which data is read through words instead of letters * Detailed explanations and critical evaluations of the many different types of DNA maps that can be generated-including cytogenic and restriction maps as well as interspecies cell hybrids * Informed predictions for the future of DNA sequencing
Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website.
Bioinformatics derives knowledge from computer analysis of biological data. In particular, genomic and transcriptomic datasets are processed, analysed and, whenever possible, associated with experimental results from various sources, to draw structural, organizational, and functional information relevant to biology. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics in Aquaculture provides the most up to date reviews of next generation sequencing technologies, their applications in aquaculture, and principles and methodologies for the analysis of genomic and transcriptomic large datasets using bioinformatic methods, algorithm, and databases. The book is unique in providing guidance for the best software packages suitable for various analysis, providing detailed examples of using bioinformatic software and command lines in the context of real world experiments. This book is a vital tool for all those working in genomics, molecular biology, biochemistry and genetics related to aquaculture, and computational and biological sciences.
