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Book Review

Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data

Andrew D. Johnson, PhD

Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Mass

	Introduction

Top Introduction Disclosures References

 
Michael R. Barnes, ed
554 pages. England, UK: John Wiley & Sons; 2007. 2nd ed. $90.00. Paperback. ISBN 978-0-470-02620-5

This book, now in its second edition for more than a year, is positioned at the intersection of disciplines including genetics, bioinformatics (the melding of computer science and biology), biomedical research, and molecular biology. Over 19 chapters, the authors cover an impressive terrain. The focus is mainly on human genetics and genomics, with research in other species also presented, particularly where it supports and advances our understanding of human genetics. Although a thoughtful discussion of the relevant literature and techniques is found in each chapter, the book is not overly technical and does not present advanced mathematical, statistical, or genetic concepts in great depth. Instead, the focus is on practical applications, available tools, software, and databases, and the presentation of supporting real world research examples. The end result is one of the best available and most accessible texts on bioinformatics and genetics in the postgenome age.

This book is recommended for at least 4 groups of individuals: (1) geneticists, who may gain knowledge of tools to increase the speed, breadth, and depth of their research and help in navigating the vast space of available browsers and databases; (2) bioinformaticians, often trained in specific subdisciplines and sometimes lacking the vocabulary to communicate with geneticists, will gain a working understanding of major issues in genetics and likely discover new bioinformatics tools they were unaware of; (3) molecular scientists, who will gain valuable insight into bioinformatics solutions and databases that may both inspire and make more efficient the design and analysis of new laboratory investigations; and (4) clinicians, who will gain a wider understanding of current approaches in molecular genetics and genomics to better work in interdisciplinary teams aimed at advancing genetics and genomics research into the basis of human disease and related traits.

The book is organized into 5 sections: (1) a brief introductory section; (2) a section providing an overview of genes and genomes and tools related to each; (3) a section focused on genetic study design, analysis, and association; (4) a section dealing with moving from genetic associations to the pursuit of functional and molecular explanations; and (5) a final section addressing genome scale research. This edition of the book arrived near the beginning of a period of near exponential growth in whole genome/genetic association studies hampering the authors’ ability to comment on most of these findings. Thus, the book does not provide the most current resources in this arena. For example, it does not highlight the use of imputation methods and bioinformatics tools like PLINK¹ and SNAP.² Nonetheless, the authors showed foresight in including a chapter titled "Needle in a Haystack? Dealing With 500,000 SNP Genome Scans," which explores many important concepts and approaches using an early GWAS dataset. Despite being a year or more behind current research, this book in its current edition still serves as one of the best resources available, particularly in chapters on noncoding RNAs, pharmacogenetics, and drug discovery, microarrays/gene expression, regulatory polymorphisms, and the potential impacts of amino acid changes.

The writing is clear, with succinct subsections within each chapter. With few exceptions, the chapter material remains focused on the intersection between genetics and bioinformatics, and clinically relevant examples are presented at many points. Although some readers may want to skip the early chapter featuring Perl programming, which serves as an introduction to a computer language widely used in bioinformatics, this chapter also presents an overview of data management principles and strategies for the automation of data-oriented tasks in genetic analysis. In Chapter 1, the editor alludes to the inclusion of an epigenetics section in a planned third edition of the book. If this is true, a number of other areas could be improved. A discussion of ethical, legal, and social issues is completely lacking in the current edition, but this is an important issue in bioinformatics in the age of large datasets, particularly with regard to data sharing and security, research participant identification, and incidental findings. Given the subject matter focus and the rapid movement of these fields, it would be an improvement if updated online content were available in conjunction with a future edition. Finally, the glossary in the current edition could benefit greatly from an expansion.

This review comes from the perspective of someone with years of training and practical experience in both bioinformatics and molecular genetics, now working as a bioinformatics analyst on whole genome association studies. Without reservation, I endorse this text as the best resource I’ve encountered that neatly introduces and summarizes many points I’ve learned through years of experience. The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity. For additional opinions on book content, please see other reviews of the current³ and first editions.^4,5

	Disclosures

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None.

	Footnotes

 
The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.

	References

Top Introduction Disclosures References

 
1. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81: 559–575.[CrossRef][Medline]

2. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. In press.

3. t Hoen PAC. What? Where? Which? WWW resources for geneticists. Eur J Hum Gen. 2007; 15: 1280.[CrossRef]

4. Loytynoja A. Finding genes in the Web. Bioessays. 2004; 26: 1258–1259.[CrossRef]

5. Rocha LM, Rechtsteiner A. Bioinformatics for geneticists. Clin Chem. 2004; 50: 2471–2472.[Free Full Text]

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