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Usda-Ars-Npa-Gmprc-Pseru Manhattan, Ks 66506
(gbai{at}agron.ksu.edu)
Dhammika Amaratunga and Javier Cabrera, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030. 2003. 246 p. $49.95 hardcover. ISBN 0-471-27398-8.
As high-throughput DNA sequencing technology is developed, whole genome DNA sequence information is available for more and more species. Molecular biologists are now confronted with a huge number of newly identified genes that await discovery of their functions. Microarray technology makes it possible for molecular biologists to analyze simultaneously thousands of DNA and protein samples and monitor their behavior patterns, which brings about a tremendous improvement over the tedious "one gene per experiment" paradigm that prevailed previously. To make sense of mountains of data generated by microarray technology, bioinformatics has emerged as a new discipline to become an integral part of genomic research. In this book, the authors effectively outline the different phases of microarray experiments and various techniques for microarray data analysis, and provide a comprehensive and up-to-date overview of this important field.
This book covers the essentials of what a researcher who does microarray analysis needs to know about (i) experimental design; (ii) major steps in a microarray experiment; (iii) computational, visual, and statistical tools for data analysis; and (iv) data normalization, summarization, and interpretation. This book describes all the critical procedures of a microarray experiment from planning to final data analysis and interpretation. The first part of the book briefly reviews general information on fundamental molecular biology and procedures for a typical microarray experiment and data collection. The authors do a good job of providing important references on genomics and microarray research that may guide the readers into more detailed readings on these topics. The second part of the book, which represents about 60 percent of the text, describes various techniques for analyzing microarray data. Protein arrays are briefly discussed in the last chapter.
The book is extremely well written, and I would recommend its reading by graduate students dealing with microarrays as they begin to develop their research projects and by molecular geneticists in the functional genomics area to help them learn the procedures of microarray data analysis. This book is an excellent textbook for graduate students majoring in bioinformatics. In each chapter, the authors provide in-depth readers a source of additional information. Chapter exercises are carefully constructed by the authors and will help readers to understand further the theories and principles and to practice their applications to hypothetical data. It is also a good book for undergraduate students or other biologists who want to learn basic knowledge about microarray analysis. The emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with knowledge of undergraduate-level statistics and biology.
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