F. James Rohlf

General BiologyBiomeasurement: A Student's Guide to Biological Statistics. Second Edition.ByDawnHawkins. Oxford and New York: Oxford University Press.

Bioinformatics: Tools and Applications. Edited by David Edwards, Jason Stajich, and David Hansen. New York: Springer.

49.95 (paper). xxviii + 337 p.; ill.; index. 9780199219995. 2009.

89.95. xii 451 p.; ill.; index. ISBN: 978-0-387-92737-4 (hc); 978-0387-92738-1 (eb). 2009. This edited volume provides a systematic overview of the increasingly mature field of bioinformatics. Its coverage is broad, with 19 chapters carefully organized to span the diversity of the field as traditionally understood, including standard topics such as gene prediction and emergent subjects of importance such as literature mining. Unlike many bioinformatics books that are focused on biomedical applications, the editors appear to have made an effort to avoid such a bias here. Most chapters focus on no particular study organism, while others are self-consciously focused on nonmammalian systems (for example, a pair of chapters on phenomics in plants and microorganisms). Some of the chapters provide foundational background that cuts across specialties, such as an excellent primer on software engineering techniques by John Boyle. A risk inherent to freezing this content within the pages of a book is the rapid evolution of the state of the art. Individual software tools have a relatively short life, and computational techniques used to make sense of data produced with yesterday’s technology often differ from those needed for the data we will face tomorrow. Some chapters do a better job than others at focusing on fundamental principles that are unlikely to go obsolete any time soon; a standout is the chapter on regulatory motif analysis by Moses and Sinha. Still, some of the more time-sensitive reviews are well done, including an excellent one on the prediction of noncoding RNA transcripts by Kavanaugh and Ohler. The authors intend the volume both for biologists and computer scientists who are interested in learning more about the field. Although individual chapters tend toward one audience or the other, overall I think the book will achieve its aim of being a useful resource to both audiences, and would be an excellent choice for an advanced graduate course or reading group. Todd Vision, Biology, University of North Carolina, Chapel Hill, North Carolina Biomeasurement: A Student’s Guide to Biological Statistics. Second Edition. By Dawn Hawkins. Oxford and New York: Oxford University Press.

Basic Microcomputing and Biostatistics. How to Program and Use Your Microcomputer for Data Analysis in the Physical and Life Sciences, Including Medicine, With a Permanent Library of Many Helpful Programs, Challenging Problems, and Worked Exercises.Donald W. Rogers

49.95 (paper). xxviii 337 p.; ill.; index. ISBN: 978-0-19-921999-5. 2009. This is a very user-friendly introduction to statistical methods for first-year undergraduate biology students. As the author points out, it has a relaxed style. It is based on the philosophy that “biologists don’t need to understand the mathematical principles behind statistical equations to be good scientists. They really need faith in the mathematicians who created the statistical formulae” (p. viii). That may be a good strategy at the elementary level, but at some point scientists need to become much more skeptical about exactly what methods do and under what conditions they are valid. For each major method discussed, the book presents the logical steps, a series of self-help questions in a convenient checklist of key points covered in the chapter. There are also worked examples done by hand, and by using SPSS 16.0. A website that gives additional information and example data in various file formats is also included. A set of standard statistical tables are provided in an appendix. A useful feature is a “Literature link” to published studies that are examples of particular methods. I was surprised to see an advanced topic such as General Linear Model (GLM) included in an elementary textbook. The descriptions of most methods are quite accurate. However, the chapter on regression does not make the proper distinction between model I and model II and, unfortunately, uses a data set more appropriate for model II. F. James Rohlf, Ecology & Evolution, Stony Brook University, Stony Brook, New York

Book Review:Computer Programming in Quantitative Biology. R. G. Davies

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