The discovery of proteins and DNA as fundamental building blocks of life and their subsequent sequencing, or breaking into component parts for analysis, stimulated much research to bring computational techniques into biology. As this area of study has grown, textbooks such as this one are appearing for undergraduate or graduate courses in bioinformatics and computational biology.

Bioinformatics seeks to analyze DNA and proteins from many sources to identify structures such as genes, detect evolutionary patterns, and determine genetic similarities or differences within and across species. A number of databases of sequence data are publicly available from various sources. A major issue in biological analysis is the massive volume of data and computational complexity in performing such analyses. Researchers must use simplifying models and statistical methods to make computations manageable.

This book is an introduction to the use of computational techniques to analyze biological data. It is divided into four parts. Part 1 defines the fields of bioinformatics and molecular biology. This is followed by overviews of biological databases. Here, and subsequently, the text discusses various algorithms that were developed to analyze biological data, and then notes how they are implemented and used in MATLAB, a commercial data analysis product that includes interfaces to various online databases and a toolbox of routines for working with biological data. Examples of analysis using MATLAB are used throughout the rest of the book. Part 2 covers data retrieval from biological databases and techniques for comparing sequences, called “alignment.” Sequences typically have differences. The computational problem is finding efficient ways to measure how similar two or more sequences are to each other. Several algorithms, such as Needleman-Wunsch or Smith-Waterman, are described along with how they are invoked using MATLAB. Part 3 introduces sequence analysis using various statistical and probabilistic modeling methods such as Markov models, hidden Markov models, quadratic discriminant analysis, and so on, and a number of specific gene models. Part 4 considers phylogenetics and systems biology. This is the study of evolutionary relationships in organisms, for example, constructing a descendant tree that shows species divergence over time. Various methods of analysis, such as distance-based, parsimony, and maximum likelihood, are discussed. This part finishes with a description of microarrays, also called gene chips, devices containing probes that measure changes in gene expression. Appendices provide details on MATLAB, which is referenced throughout the text, and BioPerl, an open-source Perl library of bioinformatic routines referenced in two places.

This is a high-level overview of a number of topic areas. Each topic is presented at a high level without a lot of detail, but with references given at the end of each chapter for those wanting more information. The intended audience includes “students in computer science, engineering, and information technology at the undergraduate or lower graduate level.” Knowledge in a number of disciplines would be helpful to the reader, particularly string manipulation and regular expressions, statistical analysis, and elementary calculus. Some background in microbiology would also be helpful. Biological databases are treated as applications, thus database theory is not needed here.

The book is logically organized, understandable, and clearly written, although especially in the early chapters (as I’ve seen in other books published by Springer) the proofing of English is poorly done, resulting in many sentences with incorrect use or wrong words. We find, for example, sentences with phrases such as “Each pair of chromosome” or “encoded in on 23 chromosomes.” That small complaint aside, the book offers a competent overview of the core topics of bioinformatics.