This book describes itself as “designed for biologists,” but in fact it is also ideal for computer scientists who want to learn about practical applications of computing in biology. Books and collections that attempt to build bridges between biology and computing frequently get lost in jargon and justification. Biology is, arguably, a science where the details of particular case studies are crucial. In this book, however, the author is able to bring out very challenging problems, without losing the readers with too many concrete details. The book strikes an excellent balance and covers a large field without losing focus.
The manifest goal of the book is to introduce readers to ways of deploying simple programs in Perl or Python to aid research in the broad area of molecular biology. The given programs are explained very clearly and illustrate how simple code written without making use of specialized libraries or tools can be very useful. This is an achievement in itself, and hopefully members of the intended target community of biologists will be encouraged to try their hand at examining, running, and adapting the programs. The book shows Perl code, but Python versions of the programs are available on the web. Both of these scripting languages have already proved their mettle as powerful tools for biologists. The appendices provide short introductions to Perl, Unix, and several packages that are often used in bioinformatics research. The weak point may be the expectation that life scientists will quickly adapt to using Unix or Linux. The author should have clearly explained that Perl is easily available for both Mac OS X and Windows platforms as well.
However, the book does more than introduce computing to life scientists. It also provides an excellent overview of the many exciting and challenging research directions in molecular biology, and explains how a computational approach is an integral and important aspect of this active field.
Introductory texts often explain the process of gene expression in the simple terms of the so-called central dogma. This rule says that genes are transcribed into ribonucleic acid (RNA) and then translated into sequences of amino acids, which then take 3D shapes that underpin their biological function. This abstraction serves as a good foundation for understanding the relevance of string searching and matching algorithms, which have been crucial in advancing the field of bioinformatics. However, there is much more complexity in the processes that control gene expression, and in the first few chapters, the author introduces some of these factors, such as the role of small RNAs. He also shows through examples how genes can be manipulated in the lab with the help of information uncovered through simple algorithms.
The later parts of the book present interesting and topical ramifications and applications of gene technologies, ranging from human diseases (iron imbalances and cancer) to understanding evolution and comparative genomics. The author does an excellent job of keeping the text accessible and compelling with fascinating examples, such as the extinction of the Tasmanian tiger, a murder mystery, and the role of hemophilia in European history. The coverage concludes with an exploration of personal genome information, which makes advances such as personalized medicine possible, due to the cost-effective methodologies now available.
Overall, the book provides a lively and accessible introduction to current research in the life sciences, and it does so in a succinct way by grounding the explanations with simple algorithms expressed in Perl code. As such, the book can be very useful to a general science audience, particularly those with a computer science background, whether established researchers or undergraduate students. The writing is inspiring and engaging, and the inclusion of Perl code makes it easy for readers to apply the knowledge and observe the outcomes.
