Computational biology is a thriving discipline with an ever-increasing body of so-called “-omics” data to be explored. However, as highlighted by the editors in the first chapter of this book, there is also a plethora of software tools and websites that have been developed to access the data; the problem is that a lot of this software is not maintained, having been mostly developed as part of small, contained research projects. We all know that academic careers depend on the cycle of research grants and publications, with software being often just one of the incidental products. The authors call the distance between the actual analysis that possible users need to do and the collection of tools described in the literature as the “bioinformatics analysis gap.” This volume attempts to bridge this gap.

Chapter 2 is an excellent and accessible overview of gene regulation. Many books aimed at non-biologists stop at the explanation of the so-called central dogma of molecular biology, namely that DNA sequences are transcribed into RNA sequences and then translated into amino acid sequences. Here, the authors explain important details such as the structure of chromatin and the effect of nucleosomes. Complex control mechanisms are explained, with a minimum of extraneous detail.

The first two background chapters are followed by contributions in which many software tools are introduced. The first family of tools covered includes bio-databases and data mining software; the various contributors have wisely focused on general ideas and issues such as data formats, standards, and heterogeneity. Many tools are introduced, though some of them (such as Ondex and PubMed) are covered in depth in individual chapters.

The second theme that is expanded is the network-related aspect of biological data, including visualization tools and analysis tools. Some of the visualization tools are simple adaptations of general graph manipulation approaches, but these can be very helpful in improving access to “-omics” data and are a clear and important contribution to research in the life sciences. The analysis tools, on the other hand, expand on computation approaches such as Bayesian networks and especially Petri nets, the topic of an individual chapter. The vexing issue of sharing data between different modeling approaches is addressed, with an overview of different exchange formats and a listing of different types of software that access data in these different formats. Several tools are covered in a page or so of text, which is enough to give a brief idea of how each one fits in the overall tool landscape; there are enough links to web resources to allow users to follow up on this introduction and obtain software when possible.

Finally, a few chapters are dedicated to very current issues and difficulties in bringing these large representations of cellular functions together. One such issue is the localization of metabolites and pathways within cells. Another is linking genetic and metabolic data with phenomic information. The word “compution” (which I confess I didn’t know was even a correct word until now) is used to refer to the use of computation to discover new insights from the data processing paradigm, something that is very current with the increased global interest in data analytics and big data.

The emphasis throughout the book is very practical: instead of explaining fundamental concepts in excruciating, formal detail, all of the contributors start by alluding to the issues in a high-level discussion and then introduce relevant software and the usage of software through examples. This is probably the best way of giving a bird’s-eye view of the vast and complex landscape of tools in computational biology. The extensive bibliographies and lists of Internet sites complement this view with enough pointers for further study.

Overall, this very well-edited and coherent book can be useful both to data scientists who would like to know how their discipline can be applied to a rich domain, and also to biologists who are getting more and more convinced of the need to access sophisticated software tools to take their research forward.