The analysis of vast amounts of biological data has the potential to significantly impact and aid further development of bioengineering processes that can be used for medical purposes such as the treatment of diseases. Traditionally most bioinformatics algorithms are sequential, but because of the size of the data involved, even linear time algorithms are sometimes computationally infeasible or require substantial computational resources. So the natural question arises whether in some cases algorithms that are sequentially infeasible can be made tractable by converting them to parallel or distributed algorithms. This excellent new book focuses on such problems and their possible distributed solutions.

Each chapter here follows the same layout. They begin with a general problem statement followed by a description of the available sequential algorithms. The author then moves on to parallel/distributed algorithms for the same problem or, if none are yet available, provides ideas on what such algorithms may look like. Available software packages are also described.

The book focuses on bioinformatics problems that require substantial computational power due to data size requirements and are hence good candidates for parallelization, particularly in Part 2, “Biological Sequences,” and in Part 3, “Biological Networks.” Part 1 provides the reader with background information. The three parts are subdivided further: Part 1 first goes into an introduction to molecular biology followed by a discussion of graphs, algorithms, and complexity and finally a chapter on parallel and distributed computing. Part 2 begins with string algorithms and then moves on to sequence alignment, followed by clustering of biological sequences, sequence repeats, and genome analysis. Part 3 presents the analysis of biological networks, followed by cluster discovery in biological networks, network motif search, network alignment, phylogenetics, and an epilogue.

Because of its clear layout and well-thought-out design, this book is suitable for students and researchers in computer science as well as in biology. It is my sincere hope that it will introduce new groups of researchers to these very important problems and motivate them to look for new and more efficient solutions to them. This book is an excellent resource for anyone with an interest in bioinformatics.