Gene finding is the process of identifying the regions of genomic DNA that encode genes. This is a notoriously difficult task and researchers have developed several different approaches to handle these difficulties. This book focuses on “comparative methods [that] use two or more evolutionary-related sequences [and compare them] to identify novel genes” and to identify the function of genes and noncoding parts of the genome. This second edition is identical to the first edition, with an additional chapter on next-generation sequencing (NGS) technology that provides a brief description of these techniques. Chapter 2 has also been “extended to include a section on conditional random fields.”

I am very impressed by this book. It skillfully introduces readers to a difficult subject, while at the same time motivating them to enter this very important area. To fully appreciate it, it will be helpful to have some background in mathematical statistics and bioinformatics. It is best suited for a graduate course or as an introduction for researchers not familiar with this field.

The book begins in chapter 1 with an introduction to genetics and familiarization with the necessary biological terms. Chapter 1 also introduces the gene-finding problem. Chapter 2 discusses the most common algorithms used for single-species gene finding. The author then connects each of the algorithms and models to “existing gene-finding software that uses the [corresponding] model.” Chapter 3 continues with the problem of sequence alignment, first describing different scoring schemes, dynamic programming, and heuristic database searches, and then the “most common approaches to multiple sequence alignment.” Chapter 4 introduces the main topic of this book: comparative gene finding by combining the strengths of the methods first introduced in chapters 2 and 3. Chapter 5 presents the “gene features most commonly captured by a computational gene model.” Chapter 6 discusses parameter training and covers “different parameter estimation and optimization techniques.” Chapter 7 discusses the “implementation of a comparative gene finder” with the example of SLAM. The book concludes in chapter 8 with a brief description of NGS techniques, in particular annotation pipelines. Each chapter consists of a theoretical part followed by examples and applications in an existing gene finder.

Overall, this is an excellent introduction to comparative gene finding. Readers who already have the first edition, however, should not expect any significant updates. The last chapter on NGS techniques is relatively brief and does not exhibit the more in-depth exploration of the preceding chapters. I especially recommend this book to any computer scientist with an interest in current problems in bioinformatics.