Statistics in the 21st Century.

The book  nishes with chapters on evolutionary models and phylogenetic tree estimation. The discussion of evolutionary models is concise and informative and nicely summarizes the relationships among the various types of models. This discussion is well integrated into the subsequent chapter on phylogenetic tree estimation. This chapter gives a very readable and intuitive description of basic tree-building methods (UPGMA, neighbor-joining, parsimony, and maximum likelihood). It goes on to give data examples of these four methods, and follows with a careful discussion of how the results compare. The result is a clear and concise overview of phylogenetic tree construction that would be useful reading for anyone with interest in this area. Currently, bioinformatics books are proliferating very rapidly. However, this book is the only one that I am aware of that focuses on the probabilistic aspects at an introductory level. Overall, this book is less comprehensive than many currently available texts (e.g., Mount 2001; Baxevanis and Oullette 2001). However, its explanations of selected topics have much more depth. These topics are well chosen to give a sense of the problems and approaches used in bioinformatics. Unlike the aforementioned texts books, this book has little discussion of available bioinformatics tools or the current state of the art. It also has little on algorithmic aspects of bioinformatics. This area is also covered in a number of recent books (e.g., Waterman 1995; Pevzner 2000). The only book that I am aware of that provides competition in the same niche as this book is the text by Durbin et al. (1998). This latter book covers the probabilistic basis of many bioinformatics problems in a very readable fashion; however, its emphasis is more on algorithmic than on statistical aspects. The choice between these books is primarily a matter of instructor taste. In summary, I would say (with some caveats) that Statistical Methods in Bioinformatics: An Introduction successfully  lls the gap that it was intended to  ll. The highlights of this book are the chapters that actually cover bioinformatics. These are well written and at a good level for an introductory course. These chapters would also make good reading for statisticians looking for an easy introduction to bioinformatics concepts. Nonetheless, the book falls short in the chapters on basic statistics and stochastic processes. The amount of material is too great to cover in one book or one two-semester course. I would suggest that a bioinformatics course based on this book have an introductory mathematical statistics course as a prerequisite. Without the need to cover this material, there would be time to cover the stochastic process material adequately. Equally important, students will have had a chance to develop their probability skills and intuition before tackling the fairly challenging material of this book. An instructor using the book in this way should be prepared to supplement the stochastic processes chapters with better introductory materials. Used in this way, the book would be appropriate for two types of classes: an intermediatelevel course for statistics graduate students who will not otherwise cover stochastic processes in depth, and as part of a larger curriculum for students in bioinformatics that includes both basic statistics courses and courses giving a more comprehensive view of bioinformatics (particularly computer algorithmic aspects).