Paradoxically, one of the merits of the book is something that is not in its content: There is no tutorial on how to surf the Web or how to download files using FTP. You are supposed to already have basic skills.
Bioinformatics is a moving target, and updated information is a must. This book fulfills this requirement very well. Coverage of the eMOTIF method of motif analysis, HMMER, PHI-BLAST, and CASP3 contests are examples of how up-to-date this book is.
The book is OS agnostic; you won't find a whole chapter devoted to "bioinformatics software for the Macintosh". The most representative programs in each area are introduced, regardless of the OS on which they are used. It seems that the author takes for granted that every research lab has at least one *nix box, a Windows machine, and a good Internet connection (some of the programs are Web applications). There is one inexplicably missing package: EMBOSS, the European Molecular Biology Open Source Software is not even mentioned, though it is well-known that the tools and databases used in the U.S. are different from the ones used in Europe.
The book has everything you really need to know. The underlying algorithms and assumptions and some limitations on their use are clearly explained. No particular software is explained; if you need it, read the fine manual. Why waste printed pages on man/help output, if it's easy to get? The main algorithms are thoroughly explained, without falling into complex mathematical formulas. If you really want to dig into the mathematic complexity, the book has plenty of references.
The extensive tables provide Web site links and references to select resources. They deserve a specific index for themselves, turning this book into a valuable reference manual.
There are a lot of flowcharts which work as a procedure manual or as a cheat sheet for an exam (at least to have a glance at "the big picture"). I dare to say that there is no bioinformatics book with such a feature. This is very important, since most bioinformatics work consists of managing a continuous data flow and making decisions regarding what to do next with your data. As a programmer, I particularly love this feature.
Protein analysis is usually a weak point in books not dealing directly with the topic. Chapter 9, "Protein Classification and Structure Prediction", has almost 100 pages devoted to methods to get more structural information about your protein than you thought was possible. The figures in this chapter are very well chosen, and good illustration is a must to understand complex 3D patterns like these.
The explanations are so conceptual that you can learn the fundamentals of Bayesian Statistics in just three pages. The level is fairly adequate; it's not a book that you can take to the beach and casually read while sunbathing. It is college-level material, so you have to take your time. This does not mean that the author makes topics unnecessarily difficult; he just treats these topics the way they deserve to be treated.
In the chapter on sequencing and databases (chapter 2), the author should have mentioned solutions for local databases. When making reference to ACEDB, for example, he should have pointed out that it is not only a public database, but a program which allows you to locally consult your own database.
Unfortunately, there is no chapter with a section which explains the application of the methods and techniques described. The only chapter with an application section is chapter 5, "Prediction of RNA Secondary Structure", and the section is incomplete and at the end. The incompleteness comes from the fact that it does not mention one of the hottest applications related to prediction of RNA secondary structure: ribosome design, enzymes based on RNA sequences, capable of cutting specific sites in the RNA, which could very useful against RNA viruses such as HIV, SARS, etc. It is my opinion that such a section should be at the beginning of each chapter to stimulate the reader. I also believe that the author should not deal with such hard topics without mentioning their applications. Would you learn assembler if you didn't know that the programs made with it will run faster and with low level access to hardware?
Biologists looking for a reference manual to help them with their daily work at a wet lab will be disappointed because there is nothing about primer design, indel, or SNP-based probe searching.
Programmers will miss a chapter or two with basic concepts of molecular biology. Still, you can't rely just on a book chapter to get all the biology you need to work on bioinformatics. If you want to be serious about this, my recommended course of action would be to read a beginner's book like Biology by Helena Curtis and/or take a 4-to-6-month introductory biology course at your local college.
A couple of lines about the associated Web site:
There is a sticker on the inner front cover with an access code which works as a ticket to Bioinformaticsonline.com. On the site, there is "supplemental information" which wouldn't fit in the book, like an in-depth explanation of how to set gap openings and gap extension scores with different programs. There is a set of very useful links and problem sets for classroom use for each chapter (though the provided solutions are plain, with no explanation or justification of answers).
The author, a professor of Bioinformatics at the University of Arizona, focuses on teaching the methods of sequence and structure analysis. He accomplish his goal pretty well. I highly recommend this book, not for a wet lab scientist or for a hardcore programmer with no biology background, but for anyone who wants to have an in-depth knowledge of what is behind bioinformatics software. You may end up wanting more, but I'm sure you won't regret buying this book.