Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

I keep coming back to this book for its readable, applicable summaries of basic algorithms. One chapter covers the basics of dynamic programming for string matching: a staple of bioinformatics computing. The authors come back to it a number of times as they introduce new variations on the string-matching theme. They give about the clearest description of the Needleman-Wunsch and basic variants (including Smith-Waterman) of any book I know.The bulk of the book is devoted to Hidden Markov Models (HMMs), as one might have guessed in a book with Eddy as co-author. It covers the basics of model construction, motif finding, and various uses for decoding. Again, it covers all the basics so clearly you'll want to start coding as soon as you read it. The later sections of the book cover phylogeny and tree building, along with the relationships to multiple alignment. Good, solid, clear writing prepares the reader for texts that may be more specialized, but possibly less transparent. The next-to-last chapter, on RNA folding, is weaker than the ones before, in my opinion. It ties to the other chapters reasonably well in terms of algorithms, but I don't think it does justice to the thermodynamic models of RNA folding. If there is any weakness in this chapter, though, it does not detract from the strengths elsewhere. The final chapter, the "background on probability", is the one that I think needs the most support. If you don't already understand its topics, I doubt that this will help very much. (If you do understand them, you won 't need the help.) There's nothing inherently tricky about probability, but individual distributions carry many assumptions, and I did not see those spelled out well.This shouldn't be the only book in your bioinformatics library. If you really want algorithms, though, it's a good book to have in the collection and one you'll keep coming back to.

I picked up this book at the recommendation of a number of colleagues in computational linguistics and speech processing as a way to find out what's going on in biological sequence analysis. I was hoping to learn about applications of the kinds of algorithms I know for handling speech and language, such as HMM decoding and context-free grammar parsing, to biological sequences. This book delivered, as recommended.As the title implies, "Biological Sequence Analysis" focuses almost exlusively on sequence analysis. After a brief overview of statistics (more a reminder than an introduction), the first half of the book is devoted to alignment algorithms. These algorithms take pairs of sequences of bases making up DNA or sequences of amino acids making up proteins and provide optimal alignments of the sequences or of subsequences according to various statistical models of match likelihoods. Methods analyzed include edit distances with various substitution and gapping penalties (penalties for sections that don't match), Hidden Markov Models (HMMs) for alignment and also for classification against families, and finally, multiple sequence alignment, where alignment is generalized from pairs to sets of sequences. I found the section on building phylogenetic trees by means of hierarchical clustering to be the most fascinating section of the book (especially given its practical application to classifying wine varietals!). The remainder of the book is devoted to higher-order grammars such as context-free grammars, and their stochastic generalization. Stochastic context-free grammars are applied to the analysis of RNA secondary structure (folding). There is a good discussion of the CYK dynamic programming algorithm for non-deterministic context-free grammar parsing; an algorithm that is easily applied to finding the best parse in a probabilistic grammar. The presentations of the dynamic programming algorithms for HMM decoding, edit distance minimization, hierarchical clustering and context-free grammar parsing are as good as I've seen anywhere. They are precise, insightful, and informative without being overly subscripted. The illustrations provided are extremely helpful, including their positioning on pages where they're relevant.This book is aimed at biologists trying to learn about algorithms, which is clear from the terse descriptions of the underlying biological problems. The technical details were so clear, though, that I was able to easily follow the algorithms even if I wasn't always sure about the genetic applications. After studying some introductions to genetics and coming back to this book, I was able to follow the application discussions much more easily. This book assumes the reader is familiar with algorithms and is comfortable manipulating a lot of statistics; a gentler introduction to exactly the same mathematics and algorithms can be found in Jurafsky and Martin's "Speech and Language Processing". For biologists who want to see how s

I am a physicist and had some interest in what these bio informatics actually do. I must say I am impressed both in the rigor and sharpness of the probabilistic reasoning. This book relies heavily on probability theory (especially hidden Markov models) and is clear enough to be read without a sharp pencil. Don't get me wrong it is not simple enough to be good late night bedtime entertainment. The biological and chemical background is also easy to grasp. The authors are obviously very active in the field they describe. Their self citations seem absolutely reasonable..

This book is a very well written overview to hidden Markov models and context-free grammar methods in computational biology. The authors have written a book that is useful to both biologists and mathematicians. Biologists with a background in probability theory equivalent to a senior-level course should be able to follow along without any trouble. The approach the author's take in the book is very intuitive and they motivate the concepts with elementary examples before moving on to the more abstract definitions. Exercises also abound in the book, and they are straightforward enough to work out, and should be if one desires an in-depth understanding of the main text. In addition, there is a software package called HMMER, developed by one of the authors (Eddy) that is in the public domain and can be downloaded from the Internet. The package specifically uses hidden Markov models to perform sequence analysis using the methods outlined in the book. Probabilistic modeling has been applied to many different areas, including speech recognition, network performance analysis, and computational radiology. An overview of probabilistic modeling is given in the first chapter, and the authors effectively introduce the concepts without heavy abstract formalism, which for completeness they delegate to the last chapter of the book. Bayesian parameter estimation is introduced as well as maximum likelihood estimation. The authors take a pragmatic attitude in the utility of these different approaches, with both being developed in the book. This is followed by a treatment of pairwise alignment in Chapter Two, which begins with substitution matrices. They point out, via some exercises, the role of physics in influencing particular alignments (hydrophobicity for example). Global alignment via the Gotoh algorithm and local alignment via the Smith-Waterman algorithm, are both discussed very effectively. Finite state machines with accompanying diagrams are used to discuss dynamic programming approaches to sequence alignment. The BLAST and FASTA packages are briefly discussed, along with the PAM and BLOSUM matrices. Hidden Markov models are treated thoroughly in the next chapter with the Viterbi and Baum-Welch algorithms playing the central role. HIdden Markov models are then used in Chapter 4 for pairwise alignment. State diagrams are again used very effectively to illustrate the relevant ideas. Profile hidden Markov models which, according to the authors are the most popular application of hidden Markov models, are treated in detail in the next chapter. A very surprising application of Voronoi diagrams from computational geometry to weighting training sequences is given. Several different approaches, such as Barton-Sternberg, CLUSTALW, Feng-Doolittle, MSA, simulated annealing, and Gibbs sampling are applied to multiple sequence alignment methods in Chapter 6. It is very well written, with the only disappointment being that only one exercise is given in the entire

This is that rarest of rare: an intro-level, multi-authored monograph which is thorough, internally consistent, and a joy to read. Unlike the reviewer above, although not the first book I read on the subject, had it been so, I could have saved myself a great deal of time. As an introductory work it is simply unparalleled. In view of the rate of information growth in the field, this reader thinks it deserves to be amended annually, not merely reprinted periodically. The authors and editors are to be congratulated for producing a real gem. Your choice of which of the 30 or so "advanced" (i.e., costing >$60) books on probabilistic sequence analysis will be much more informed if you read this one first.