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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mi
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Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.
- Sales Rank: #372287 in Books
- Brand: Brand: Cambridge University Press
- Published on: 1998-05-13
- Released on: 1998-04-23
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .75" w x 6.85" l, 1.60 pounds
- Binding: Paperback
- 356 pages
- Used Book in Good Condition
Review
"The book is amply illustrated with biological applications and examples." Cell
"...successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence alignment...an excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology
"This is one of the more rewarding books I have read within this field. My overall evaluation is that this book is very good and a must read for active participants in the field. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology
Most helpful customer reviews
4 of 5 people found the following review helpful.
Brief and clear
By wiredweird
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.
24 of 26 people found the following review helpful.
Fantastic Descriptions of Probabilistic Sequence Algorithms
By Bob Carpenter
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 sequence statistics and algorithms applied to language, I would suggest Manning and Schuetze's "Foundations of Statistical Natural Language Processing". Although it is much more demanding computationally, more details on all of these algorithms, as well as some more background on the biology, along with some really nifty complexity analysis can be found in Dan Gusfield's "Algorithms on Strings, Trees and Sequences".
In these days of fly-by-night copy-editing and typesetting, I really appreciate Cambridge University Press's elegant style and attention to detail. Durbin, Eddy, Krogh and Mitchison's "Biological Sequence Analysis" is as beautiful and readable as it is useful.
6 of 15 people found the following review helpful.
Good bargain, but...
By A Customer
not suffciently precise for being an academic textbook. The definitions are sometimes incomplete, correctness proofs are missing, some exercises are incorrect. On the positive side, it does cover important topics, and brings good examples to illustrate main concepts and algorithms (which partially compemsates for the lack of precisenss).
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