- Hardcover: 556 pages
- Publisher: Cambridge University Press; 1 edition (May 28, 1997)
- Language: English
- ISBN-10: 0521585198
- ISBN-13: 978-0521585194
- Product Dimensions: 7 x 1.5 x 10 inches
- Shipping Weight: 3.4 pounds (View shipping rates and policies)
- Average Customer Review: 14 customer reviews
- Amazon Best Sellers Rank: #814,610 in Books (See Top 100 in Books)
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Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology 1st Edition
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"...an important summary of the state of the art in pattern matching and an indicator of the importance biological problems have assumed among many researchers. It will hopefully encourage them to question the importance of the problems they endeavor to solve." SIGACT News
"The book will be profitable both for graduate students in computer science and for biologists with a good background in programming." Mathematical Reviews
"One often encounters in this book thought-provoking quotes relating to the importance of sequence analysis...Also found in the text are interesting biological examples of sequence analysis..." Cell
String algorithms are a traditional area of study in computer science. In recent years their importance has grown dramatically with the huge increase of electronically stored text and of molecular sequence data produced by various genome projects. This book explains a wide range of computer methods for string processing. It also contains extensive discussions on biological problems that are cast as string problems, and on techniques to solve them. The book is both a reference for computer scientists and computer-oriented professionals in biology and bio-informatics and a textbook for graduate and advanced undergraduate courses on string algorithms and on computational biology.
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All of the major exact string algorithms are covered, including Knuth-Morris-Pratt, Boyer-Moore, Aho-Corasick and the focus of the book, suffix trees for the much harder probem of finding all repeated substrings of a given string in linear time. In addition to exact string matching, there are extensive discussions of inexact matching. Even the discussions of widely known topics like dynamic programming for edit distance are insightful; for instance, we find how to easily cut space requirements from quadratic to linear. There is also a short chapter on semi-numerical matching methods, which are also of use in information retrieval applications. Inexact matching is extended to the threshold all-against-all problem, which finds all substrings of a string that match up to a given edit distance threshold. The theoretical development concludes with the much more difficult problem of aligning multiple sequences with ultrametric trees, with applications to phylogenetic alignment for evolutionary trees (an approach that has also been applied to the evolution of natural languages).
Note that there is no discussion of statistical string matching. For that, Durbin, Eddy, Krogh and Mitchison's "Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acides" is a good choice, or for those more interested in language than biology, Manning and Schuetze's "Statistical Natural Language Processing". There is also no information on more structured string matching models such as context-free grammars, as are commonly used to analyze RNA folding or natural language syntax. Luckily, Durbin et al. and Manning and Schuetze also provide excellent coverage of these higher-order models in their books.
This book is not about efficient implementation. If you need to build these algorithms, you'll also need to know how to write efficient code and tune it for your needs. This is an algorithms book, pure and simple.
As a computer scientist, I found the discussions of computational biology to be more enlightening than in other textbooks on similar topics such as Durbin et al., because Gusfield does not assume the reader has any background in cellular biology. Instead, he provides his own clear and gentle introductions illustrated with algorithms, applications, open problems and extensive references. Like most Cambridge University Press books, this one is beautifully typeset and edited.
No, there isn't any real source code here. That should not be a problem - this book aims above the cut&paste programmer. The book in meant for readers who can not only understand the algorithms, but apply them to unique solutions in unique ways.
String matching is far too broad a topic for any one book to cover. The study can include formal language theory, Gibbs sampling and other non-deterministic optimizations, and probability-based techniques like Markov models. The author chose a well bounded region of that huge territory, and covers the region expertly. The reader will soon realize, though, that algorithms from this book work well as pieces of larger computations. The book's chosen limits certainly do not limit its applicability.
By the way, don't let the biological orientation put you off. DNA analysis is just one place where string-matching problems occur. The author motivates algorithms with problems in biology, but the techniques are applicable by anyone that analyzes strings.
Most recent customer reviews
This is a very dense book for the serious.Read more