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Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology, by Dan Gusfield
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Traditionally an area of study in computer science, string algorithms have, in recent years, become an increasingly important part of biology, particularly genetics. This volume is a comprehensive look at computer algorithms for string processing. In addition to pure computer science, Gusfield adds extensive discussions on biological problems that are cast as string problems and on methods developed to solve them. This text emphasizes the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics.
- Sales Rank: #806190 in Books
- Brand: Brand: Cambridge University Press
- Published on: 1997-05-28
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x 1.18" w x 6.97" l, 2.40 pounds
- Binding: Hardcover
- 556 pages
- Used Book in Good Condition
Review
"...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
Most helpful customer reviews
38 of 38 people found the following review helpful.
Definitive String Algorithms Text
By Bob Carpenter
If you like definition-theorem-proof-example and exercise books, Gusfield's book is the definitive text for string algorithms. The algorithms are abstracted from their biological applications, and the book would make sense without reading a single page of the biological motivations. Gusfield aims his book at readers who are fluent in basic algorithms and data structures (at the level of Cormen, Leisersohn and Rivest's excellent text). The exercises are wonderfully illustrative, being neither trivial nor impossible.
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.
67 of 69 people found the following review helpful.
Excellent...but dense
By Dr. Lee D. Carlson
This textbook gives a rigorous introduction to the algorithms of computational biology from the standpoint of theoretical computer science. It does however give the reader an overview of the practical application of these algorithms to the subject. The author gives a very detailed discussion of the most important results in the field, but the book is very dense: there are 228 definitions, 127 theorems, 490 references, and over 400 exercises that both illustrate the topics in the book and extend them. The author omits any real source code, but does give a URL where code for many of the algorithms can be found.
The author restricts his attention to deterministic approaches to string matching and comparison, and thus there is no treatment of hidden Markov models or Monte Carlo methods. The major algorithms such as the Aho-Corasick, Boyer-Moore, Knuth-Morris-Pratt, Needleman-Winsch, and Smith-Waterman are discussed and brilliantly motivated in the book. The author employs very effective diagrams to illustrate the matching concepts that are detailed in the book.
The book does require some time to read but it is worth the effort. Also, the exercises can be challenging but some should he done in order to understand the concepts in the book. The empirical results of the algorithms as sequence databases are also included, with FASTA, BLAST, BLOCKS, BLOSUM, and PROSITE are discussed in detail. The chapter that discusses these is the least mathematical of all the ones in the book and was no doubt included to connect the reader with real-world applications of the techniques in the book.
The last quarter of the book is a lot more trendy than the rest, with emphasis placed on algorithms for physical mapping, fragment assembly, and phylogenetic trees. These algorithms of course take on particular importance today given the Human Genome and other gene sequencing projects. Radiation-hybrid mappings, direct sequencing, and shotgun DNA sequencing are discussed in one of the chapters in this section, and the author addresses in great detail some approaches to speeding up sequence assembly. In the discussion on shotgun DNA sequencing the author refrains from any probabilistic analysis, instead referring the reader to the references. This omission goes along with the rest of the book, where probabilistic methods are not used, which is a little disappointing since these have shown great promise in computational biology. The exercises at the end of the chpater are very interesting and it is worth spending time working some of them through.
In a later chapter, the solution of the satisfiability problem in mathematical logic is discussed and shown to be solved (at least theoretically) by DNA-based computing. The quantities of DNA needed to carry out the computation are shown to be infeasible by the author.
This book will no doubt be of great assistance to those interested in the more rigorous approaches to computational biology. But the best attribute of the book is that one gets the impression that the author had a good time writing it, and that shows through in this very important book.
11 of 12 people found the following review helpful.
What it says, it says best.
By wiredweird
If you haven't read this book, you don't know biological string matching. The book's focus is clearly on string algorithms, but the author gives good biological significance to the problems that each technique solves. I came away from this book understanding the algorithms, but also knowing why the algorithms were valuable.
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.
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