Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology

Book Description

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.

Customer Reviews:

Very in-depth explanations.......2006-07-27

I bought this book not because I have any interest in computational biology but because at that time I had an interest in (and professional need for) extremely fast and efficient ways to search through massive data stores. In this I was not disappointed, having found thorough treatments of how to do exact pattern matching as well as various types of "closest" match searching though very large data sets in minimal time.

While perhaps overly theoretical for a person like myself who has not had extensive schooling, it certainly matched my expectations.

I would recommend this book to anyone who I thought could benefit from it.

nice intersection of computing and biology.......2006-01-03

The text sits at the intersection of computer science and computational biology. It centres around the observation made by the author and others that often in CS, one has to manipulate strings of text, which are just sequences of text. While in computational biology, a recurrent theme is how to deal with sequences of molecules. These might be in a DNA sample or in a protein.

Surprisingly, from this simple observation, Gusfield manages to gather together considerable material. Over the decades, computing has accrued many algorithms for text string processing. The book's merit is in presenting those which are also applicable in bioinfomatics. The level of treatment is sophisticated, from the computing vantage. Enough so that perhaps the typical geneticist might not be able to easily follow the narrative. But a researcher with a strong background in both fields might be able to benefit.

What it says, it says best........2003-08-17

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.

Definitive String Algorithms Text.......2003-01-05

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.

All about suffix trees.......2001-11-05

Excellent book on String Algorithms. A lot of material. This is not an easy read, though, relatively not difficult for an algorithms and data-structures book.

This is the most complete resource i could find about suffix trees, how to implement them, usages, and algorithms. Actually, when I took this book, I was interested in suffix arrays. Well - this book explains those better than the original paper do.

Many applications to suffix trees are listed, along with comparisons to other algorithms applied to those problems.

If you need to get into string algorithms from computer science perspective - this is a good book to start. If you want to "feel" of the biologists side of the story, than this is not a good choice.

I use this book as a textbook on the subject, and I'm sure I'll be using it as a reference later on.

Book Description

Reviews of the Second Edition
"In this book, Andy Baxevanis and Francis Ouellette . . . have undertaken the difficult task of organizing the knowledge in this field in a logical progression and presenting it in a digestible form. And they have done an excellent job. This fine text will make a major impact on biological research and, in turn, on progress in biomedicine. We are all in their debt."
--Eric Lander, from the Foreword to the Second Edition
"The editors and the chapter authors of this book are to be applauded for providing biologists with lucid and comprehensive descriptions of essential topics in bioinformatics. This book is easy to read, highly informative, and certainly timely. It is most highly recommended for students and for established investigators alike, for anyone who needs to know how to access and use the information derived in and from genomic sequencing projects."
--Trends in Genetics
"It is an excellent general bioinformatics text and reference, perhaps even the best currently available . . . Congratulations to the authors, editors, and publisher for producing a weighty, authoritative, readable, and attractive book."
--Briefings in Bioinformatics
"This book, written by the top scientists in the field of bioinformatics, is the perfect choice for every molecular biology laboratory."
--The Quarterly Review of Biology
This fully revised version of a world-renowned bestseller provides readers with a practical guide covering the full scope of key concepts in bioinformatics, from databases to predictive and comparative algorithms. Using relevant biological examples, the book provides background on and strategies for using many of the most powerful and commonly used computational approaches for biological discovery. This Third Edition reinforces key concepts that have stood the test of time while making the reader aware of new and important developments in this fast-moving field. With a new full-color and enlarged page design, Bioinformatics, Third Edition offers the most readable, up-to-date, and thorough introduction to the field for biologists.
This new edition features:
* New chapters on genomic databases, predictive methods using RNA sequences, sequence polymorphisms, protein structure prediction, intermolecular interactions, and proteomic approaches for protein identification
* Detailed worked examples illustrating the strategic use of the concepts presented in each chapter, along with a collection of expanded,more rigorous problem sets suitable for classroom use
* Special topic boxes and appendices highlighting experimental strategies and advanced concepts
* Annotated reference lists, comprehensive lists of relevant Web resources, and an extensive glossary of commonly used terms in bioinformatics, genomics, and proteomics
Bioinformatics, Third Edition is essential reading for researchers, instructors, and students of all levels in molecular biology and bioinformatics, as well as for investigators involved in genomics, clinical research, proteomics, and computational biology.

Customer Reviews:

Somewhat more than an out-of-date catalog of tools.......2004-03-27

The book is a collection of chapters by different authors addressing software tools for various problems: database search, multiple sequence alignment, gene prediction, protein structure prediction, etc. A big flaw is that all of the authors assume a different level of prior background and have rather different emphases.

I'd have to agree with the other reviewer that Chapters 1 & 17, which constitute 10% of the book, are wasted paper. No one in 2001 (when the book was published), let alone 2004, needs Chapter 1's lengthy explanation of what e-mail and web browsers are. And the perl program at the anticlimax of Chapter 17 was ... anticlimactic.

The book is to a great extent a catalog of available software tools. With the exception of the chapters on multiple alignment and phylogeny, the emphasis is on not on how the tools work but how to operate them -- to the of saying "at this URL there is a web page where you can either paste in your sequence or upload a file". The idea of invoking a program through a Unix command line is more than once presented as a truly daunting prospect. The authors generally do a good job of emphasizing that the programs are the beginning of analysis and not the end; the results must always be viewed somewhat skeptically with an expert eye.

If you're coming at the book as a biologist, you will probably find it to be a useful catalog of software, though undoubtedly dated by now. If you're coming at it from the informatics side, you're going to need some background... a book like Dwyer's, Setubal and Meidanis's, or Mount's will get you up to speed on the algorithm aspects of the field with simplified versions of many of the big problems. Then you can look at this book to find good pointers to the ways the real-world versions have been addressed.

The book was published three years ago and, being to a large extent an index of the work of others, is necessarily no longer up to date in a fast-moving field. It needs a revision and, in the meantime, it would make more sense to snag a used copy than to pay full price for a new book.

A survey for tool users.......2003-10-09

Like any survey, it seems to touch the major features only. And, as others have pointed out, the tools change but the book doesn't.

I think this is a good, brief introduction to the wide variety of bioinformatic tools and databases on the internet. It describes the major features of each, and the kinds of results that each tool is good for. After that, the serious user will go to the sources of each tool or database, to learn more about the specifics as of the moment. No book can hope to keep up with the weekly enhancements at the major repositories.

I emphasize that this is for tools users, not tool makers. It addresses the working scientists who already know their subjects and their needs. This skips over the algorithms in favor of higher level descriptions, and skips over many of the biological reasons for the tools described. Better-informed tool users get better answers from the tools, true. At some point, though, the biologists want to skip the theory, skip the introduction to subjects in which they're experts, and get on with their science. I don't think this book was ever meant for people - and I'm one - who want full details of the algorithms.

I agree, the book treats its many subjects in a shallow way. I think that is by intent, since the book's real goal is breadth and its target is a reader who knows the basic science. It's a bit off the center of my interests, but I've found it helpful.

Bioinformatic for the beginner..........2003-01-31

I guess that everybody interrested by this kind of book knows already a little about bioinformatic and wants to improve his bioinformatician skill. So forget about this book:
This is really a well-documented introduction to all the methods currently used by every biologist or biology student, such as Blast, Clustal, multiple alignement or use of web-interface for submiting sequence.
So get it if you need a clear introduction to the field, but if you already know a little bit about bioinfo, immediately choose a more detailed book.

Poorly organized overpriced book.......2002-09-30

Although the book is presented as an introduction to the topic, its organization assumes that the reader has already been working in the area. Two of the chapters (1 and 17) are a waste of space. The first chapter presents a (useless) introduction to internet, while chapter 17 attempts (and fails to do so) to explain Perl in the context of bioinformatics. For the same money you can find far better books in the market. The good thing is that I only borrowed the book :)

..........2002-06-27

I used the Baxevanis volume in coursework at Johns Hopkins Biotechnology program...several of the chapter authors are associated with the school. There's no doubt that the editor and authors are experts in their fields, but the volume seems somewhat dated, and is disjointed. I found a couple of the chapters virtually unreadable. The real killer is the price. The book is GROSSLY overpriced...

Book Description

Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences. This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences. Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.

Customer Reviews:

A modern classic.......2003-10-15

The first name people learn in bioinformatics is the Smith-Waterman algorithm. Some people never learn anything else. This is by that Waterman. Although written in 1995, it still has some of the best discussion I've seen on the topics it addresses.

The first few chapters deal with the "digest problem," reconstructing a DNA or protein sequence from the fragment sizes of enzyme digests. The technique is not used as much now as it was then, but it's always good to know the background of modern techniques.

The digest problem doesn't stand alone, though. It introduces concepts - islands, anchors, etc. - that still matter. The problems in reconstructing molecules from digests yield the same kinds of intermediate results and the same ambiguities that arise in modern sequencing. As Waterman advances the discussion, shotgun sequencing appears as a logical extension, at least mathematically, of digest assembly.

Sequence assembly involve end matching, perhaps in the presence of sequencing errors. That introduces the topic for which Waterman's name is famous, approximate string matching. The next few chapter progress through dynamic programming and multiple alignments. The logical connections between the techniques shown are so tight that chapter boundaries are almost artificial. It was a real pleasure to see the computational and practical relationships laid out.

The final topics, RNA structure and phylogenetic trees, lack the continuity that characterized the first dozen chapters. The RNA structure may be the weakest chapter in the book, but still a very competent introduction.

Throughout, Waterman emphasizes mathematical rigor without insisting on uninformative theorems. Every topic is presented in rich detail, with special attention to scoring and background models. Perhaps there are newer discussions of some topics. I don't know of any clearer discussions, though. Best, I think, is how Waterman prepares the reader to ask all the right questions in any future discussion: what are the elements of the computation, how can elements be recombined, how good is a result, and how does the result stand out from the statistical background.

The final chapter is what a bibliography should be. It doesn't just list authors, titles, and dates of publication. It actually discusses the contribution that each source made to this book. Rather than leave the reader to wander aimlessly among obscure titles, Waterman shows which sources are most informative on which topics. I wish more authors took the time for such commentary.

This is a book worth having. It covers topics that I haven't seen elsewhere, and shows how many different topics relate to each other. It is rigorous without giving distracting detail. Most of all, it keeps the biology in sight of all calculations. Some authors seem to forget that anything exists but the arithmetic; Waterman puts the math clearly in the service of its subject. I enjoyed it immensely, and look forward to applying its content in my own research.

Packed full of good information.......2000-08-13

This book gives a good survey of the different techniques employed by computational biologists. After a brief review of molecular biology in Chapter 1, the author treats the mathematical modeling of restriction maps in Chapter 2 using graph theory. His presentation is somewhat hurried, but he does give references and gives the reader three exercises at the end of the chapter. Multiple maps are treated in Chapter 3, wherein the author first makes use of probability theory, via the Kingman subadditive ergodic theorem. The proof is omitted but the author does a good job of explaining its use in studying the double digest problem (DDP). The best part of this chapter is the author's explanation of the difficulties of using Kingman's results for solving the DDP, and goes on to discuss multiple solutions of the DDP. Graph theory is again used in the discussion. This sets up the discussion in Chapter 4, which outlines algorithms for the DDP. The author gives a very compact introduction to P- and NP-complete problems in the theory of computation, then proves that DDP is NP-complete. The author does a good job of discussing subsequent approximate methods used for the DDP, such as simulated annealing. Markov chains are introduced in the book here for the first time, but due to the shortness of the presentation, the reader should do outside reading as a back-up. The author does a great job of explaining the difficulties if measurement error is introduced in the DDP at the end of the chapter. Cloning is discussed in Chapter 5, with tools from probability theory used to deal with partial digest libraries. The chapter is really short though, and the working the problems at the end of the chapter is essential for the understanding the results of this chapter. The author switches gears in the next chapter, wherein physical maps are discussed. The discussion is fairly detailed and interesting. Sequencing is discussed in the next two chapters, and the treatment is very good. Hashing is introduced here, and psedocode is given throughout. The very important method of dynamic programming is outlined in Chapter 9, which is beautifully written, and again pseudocode abounds throughout. Genetic mapping is left out though, but the this, the longest chapter of the book, is a detailed introduction to this area. The results in this chapter are used to study multiple sequence alignment in Chapter 10, wherein hidden Markov models are introduced for the first time. The discussion of these models is very curt, but there are other books and notes available if the reader needs further guidance. The best chapter of the book follows, which discusses probability and statistics for sequence alignment. The theory of large deviations is brought in, and the author does an excellent job of discussing this important, and powerful theory. The reader's level of mathematical sophistication is assumed to be a lot greater than the rest of the book in this chapter. Knowledge of measure theory and martingales are assumed here. The author uses the very powerful tool of relative entropy, so indispensable in other applications of probability. The problem set at the end of the chapter is challenging but working them through is definitely worth the time involved. The next chapter also uses some heavy guns from probability theory to study sequence patterns. The author returns to matter of a more empirical nature in Chapter 13, which deals with RNA secondary structures. The reader with a background in simple combinatorial theory should find the reading straightforward and informative. Continuous-time Markov chains are introduced in the next chapter to study trees and sequences. The treatment here is rather hurried, so again the reader should work the exercises at the end of the chapter. The book ends with a discussion of the literature and references. All in all a very nice book, worth the price, and worth spending time reading. The only minus might be the total omission of actual source code, but that really was not the intent of the book. Readers with a strong mathematical background will like the book, as well as anyone interested in going into the area of computational biology.

Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)

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Informative, but not information I can use
Excellent survey of the field
it's about time!!!

Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)

Manufacturer: The MIT Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0262524236

Book Description

The advent of ever more sophisticated molecular manipulation techniques has made it clear that cellular systems are far more complex and dynamic than previously thought. At the same time, experimental techniques are providing an almost overwhelming amount of new data. It is increasingly apparent that linking molecular and cellular structure to function will require the use of new computational tools.

This book provides specific examples, across a wide range of molecular and cellular systems, of how modeling techniques can be used to explore functionally relevant molecular and cellular relationships. The modeling techniques covered are applicable to cell, developmental, structural, and mathematical biology; genetics; and computational neuroscience. The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene products. Modeling examples are provided at several scales for each subject. Each chapter includes an overview of the biological system in question and extensive references to important work in the area.

Customer Reviews:

Informative, but not information I can use.......2004-05-03

Regulatory networks are central to every aspect of computational biology. Determining what they are, and what genes, proteins, and post-translational modifications interact is a major and exciting field of study.

I just didn't come away from this book with that excitement. I was hoping for more about the large-scale regulation networks, but these papers go down to the quantum mechanics of interactions between pairs of molecules. I appreciate that the exact interactions matter, and that computation is probably the only way to examine some kinds of interactions (e.g. the ones in lethal mutations). It's just not what I think of as a "network."

I was also hoping for some more specifics about the computation techniques. There were some interesting insights here. For example, I never thought about the similarities between steady state chemical equilibrium and steady state Markov model behavior before, but the formalisms have striking similarities. I was also interested in some of the information-based measures for determining how well a model represents a system. I learned that the statistical assumptions behind normal chemical "equilibrium" break down at the scale of bacteria - instead, presence or absence of individual molecules matters more. Still, those were isolated kinds of facts and never came together into a whole for me.

The range of views was worthwhile. On the whole, though, the models all seemed very low-level to me, probably not well suited to handling more than a few dozen interactions, and the computation specifics were not always explicit. I'm still looking for a book with more information that I can apply directly.

Excellent survey of the field.......2001-08-04

An excellent survey for anyone contemplating doing research in this area. The authors make a special effort to identify the open research problems, what has been done to date and what there is very little of. This book will bridge the gap for anyone with a background in Molecular Biology that wants to build computer models for cellular and genetic activities. It is especially focused on gene regulation, but also covers other modeling areas such as diffusion. In reading this book, you will appreciatge both the good start this field is off to, but also the long way to go before a complete cell can be modeled. A great area to do pioneering work.

it's about time!!!.......2001-04-03

For many years, biologists have been accumulating descriptions of biological "parts" with an almost complete lack of a framework for understanding how those parts might really work together. This book represents the first and so far only example I have seen of an effort to describe modeling techniques that are right now being developed to construct such a framework. There are other books on "computational biology", but most of them are focused only on measuring and comparing different strands of molecules -- this book describes how computational techniques are starting to be applied to actually trying to understand how those molecules work together to generate life. On the outside jacket of the book, Bruce Alberts, President of the National Academy of Science, AND the guy whose book on molecular biology I had to buy for a lot of money when I was in college, describes the authors of this book as being "Brave". I would say it is an introduction to a "Brave New World". This has to be where biology is going -- Each of the chapters are written by different people, and as such there is some variation in readability. I also wish that the color illustrations were part of the chapter they refer to instead of being grouped in the middle. But most of the chapters start with enough of an overview to be understandable to anyone with a decent background in biology. And WOW -- biology is going to get much more exciting!! Oh one other thing -- the art on the inside of the jacket is wonderful - especially in contrast to the black cover with its standard diagram of metabolism -- I wonder if there is a message there :-) .

An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)

Average customer rating:

Uma excelente introdução à bioinformática
Excellent algorithms exercise & bioinformatics intro
Should really be called Intro Data Structures and Algorithms
A very good introduction!
The First Undergraduate Text

An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
Neil C. Jones , and Pavel A. Pevzner
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0262101068

Book Description

This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.

The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.

An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.

PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author's website.

Customer Reviews:

Uma excelente introdução à bioinformática.......2007-08-04

Este livro é excelente por várias razões. Entre elas posso citar o fato de estar totalmente voltado ao aprendizado por exemplos, sempre de forma a relacionar um problema computacional com um problema em bioinformática. É um livro muito abrangente, cobre muito bem os tópicos relacionados a alinhamentos e comparações de sequências. Seu capítulo sobre Algoritmos com Grafos é o meu preferido. O autor consegue passar as noções fundamentais com muita simplicidade, de forma que qualquer pessoa possa aprender num ritmo bem rápido.

Excellent algorithms exercise & bioinformatics intro.......2005-09-25

This is the first book that I've read regarding bioinformatics, so Im updating this as my class moves along. You better have a grasp of basic data structures prior to beginning this book and background with a programming language as there is very little hand-holding in this text. A bio background makes it all more interesting but certainly is not critical. There are no sample code or sources printed with the book nor is there an included CD nor answers to exercises. There is an associated web site where some ideas may be had and errata found/reported, but its not very active that I have seen. The pseudo code in the book is very python-like so easy to make use of. I personally transfer the book's concepts to C/C++ (habit) without much problem, except sometimes my results differ from the book. Apparently these are book bugs, so be sure to check the web site out if unexpected things pop up.
Presently my class is in chapter 8 (of 12) and looking back I would like to caution that some data processing algorithms will drive a computer's CPU quite hard so be aware of battery-munching & heat. My only bones with this book so far are the alphabet soup of variables and lack of answers to exercises. It would be nice if variable definitions were refreshed at the beginning of pseudo code samples.
I like this book as an algorithms text over traditional texts because the applications are much more fascinating. Imagine searching for something and you don't know where that something is. On top of that add not even knowing exactly what it is you are looking for. And when you do find it, its not even in the data searched! This may sound unlikely or even impossible, but it is neither. Rather, its very cool.
4-stars

Should really be called Intro Data Structures and Algorithms.......2005-07-08

I knew most of the stuff before I opened the first page. It's basically teaching data structures 101 using a few watered down bioinformatic problems for motivation. The lack of applied problems involving real data was most disappointing. It does have a lot of the type questions that some nerd (me one day :P) might ask you on a job interview. The questions are also a good way to kill time if you have nothing better to do. I give the book credit for stressing dynamic programming. I believe that this is one of the most important concepts in problem solving.

3 stars because I think it is a fairly good introduction for fledgling computer scientists BUT not a good reference for comptuer scientists trying to apply their skills to solve bioinformatic problems.

A very good introduction!.......2004-12-13

This book gives a broad overview of algorithmic methods used in bioinformatics. It is well writen and the mathematics needed to understand is undergraduate level. Reading this book makes appetite to apply these methods to problems or to dig deeper in the corresponding method.

Overall, a very good book, and due to its introductory level, one can recommend to all people interested in bioinformatics from all disciplines.

The First Undergraduate Text.......2004-12-07

Bioinformatics is probably the fastest growing field in both biology and computer science. The problems have come from the computer science department and the biology department having such fundamentally different goals. The computer scientists see the computer as an end in itself with no real thought on trying to do something useful with it. The biologists see the computer as just another tool in their laboratory. And the biological problems are huge, massive computers like the new Cray's and large Linux clusters are being devoted to biological applications.

This book is intended to fit into the chasm between biology and computer science. It discusses computer the algorithmic principles in terms of practical techniques that make sense to the undergraduate biologist. The book is well suited for a first class for the budding bioinformaticist.

Each main chapter in the book first introduces an algorithm, then it discusses the biologically relevant problems that this algorithm addresses, it includes a detailed problem and one or more solutions. Finally the chapter concludes with brief biographical sketches of leading figures in the field.

This is the first book of its type, and it's likely to remain a classic in the field through many editions and many years.

Computational Biology Of Cancer: Lecture Notes And Mathematical Modeling

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Computational Biology Of Cancer: Lecture Notes And Mathematical Modeling
Dominik Wodarz , and Natalia L. Komarova
Manufacturer: World Scientific Publishing Company
ProductGroup: Book
Binding: Hardcover

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ASIN: 9812560270

Java for Bioinformatics and Biomedical Applications

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Java for Bioinformatics and Biomedical Applications
Harshawardhan Bal , and Johnny Hujol
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387372350

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The science and practice of medicine has undergone a fundamental change as a result of large-scale genome projects that led to the sequencing of a number of important microbial, plant and animal genomes in the last 5 years. This book aims to combine industry standard software engineering and design principles, genomics and bioinformatics and cancer research. It focuses on creating and integrating practical, useful tools for the scientific community in the context of real-life, real-value biomedical problems that researchers face on a routine basis, rather than being just a didactic exercise in learning a programming platform. The book leverages technologies for molecular biology, genomics and bioinformatics and cancer research developed by the NIH, NCI-Center for Bioinformatics (NCICB), the National Center for Biotechnology Information (NCBI, a division of the National Library of Medicine (NLM) at the NIH) and Stanford University.

Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology)

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An excellent conversational review
Readable and practical
The title says it...
Good book, but the back cover lies....
computational

Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology)
Pavel A. Pevzner
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0262161974

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In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple manner. This makes the material accessible to computer scientists without biological training, as well as to biologists with limited background in computer science.

Computational Molecular Biology series
Computer science and mathematics are transforming molecular biology from an informational to a computational science. Drawing on computational, statistical, experimental, and technological methods, the new discipline of computational molecular biology is dramatically increasing the discovery of new technologies and tools for molecular biology. The new MIT Press Computational Molecular Biology series provides a unique venue for the rapid publication of monographs, textbooks, edited collections, reference works, and lecture notes of the highest quality.

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An excellent conversational review.......2005-08-16

Dr. Pevzner writes with a very lucid and conversational style about very complex and seemingly inscrutable topics. As a biologist who works primarily with computational tools in the field of genomics, this resource has helped to provide me with more than a rudimentary understanding of the algorithms and logic lurking in the methods of sequence analysis. Explaining dynamic programming to a biologist with rudimentary programming skills is a daunting task. However, his description of sequence alignment algorithms (including dynamic programming) in chapter 6 is quite readable and the information is very accessible. I highly recommend this book if you want a comprehensive understanding of the computational biologists toolkit.

Readable and practical.......2005-02-04

Pevzner has written a very useful book on bioinformatics algorithms, and one that seems reasonably up to date. The table of contents follows a classic plan: restriction maps, assembly and sequencing, 2- and N- way string comparisons, and analysis of rearrangements. There's a good but brief section on mass spec analysis - unfortunately, that chapter is called "Proteomics" even though the term covers a lot more than MS. Other sections skim the surface of hidden Markov models and Gibbs sampling for finding patterns ("motifs") in DNA.

A few chapters have unusual strengths. The "Conway Equation" gives more insight in analysis of motif significance than other introductory books do. The section in sequence comparison pays a lot more attention to BLAST-like algorithms than other books do, also - modern material you'd normally see only in the journals. Also, the section on rearrangements gives some ideas about using rearrangement data for phylogenetic analysis. That really gives the material meaning. Rearrangements aren't just string operations, they're features of evolution, and they can be compared to each other. No matter what the discussion, Pevzner keeps maintains a readable and enjoyably informal tone.

The book does have some weaknesses, though. It's a bit advanced for an undergrad intro, but bottoms out before the Baum-Welch algorithm, for example. Discussion of microarrays for sequencing seems dated. Pevnzer describes their use in sequencing, a rarity now, but skips their use in functional gneomics, where they are used most often. Illustration style is erratic and many diagrams are oddly stretched (3.5, 5.7, 8.3, and others, some much worse). Formal analysis of the algorithms is weak, but Pevzner somewhat makes up for that with better statistical analysis than many authors give. Also, even though the book was reprinted in 2001, it still estimates 100K genes in the human genome.

This is a good second book, maybe the one to read after Pevzner's newer "Introduction". It covers most of the basics and gives fairly usable pseudocode. Most of all, it always keeps the biology in mind. That, by itself, makes this book stand out.

//wiredweird

The title says it..........2004-01-12

An excellent book for studying computational molecular biology from an algorithmic perspective. (But if you never took mathematics seriously, you are forewarned.)

Good book, but the back cover lies...........2002-11-21

As others have noted, the premise that this book is for beginners from either the computational or the biological field is flawed...unless one's definition of beginner is a lot more advanced than mine.

For example even chapter one throws out terms like "recombination" and electrophoresis. without enough explanation for the biology newbie, IMO. Heck, for someone truly new to biology, a bit of time explaining what a chromosome is is probably time well spent.

And for the person coming from a pure biology background, some of the mathematics will definitely be a problem unless they have a decent understanding of combinatorics and discrete mathematics. And that "computational biology without formulas" blurb on the back cover should be read as "not as many formulas as I could have included if I really wanted", rather than "no formulas at all". There are equations galore in this book, rest assured of that.

That said, if a person *does* have the necessary background to make the material accessbile, then the book is definitely worth the purchase. The book's failure is in defining its target audience, not in the material presented.

computational.......2000-12-22

While this is certainly the do-loop of computational biology the reader would question the assertion that this book provides a common link (no pun) between the biologists need for computational expertise and the programmer's need for biological insight. In either case a solid basis in Discrete Mathematics goes along way here (usually a required course for computer science majors). This reader thinks a similar required course in genetics should be made for engineers to reduce their reductionistic tendencies. However the distinction between these lines grows narrower with each new computer chip. None the less the book is well written, and easy to read (as Discrete Math stuff goes). This book is not for beginners in either Combinatorics or genetics and the last part of the book poses many current questions that as the author says, "are just currently being answered". This book already assumes you know about such things as NIH, PDB, Chime, Isis, NCIB, docking, etc. For those less adapt at programming (myself) the following alternatives are fun, useful and to the point. Both trees and networks can be easily set up in MathCad using their built in resource center add-ins for Combinatorics and Set Theory. They also provide a Traveling Salesman routine in Numerical Recipes that can be applied directly to the problems in Pevzner's book. (Although remembering that most optimization algorithms provide only the most probable 100 out of 2 million it is still fun!). Most of the mappings and node process familiar to Discrete Math can be solved using Mathcad and some sort of adjacency matrix combination. (Including the four-color mapping problem). This provides the basis for most nodal mappings. For the more daring the adjacency matrices can be run through Matlab's GUI's decompositions and analyzed using their optimization toolbox. Currently I'm investigating the Hidden Markovian chains using the Frame advance feature of Mathcad applied to 2D cspline- intercept graphing and updating by frame iteration. This book is for the serious student or solid course material in a related field, and while probably not rated in top ten novels of 2000 certainly rates five mouse clicks from this reader.

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)

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"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)
Richard C. Deonier , Simon Tavaré , and Michael S. Waterman
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387987851

Book Description

Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.

This book features:

Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation

Presentation of fundamentals of probability, statistics, and algorithms

Implementation of computational methods with numerous examples based upon the R statistics package

Extensive descriptions and explanations to complement the analytical development

More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature

Exercises at the end of chapters

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.

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"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ.......2006-07-08

This textbook was based on the authors' instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master's, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry.

This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or 'machine' utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species.

The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computational biology to fully understand them, the authors include examples of computations using R. This volume is described as a "roll up your sleeves and get dirty" introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for intellectual growth as new experimental approaches lead to new computational tools.

One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics --- the 98% being processed in R language. It isn't intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher's answer book had been appended.

The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data.

Leonard C. Silvern
Systems Engineering Laboratories
Clarkdale, AZ

Computational Molecular Biology (Theoretical and Computational Chemistry)

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Computational Molecular Biology (Theoretical and Computational Chemistry)

Manufacturer: Elsevier Science
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Binding: Hardcover

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ASIN: 0444500308

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This book covers applications of computational techniques to biological problems. These techniques are based by an ever-growing number of researchers with different scientific backgrounds - biologists, chemists, and physicists.

The rapid development of molecular biology in recent years has been mirrored by the rapid development of computer hardware and software. This has resulted in the development of sophisticated computational techniques and a wide range of computer simulations involving such methods. Among the areas where progress has been profound is in the modeling of DNA structure and function, the understanding at a molecular level of the role of solvents in biological phenomena, the calculation of the properties of molecular associations in aqueous solutions, computationally assisted drug design, the prediction of protein structure, and protein - DNA recognition, to mention just a few examples. This volume comprises a balanced blend of contributions covering such topics. They reveal the details of computational approaches designed for biomoleucles and provide extensive illustrations of current applications of modern techniques.

A broad group of readers ranging from beginning graduate students to molecular biology professions should be able to find useful contributions in this selection of reviews.

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