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.
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.
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.
Customer Reviews:
"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
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Introduction to Bioinformatics
Arthur M. Lesk
Manufacturer: Oxford University Press, USA
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Bioinformatics For Dummies (For Dummies (Math & Science))
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Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins
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An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
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Essential Bioinformatics
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Beginning Perl for Bioinformatics
ASIN: 0199251967 |
Book Description
Bioinformatics is the collective name for a set of skills that has now become arguably one of the most important information-gathering and knowledge-building tools in current science research. The increase in the reliance upon bioinformatics in current research has made it essential for training in these skills to become an integral part of current science education. Introduction to Bioinformatics is a timely and much-needed textbook which provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. As a pioneer of the use of bioinformatics techniques in research, Dr Lesk brings unrivalled experience and expertise to the study of this field. The aim of the book is to generate an understanding of the biological background of bioinformatics, and to integrate this with an introduction to the use of computational skills. Without describing computer science or sophisticated programming skills in detail, the book supports and encourages the application of the many powerful computational tools of bioinformatics in a way that is both relevant to and stimulating for the reader. The book contains numerous problems and innovative Weblems (for Web-based Problems) to encourage students to engage with the subject and, with the accompanying web site, to develop a working understanding and appreciation of the power of bioinformatics as a research tool. Web site www.oup.co.uk/best.textbooks/biochemistry/bioinf/ A logo in the text alerts the reader to check the web site for the full text of programs referred to in the book. The web site also has links related to the book's problems, the innovative Weblems (for Web-based Problems), to encourage students to engage with the subject and, with the web site, to develop a working understanding and appreciation of the power of bioinformatics as a research tool.
Average customer rating:
- Don't start with this book
- Unsuitable for its stated purpose.
- Interesting but not very good for beginners
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Computational Molecular Biology: An Introduction
Peter Clote , and
Rolf Backofen
Manufacturer: Wiley
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Bioinformatics: Sequence and Genome Analysis
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Bioinformatics and Functional Genomics
ASIN: 0471872520 |
Book Description
Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self-study
* Source code of programs available on a Web site
Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.
Customer Reviews:
Don't start with this book.......2004-02-13
In general I agree with the two previous reviews.
This book is not very good as an introduction. First read some other book such as Setubal and Meidanis, "Introduction to Computational Molecular Biology"; or Krane & Raymer, "Fundamental Concepts of Bioinformatics". These books have more readable narrative and examples.
The writing in this book is obtuse. It is written like an advanced abstract math book, not like an ostensibly applied science book. The notation is unnecessarily intricate. Even though it says "Introduction" in the title, there are very few tutorial examples. This is just for mathematicians/computer scientists: no biologist I have ever known would/could read this and really understand the algorithms.
This book does, however, have one of the more complete detailed descriptions of various algorithms used for sequence matching, etc. If you have read some other books and are looking for more details on algorithms, then this is your book. But I'm still waiting for THE ultimate Computational Biology book!
Unsuitable for its stated purpose........2001-03-21
The book purports to be a "self-contained introduction" to computational biology. It fails on both counts due to its excessive ambition, its opaque pedagogy, and a large number of significant typographical errors, such as entire subroutines missing from pseudocode examples. Undergraduates seeking an accessible survey are advised to look elsewhere.
That said, the mathematical rigor of the text makes it ideal for students who have moved beyond the need for accessible surveys and wish to improve their fundamental understanding of the field.
Interesting but not very good for beginners.......2000-11-23
This is an unusual book. The authors obviously have not been aquinted with biomolecular sequence analysis and fail to give state-of-the-art references to research work in this field. The same comment applies to the description of applications of Shannon communication theory to DNA and protein sequence analysis. The enormous impact of these applications in the 1970s, 1980s and 1990s is not reflected in the book and one could wonder why the authors bother to write of Shannon theory at all. In addition to the above misgivings the authors decided to confuse the reader by including a discussion of quite controversial relationship between Shannon entropy and thermodynamic entropy. Both computational and laboratory biologists will not benefit from this kind of confusion. Mathematicians and computer scientist will probably be mislead by a superficial treatment of this quite intricate topic. Physicists and chemist will probably be able to sort out useful information from over-interpretations but they may wonder why this issue is discussed in a computational biology text.
Despite the above critique I like the book. Organization of this text is interesting and distinctly different form other books in the field. Chapters on sequence alignment and phylogenetic trees are most interesting and original. They should probably be read in conjunction with more systematic textbooks such as Gusfield's "Algorithms on strings, trees and sequences" or Li's "Molecular evolution." Despite many misgivings (see the beginning paragraph of this review) the mathematical primer (chapter 2) is very much worth reading for its originality and compactness. Particularly sections about probability distributions and combinatorial optimization can be useful for non-mathematicians and interesting for those who are mathematically literate. However, care should be exercised (see the beginning paragraph) while reading sections about entropy and about optimality of the genetic code. Chapter 1 about principles of molecular biology is not very good for non-biologists because it is too compact. Chapter about structure prediction is also too compact to be either understandable to non-specialists or enjoyable by the experts. If the authors' ambitious approach was to be sustained, this chapter should probably be expanded to the size of entire book. Exercises at the end of every chapter of the book are interesting and worth the reader's attention. It would probably be good to have access to solutions of all exercises but it is a minor problem.
In summary: it is an interesting book but it should be read in conjunction with other texts. It should not be recommended to the beginners in computational biology. Mathematically seasoned readers will enjoy reading selected parts of this book. It would be nice if the publisher could consider lowering price of this book (already in paperback.)
Average customer rating:
- try the enclosed GUI program
|
Introduction to Computational Biology: An Evolutionary Approach
Bernhard Haubold , and
Thomas Wiehe
Manufacturer: Birkhäuser Basel
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ASIN: 3764367008 |
Book Description
Analysis of molecular sequence data is the main subject of this introduction to computational biology. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.
This textbook is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists.
Customer Reviews:
try the enclosed GUI program.......2006-10-14
For some readers, the best attraction of the book is the GUI program that lets you quickly experiment and apply the main ideas. The text is very interdisciplinary, written for diverse audiences, spanning biology, computer science and mathematics. Some aspects of the book may perhaps be too mathematical for some biology readers. Say the Hidden Markov Models, for example. But if you keep at it, you should get able to get the gist of the models. Which is another reason for the usefulness of the GUI. Essentially, so long as you understand the basic math ideas, the GUI lets you sidestep the grotty details and focus on applying the overall models.
It could also be that the book is suitable for a university course. The chapter exercises and accompanying answers are useful, in this regard.
Book Description
Until now, those interested in the emerging field of computational molecular biology have used surveys and technical articles collected from many sources. Introduction to Computational Molecular Biology brings together major results in the field, in coherent and readable format. Setubal and Meidanis present a representative sample of problems in molecular biology, focusing on the algorithms that have been proposed to solve them. Readers will find background material on molecular biology, definitions of key terms, descriptions of models, and a full sample of algorithmic results. Key theoretical computer science concepts are emphasized, such as the improvement in asymtotic running time with better algorithms, the contrast between heuristics and an algorithm with guarantees, and the difficulty posed by NP-complete problems. Algorithms for sequence comparison, including the popular BLAST and FAST programs, are covered. Introduction to Computational Molecular Biology serves readers from both the mathematical and computing sciences as well as molecular biology. The authors assume a basic chemistry background and some training in college-level discrete mathematics and algorithms.
Customer Reviews:
Detailed broad overview of algorithms.......2005-11-16
We used this book in a bioinformatics class. It can take a whole semester to discuss this little book. The approach here is algorithmic. It explains fundamental bioinformatics algorithms in detail. In comparison to Pevzner's Computational Molecular Biology, it's more practical, and has less mathematical formalisms. This book peaks inside the black box of bioinformatics algorithms.
It's rigourous and for mathematics and computer science folks, and some material is difficult. Biologists may have a hard time with it, because of algorithm analysis and the required familiarity with graph theory. On the other hand, computer science folks shouldn't really take the introductory chapter on biology seriously.
Take a look at its Table of Contents to see what it covers, there's no point repeating it here.
Must have for anyone interested in implementing bioinformatics algorithms. On the other hand, if you're a biologist simply interested in how to use bioinformatics in your work, e.g., BLAST, there's no point reading this book.
Strong on algorithm analysis.......2005-02-03
This is a good book on some of the algortihms used in bioinformatics. That term may not have been in wide use back in `97, leading to the title that suggests a lot more focus on the molecules than is in fact present.
The book starts with two brief chapters on biology and mathematical basics - graph theory and algorithm analysis. I'm sorry to say that these really are too brief. A reader who comes in with little knowledge of either topic will probably leave with about the same level of knowledge.
After that, the authors give coverage of the basics, as `97 writers saw them: approximate string matching, fragment assembly, mapping, trees, and a discussion of the reversals that occur in DNA over evolutionary time. Each topic is presented carefully, in a number of variations, and with formal analysis of the algorithmic complexity. That last won't do much for the biologists in the crowd, but gives programmers a good idea of how each technique will behave as the problems grow larger (and they always do). The presentation generally stick with the most popular algorithms, emphasizing detailed presentation over breadth of coverage. Multiple alignment, in particular, could have used a lot more pages. Also, topics like assembly and restriction digests aren't at the forefront of analysis any more. They're important, but good algorithms exist in widely available tools, and more advanced analyses tend to attract more attention these days. The section on genome rearrangements is quite good, but seems to stand alone - it could have been one input into tree building, but the authors don't draw any clear relationships between the reorderings and any other problems.
The section on structure prediction is definitely showing its age. RNA structure prediction has come a long way since this was written, and protein structure prediction has come even farther. Discussions of the basic ideas are good, but a more recent reader will want a lot more development. The final section, on using DNA as a material for performing computations, is interesting but hardly mainstream. The authors use one or two problems as case studies, but don't present the kinds of tools that can be applied to lots of different problems, just a few point solutions.
This book's value, today, lies mostly in the clarity of its complexity analysis and in the pseudocode that gives a programmer a step in the right direction. It has a few unusual items, like a highly generalized gap model for approximate string matching. On the whole, though, more recent books present most of the same material at least as well, and cover more up-to-date topics.
When it was new, I might have given this book a five star rating. Times have changed, though, and I have to rate this book among the others on the shelves now.
//wiredweird
Concise and to-the-point.......2002-02-21
This is a really nice introduction to the most commonly used algorithms in bioinformatics. It is not really a general introduction to molecular biology or to computer science, and to make best use of this book a reader probably needs some prior exposure to both. But, for someone who has had a basic course in say genetics, and a basic programming course that covers simple data structures and algorithms etc., this volume provides all they will need to understand what is really happening when they run a BLAST search, for instance. A serious computer-science type person will probably not find the alogorithms described here very interesting, because they aren't meant to be elegant or interesting, just useful. I think a reader would have to have some direct interest in bioinformatics per se in order to enjoy this book. One thing that I find particularly nice is that the length of the chapters is just right so that you can read through a chapter in a single sitting, and because the chapters are largely independent of one another, its a handy book to have around and pick up when one has a little spare time. I recommend it very strongly.
Not a good introduction book.......2001-11-17
I do not think it is a good introduction book for biologists to learn computational biology. The authors should have used more figures and examples to illustrate the concepts. Also, I do not like the norrow margins of this book. I like to write comments and my understanding in the books when I read. There is simply no place to write.
Not a good introduction book.......2001-11-17
I do not think it is a good introduction book. The authors should have used more figures to illustrate the concepts. Also, I do not like the norrow margins of this book. I like to write comments and my understanding in the books. There is simply no place to write!
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Introduction to Bioinformatics: A Theoretical and Practical Approach
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Book Description
A comprehensible introduction to the key biological, mathematical, statistical, and computer concepts and tools behind bioinformatics. For physical scientists, the book provides a sound biological framework for understanding the questions a life scientist would ask in the context of currently available computational tools. For life scientists, a complete discussion of the UNIX operating system offers biologists graphical-user-interface comfort in a command-line environment, plus an understanding of the installation and management of UNIX-based software tools. In the applications sections the book provides a common meeting ground for life and physical scientists. Here they will find examples of the management and analysis of DNA sequencing projects, the modeling of DNA as a statistical series of patterns, various methods of pattern discovery, protein visualization, and the use of multiple sequence alignment to infer both functional and structural biological relationships. An accompanying CD contains several full and limited trial-versions of the programs discussed in the text, as well as a complete set of illustrations from each chapter suitable for lectures and presentations.
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