Reviews (30)

horrible
This book is the worst I've ever purchased. It has been no help whatsoever.It had a couple examples of PERL programming...big deal.

The 5-star ratings are obvious shills (one reviewer wrote a very long review and has never reviewed anything else)

Good introduction, somewhat uneven
This book is a good introduction to Bioinformatics and to what it takes to get started in the field. Some reviewers deride it as too superficial or as too Unix-centric, but I think those are two of its strengths. The authors lay no claim to having written the definitive work on the subject of Bioinformatics, and they freely admit that they come in with a certain bias. If you are serious about Bioinformatics this won't be your last book anyway, but it'll get you started.

That said, I found the material a bit uneven. The authors tend to jump from almost trivial stuff to very complex in a heartbeat, and they sometimes use a concept or command before it can be properly understood One example: Introducing the Unix commands head and tail, then moving on to split and csplit. The introduction to regular expressions as needed by csplit follows a few pages later.

Nevertheless, I plan to use this book as a companion text to my own sequence of computer classes for biologists, and I think it will serve that purpose very well.

Useful only for a reference book
We are all well aware that it is impossible to write a book on bioinformatics satisfying all types of readers. That is the reason why we are spending much time on finding a book that we can say "This book is just for me!"

Well, this book is not a self-teaching book by itself. Don't expect that things will become clear to understand after reading this book.

Reviews (13)

Somewhat more than an out-of-date catalog of tools
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
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.

Reviews (14)

Useful Text
"...probably the most up-to-date textbook on molecular biology...a useful text...and reference..." (Annals of Pharmacotherapy, September 2003)

Marvelous Text
"...a marvelous text...plenty of definitions and excellent illustrations essential for an introductory textbook...the reader is...guided wonderfully through the text...this text is an essential cornerstone of information..." (Pharmaceutical Research, Vol. 19, No. 12, December 2002)

Reviews (4)

good book
This is a pretty good book. If you are trying to work on molecular evolution, comparative genomics, and bioinformatics, you should read this book and use it as a reference later on. Since this research topic is quite active now, some data or hypothesis in the book are not absolutely correct. You can read journals to keep yourself updated.

C value Chart
Its a pretty ok book. They didn't do alot of true research for it. They did alot of "adaptist story telling" in the book. For example: In the chart about C value. They took a chart from another reference and they called Amphiuma means a newt. Amphiuma means i by no means a newt. Its a long aquatic salamander with 4 small useless legs and gill slits.

Reviews (7)

Highly Recommended
"...the book is highly recommended as an excellent one stop resource on microarray technology" (Proteomics)

Recommended Book
"...a very personal, encyclopedic, diffuse compendium of everything known about microarrays...fun to read...full of information that a working biologist...might be quite interested to learn." (ASM News, Vol. 69, No. 7, 2003)

Reviews (1)

Good Concept Building book.
I bought this book to understand the computer science side of molecular biology. This book gives good treatment of the basics though if you are a programmer and want to get involved straight away, this book will be of little help.

Editorial Review

Biology, it seems, is a good showcase for the talents of Perl. Newcomers to Perl who understand biological information will find James Tisdall's Beginning Perl for Bioinformatics to be an excellent compendium of examples. Teachers of Perl will likewise find the text to be filled with fresh programming illustrations of growing scientific importance. Seasoned Perlmongers who want to learn biology, however, should search elsewhere, as Tisdall's emphasis is on Perl's logic rather than Mother Nature's.

Departing from O'Reilly's earlier monograph Developing Bioinformatic Computer Skills, Tisdall's text is organized aggressively along didactic lines. Nearly all of the 13 chapters begin with twin bullet lists of Perl programming tools and the bioinformatic methods that require them. Likewise, the chapters end with exercises. String concatenation is illustrated with gene splicing, and regular expressions are taught with gene transcription and motif searching.

Tisdall emphasizes sequence examples throughout, leading up to an introduction to a Perl interface for the NIH GenBank biological database and the widely used BLAST sequence alignment tool. After a brief discussion of three-dimensional protein structure, he returns to sequence extraction and secondary structure prediction.

Tisdall's goal is to boost the beginning programmer into a domain of self-learning. He imparts essential etiquette for the success of programming newbies: use the wealth or resources available, from user documentation to Web site surveys to FAQs to How-To's to news groups and finally to direct personal appeals for help from a senior colleague. A well-plugged-in bioinformatics Perl student will soon discover Bioperl, an open-source effort to bring research-grade bioinformatic tools to the Perl community. Bioperl is described briefly at the end of Tisdall's book and will reportedly be a forthcoming title of its own in the O'Reilly bioinformatics series.

Although he introduces bioinformatics as an academic discipline, Tisdall treats it as a trade throughout his book. He indicates that open questions and computational hard problems exist, but does not describe what they are or how they are being tackled. Ultimately, Tisdall presents bioinformatics as another arrow in a bench scientist's quiver, very much like HPLC, 2D-PAGE, and the various spectroscopies.

Reviews (12)

A terrible book
I have to say this is a terrible book. When reading the book, I have a feeling that this book is just a note for the authors themselves. The book is not written from the readers' viewpoint. Some definitions are incomplete; proofs are missing; particularly, notations are confused. I have to continue to guess what the authors truly mean. A good thing about the book is that it covers lots of examples of using HMM for sequence analysis.

Brief and clear
I keep coming back to this book for its readable, applicable summaries of basic algorithms.

One chapter covers the basics of dynamic programming for string matching: a staple of bioinformatics computing. The authors come back to it a number of times as they introduce new variations on the string-matching theme. They give about the clearest description of the Needleman-Wunsch and basic variants (including Smith-Waterman) of any book I know.

The bulk of the book is devoted to Hidden Markov Models (HMMs), as one might have guessed in a book with Eddy as co-author. It covers the basics of model construction, motif finding, and various uses for decoding. Again, it covers all the basics so clearly you'll want to start coding as soon as you read it.

The later sections of the book cover phylogeny and tree building, along with the relationships to multiple alignment. Good, solid, clear writing prepares the reader for texts that may be more specialized, but possibly less transparent.

The next-to-last chapter, on RNA folding, is weaker than the ones before, in my opinion. It ties to the other chapters reasonably well in terms of algorithms, but I don't think it does justice to the thermodynamic models of RNA folding. If there is any weakness in this chapter, though, it does not detract from the strengths elsewhere.

The final chapter, the "background on probability", is the one that I think needs the most support. If you don't already understand its topics, I doubt that this will help very much. (If you do understand them, you won 't need the help.) There's nothing inherently tricky about probability, but individual distributions carry many assumptions, and I did not see those spelled out well.

This shouldn't be the only book in your bioinformatics library. If you really want algorithms, though, it's a good book to have in the collection and one you'll keep coming back to.

Reviews (9)

What it says, it says best.
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
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
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.

Reviews (6)

Readable and practical
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 specanalysis - 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...
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....
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.

Reviews (3)

a nice book
This book was very favorably reviewed on perl.com.

Not a good perl programming book period!
This books tries to combine and explain both bioinformatics and perl programming yet fails miserably at both. Though I have taken a class on learning perl this code is difficult to read and poorly explained. The bioinformatics is useless because the examples are simply stupid. For example instead of using free energy to determine RNA folding the author uses hydrogen bonding which is completely irrelavent or predicting species by using %gc or %at content between two organsims also useless.If you are looking for bioinformatics programming tips this book will not help you.
Variables are introduced that are not explained and the program is written in the most condensed possible way making it difficult to read and leaving you wading through each line. I am thankful I have taken programming perl and bioinformatics or this book would be of zero value. If I could I would give this book a -5 stars. Check it out at a library before you BUY!!!!!!! Even if perl.com reviews the book favorably the biology is at best completely WRONG!!! Buy O'Riely's advanced bioinformatics.

Editorial Review

Larry Wall wrote Perl and he wrote Programming Perl. Better yet,he writes amusingly and well--all of which comes across in this latest editionof the definitive guide to the language.

Like Topsy, Perl just grew, and as a result the need for a third edition cameabout. It's now over 1,000 pages, which it needs to be, as it performs severaldifferent duties. First, it's an introduction to the Perl language for those whoare new to programming; also, it's a guide for those who are coming from otherlanguages; and, finally, it's a Perl language reference.

Among Larry Wall's other pursuits is being a linguist, and it's perhaps for thisreason that Perl is a peculiarly flexible language with many routes to achievingthe same ends, as the authors ably demonstrate. It's also extensible in severalways, designed to work with many other languages. Also, as it's largelyinterpreted, programs written in Perl tend to run unmodified on a variety ofplatforms--although platform-specific Perl modules and programming practices arealso discussed.

A major strength of Programming Perl is the way subject areas areapproached from several directions. This constant shift of viewpoint eliminatesblind spots in the reader's understanding and provides a pleasing echo of theway Perl itself can take many routes from here to there.

Reviews (4)

Supplementary.
"Thus researchers from all walks of pattern recognition should get something out of this book."

I agree.

An excellent book for pattern recognition
I think the authors provide a nice balance between theory and practice. Onone hand, the algorithms presented can and are meant to be implemented for testing. On the other hand, the authors provide afairly soundmathematical treatment of areas suchas Markov Models, clustering, andtemplate matching. Most important, the authors do not focus attention onlyon one type of problem (e.g. character recognition). Thus researchers fromall walks of pattern recognition should get something out of thisbook.

Two big thumbs up!

Reviews (11)

A great book!
This book is the most amazing book on algorithms I've read. The concepts are so well explained that moving to "An introduction to Algorithms by Cormen, Rivest" will be very easy.

I come from a non-computer science background. When I started my coursework in Computer Science I was intimidated with Cormen - (although that IS THE MOST AUTHORITATIVE and a complete text!) until I found Harel.

Harel covers ALL the key aspects of algorithms and quite a bit of Data Structs too. He explains all the concepts in a non-mathematical, yet intellectually stimulating manner.One can literally read through the book in single day and gain insight into the most difficult topics like, unsolvable problems, hard problems, NP and NP complete problems.

On a side note - I pity those reviewers who returned the masterpiece and took objection to Bible quotes. Please grow up and look at what the book has to offer instead of taking objection to such insignificant embellishments

Very interesting read
This is a book about solving problems, finding the limits of the kinds of problems we can solve, and those we will never solve, and how we solve them. It is also a book about the essential results of computer science, and will give the reader a better understanding of what computers can do, and why some problems in computer science are much harder then they appear.

As far as I know, this is the only book that distills the essence of computer science, and presents it in a format suitable for the average reader. This is computer science's answer to Stephen Hawkings "A Brief History of Time".

Reviews (2)

Genomics
So far I heard that this is the one of the best book containing all informations and instructions about genome project. I love to review this book throgh e-mail.
I can't afford myself to buy this book. but so far i can say if it is really very much helpful to me i recommend evybody of my class (MSC Bioinformatics) to go for buying that book.

with thanks

Reviews (17)

If you are serious about bioinformatics, don't get this one.
This book is suitable for those who have never explored bioinformatics and it is quite disorganized. Hence, lecturers need to be able to organize this book for students in a proper way. It doesn't explain algorithms in depth and so if you are algorithmic, get another book. I think this book is too expensive.

Agonizingly painful
David W. Mount is a terrible writer.I can't believe how much of a chore this book is to read.His sentences, in my opinion, are ambiguous and I find myself backtracking unnecessarily.