Book Description

Bioinformatics – the process of searching biological databases, comparing sequences, examining protein structures, and researching biological questions with a computer – is one of the marvels of modern technology that can save you months of lab work. And the most amazing part is that, if you know how, you can use highly sophisticated programs over the Internet without paying a dime and sometimes, without installing anything new on your own computer. All you need to know is how to use these technological miracles.

That's where Bioinformatics For Dummies comes in. If you want to know what bioinformatics is all about and how to use it without wading through pages of computer gibberish or taking a course full of theory, this book has the answers in plain English. You'll find out how to

• Use Internet resources
• Understand bioinformatics jargon
• Research biological databases
• Locate the sequences you need
• Perform specific tasks, step by step

Written by two experts who helped develop the science, Bioinformatics For Dummies is all about getting things done. If you're just getting your feet wet, start at the beginning with a quick review of those necessary parts of microbiology and an overview of the tools available. If you already know what you want to do, you can go directly to a chapter that shows you how. Get the lowdown on

• Researching and analyzing DNA and protein sequences
• Gathering information from all published sources
• Searching databases for similar sequences and acquiring information about gene functions through sequence comparisons
• Producing and editing multiple sequence comparisons for presentation
• Predicting protein structures and RNA structures
• Doing phylogenetic analysis

With an Internet connection and Bioinformatics For Dummies, you'll discover how to peruse databases that contain virtually everything known about human biology. It's like having access to the world's largest lab, right from your desk. This book is your lab assistant – one that never takes a day off, never argues when you ask it for help, and won't demand a benefits package. ... Read more

Reviews (6)

Bioinformatics for Dummies by J.M. Claverie & C. Notredame
"Bioinformatics for Dummies" is an excellent resource. It is clear, easy to read, well organized and illustrated. I was particularly pleased by the colloquial tone of the writing: in addition to being informative, it was fun to read!

As a scientist who spends at least half of my time BLASTing, I also read it for accuracy and found it to almost error-free (any errors were in the figures). Additionally, most of the web pages were up-to-date, although as time passes the links will decay and web pages will change their look. In addition, the book contained enough in-depth content to teach me several new tricks of the trade.

Further, I believe the book had sufficient background material to educate the novice. To test this, I gave the manual to a material science chemist and he was able to understand the material, at least until he decided it was more than he wanted to know and quit reading.

This is a useful text for those who want to know more than an operational definition of bioinformatics and a must for the library of all bioinformatics users.

Walking amongst Dummys
I'm glad I bought this book and I will continue to refer to it. The remit of the Dummies series is to provide a guide to its subject matter without any great fuss. The text focuses on practical techniques without unnecessary diversion into the detail of molecular biology or computer science. In this respect it would have been a difficult book to author, readers having come from one discipline or the other. I agree with previous reviewers that this is well worth reading before doing a bioinformatics course or degree. Bioinformatics is a new field, and this book has delivered a useful introduction to it without recourse to expensive textbooks full of unreadable filler.

A great resource for teachers too!
I have used databases before (mostly NCBI, TIGR and SWISS PROT) and yet, this book (presumably for dummies) has shown me so much more(which say a lot about me)! It is accurate and gives good step by step guide to how to perform many tasks - from how to find a gene to using the analysis tools and to exploring some of the newer features of these databases - and the areas like you have never looked into before.
It is a well-researched book and the authors are clearly knowledgeable in this area.

Even though I have been for a 4-day bioinformatics course (6 months ago), which I thought was pretty good, this book still had so much to offer. Using this book, I was easily able to substitute the proteins of my interest into their examples and generated meaningful hits.

The book also covers deeper and more advanced features of BLAST, discusses sequence alignments using several types of algorithm and even has a section on 3D structures. Towards the end of book - it features a section on working with mRNA and building phylogenetics trees - which again are excellent resources for teachers involved in teaching beginners molecular biology.

I am a teacher teaching at a Pre-unversity level. The way the book is structured also lends its material to be modified into lesson materials for training students.

It is really a great book! Worth every dollar I spent on it!

Get this book first, before enrolling in an expensive course
This book will get you up and running on Bioinformatics in no time. I wish I got this book before I enrolled in a $$$$$ Bioinformatics course. I got more knowledge and information from this book $$$$$ than the course! And I am just in chapter 5 of the book and I'm more than half way through that $$$$$ course.

Great book-- Technical without the Computer-ese
I got this book a week ago because one of my profs offered to buy it for a volunteer who was willing to check it out and then make a recommendation on it to the rest of the class. I'm glad I volunteered, and I'm encouraging my classmates to get their hands on a copy. This book wasn't boring. It was completely hands on, and it addressed the topic from the perspective of a biologist, not a technophile-- which was exactly what I needed. It helped me reconcile my love for pure science with my increasing anxiety about needing to be so darn computer proficient to have any kind of job I can apply my degree to these days. I'm glad I got a hold of it early in the semester. I think it's going to really impact my grade in the class-- Oh, and my understanding of bioinformatics! ... Read more

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. ... Read more

Reviews (8)

A very nicely written book
This is THE book on string algorithms; covers all the normal exact match algs (Z, BM, KMP) and then goes on to discuss suffix trees in great depth (but with great clarity!). The second half of the book deals with inexact matching mostly using dynamic-programming-based algs. Some of the stuff generalizes nicely to non-string DP algs as well. Worth the investment just for increasing "algorithmic maturity", not to mention Gusfield's gift for clear exposition makes it a pleasant read.

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.

This book surely is worth its cost (even if you buy it on Amazon...:-)).

Excellent...but dense
This textbook gives a rigorous introduction to the algorithms of computational biology from the standpoint of theoretical computer science. It does however give the reader an overview of the practical application of these algorithms to the subject. The author gives a very detailed discussion of the most important results in the field, but the book is very dense: there are 228 definitions, 127 theorems, 490 references, and over 400 exercises that both illustrate the topics in the book and extend them. The author omits any real source code, but does give a URL where code for many of the algorithms can be found.

The author restricts his attention to deterministic approaches to string matching and comparison, and thus there is no treatment of hidden Markov models or Monte Carlo methods. The major algorithms such as the Aho-Corasick, Boyer-Moore, Knuth-Morris-Pratt, Needleman-Winsch, and Smith-Waterman are discussed and brilliantly motivated in the book. The author employs very effective diagrams to illustrate the matching concepts that are detailed in the book.

The book does require some time to read but it is worth the effort. Also, the exercises can be challenging but some should he done in order to understand the concepts in the book. The empirical results of the algorithms as sequence databases are also included, with FASTA, BLAST, BLOCKS, BLOSUM, and PROSITE are discussed in detail. The chapter that discusses these is the least mathematical of all the ones in the book and was no doubt included to connect the reader with real-world applications of the techniques in the book.

The last quarter of the book is a lot more trendy than the rest, with emphasis placed on algorithms for physical mapping, fragment assembly, and phylogenetic trees. These algorithms of course take on particular importance today given the Human Genome and other gene sequencing projects. Radiation-hybrid mappings, direct sequencing, and shotgun DNA sequencing are discussed in one of the chapters in this section, and the author addresses in great detail some approaches to speeding up sequence assembly. In the discussion on shotgun DNA sequencing the author refrains from any probabilistic analysis, instead referring the reader to the references. This omission goes along with the rest of the book, where probabilistic methods are not used, which is a little disappointing since these have shown great promise in computational biology. The exercises at the end of the chpater are very interesting and it is worth spending time working some of them through.

In a later chapter, the solution of the satisfiability problem in mathematical logic is discussed and shown to be solved (at least theoretically) by DNA-based computing. The quantities of DNA needed to carry out the computation are shown to be infeasible by the author.

This book will no doubt be of great assistance to those interested in the more rigorous approaches to computational biology. But the best attribute of the book is that one gets the impression that the author had a good time writing it, and that shows through in this very important book. ... Read more

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

Reviews (13)

Great book, easy to follow, expert authors
Five stars, a great place for people like me (trained as a biochemist) to start in a field that I know is going to be more and more important as to how I do my work in the future. I've been able to use basic things like BLAST more effectively, and finally understand that there are other ways to look at sequence besides BLAST and how to apply those tools to my own sequences. I really like the Entrez chapter, since Entrez does so much more than I ever realized it could do! I haven't ventured into the advanced territory yet (like microarrays), but at least I understand what I'm hearing in seminars now and what all those red and green spots actually represent.

I read the review by "a reader in Cambridge, MA", and don't understand what their beef is with this title. The authors have tried (and have succeeded) in pointing the readers to the best PUBLIC DOMAIN software out there, augmenting documentation that's generally lacking. Have you ever tried finding good docs on the NCBI Web site? Well, these two editors got them for you. UNIX-centric? I can't speak for the first edition, but check out the second edition and see that there's tons of Netscape screen dumps demonstrating the tools and making things as easy as possible for the reader. I originally bought this because of the reviews published in Science and Cell and a slew of other journals, all favorable, so the "reader in Cambridge" seems out of step with all of the published journal reviews of the book. Everyone's entitled to their opinion, but I just wanted to point this out for a sense of balance here, especially since my own experience was so different.

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 tor 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.

Bioinformatic for the beginner...
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
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 :) ... Read more

Amazon.com

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.

As odd as a "bioinformatics-as-tool" book may be to its research proponents, the reduction of bioinformatics to trade status both deflates and vindicates the years of research, as Tisdall's work attests. --Peter Leopold ... Read more

Reviews (16)

Decent intro to the subject
As the banner above the title of James Tisdall's Beginning Perl for Bioinformatics indicates, this book is 'an introduction to Perl for biologists.' What the banner doesn't mention is that it's also an introduction to biology and bioinformatics for Perl programmers, and it's also an introduction to both Perl *and* biology for people that have never really been exposed to either field. The author has clearly thought a lot about making one book to please these different audiences, and he has pulled it off nicely, in a way that manages to explain basic topics to people learning about each field for the first time while not coming off as condescending or slow-paced to those that might already have some exposure to it.

Superficially, this book isn't all that different from a lot of introductory Perl books: the Perl material starts out with an overview of the language, followed by a crash course on installing Perl, writing programs, and running them. From there, it goes on to introduce all the various language constructs, from variables to statements to subroutines, that any programmer is going to have to get comfortable with. Pretty run of the mill so far. Tisdall starts with two interesting assumptions, though: [1] that the reader may have never written a computer program before, and so needs to learn how to engineer a robust application that will do its job efficiently and well, and [2] that the reader wants to know how to write programs that can solve a series of biological problems, specifically in genetics and proteomics.

As such, there is at least as much material about the problems that a biologist faces and the places she can go to get the data she needs as there is about the issues that a Perl programmer needs to be aware of. The author introduces the reader to the basics of DNA chemistry, the cellular processes that convert DNA to RNA and then proteins, and a little bit about how and why this is important to the biologist and what sorts of information would help a biologist's research. The main sources of public genetic data are noted, and the often confusing -- and huge -- datafiles that can be obtained from these sources are examined in detail.

With the code he presents for solving these problems, Tisdall makes a point of not falling into the indecipherable-Perl trap: this is a useful language, well-suited to the essentially text-analysis problems that bioinformatics means, and he doesn't want to encourage the kind of dense, obscure, idiomatic coding style that has given Perl an undeservedly bad reputation. Some of Perl's more esoteric constructs are useful, and they show up when they're needed, but they're left out when they would only serve to confuse the reader. This is a good decision.

Rather, the focus is on teaching readers how to solve biological problems with a carefully developed library of code that happens to leverage some of Perl's most useful properties. The result is pretty much a biologist's edition of Christiansen & Torkington's Perl Cookbook or Dave Cross' Data Munging With Perl. The author presents a series of issues that a working bioinformaticist might have to deal with daily -- parsing over BLAST, GenBank, and PDB files, finding relevant motifs in that parsed data, and preparing reports about all of it. If a bioinformaticist's job is to be able to report on interesting patterns from these various sources, then following the programming techniques that Tisdall explains in clear, easy-to-follow prose would be an excellent way to go about doing it.

And when I say "programming techniques," note that I'm not specifically mentioning Perl. The code in this book is clear and organized, and all programs are carefully decomposed into logical subroutines that are then packaged up into a library file that each later sample program gets to draw from. Each new program typically contains a main section of a dozen lines of code or less, followed by no more than two or three new subroutines, along with calls to routines written earlier and called from the BeginPerlBioinfo.pm that is built up as the book progresses. Each sample is typically preceded by a description of what it's trying to accomplish and followed by a detaild description of how it was done, as well as suggestions of other ways that might have worked or not worked.

This modular approach is fantastic -- too many Perl books seem to focus so heavily on the mechanics of getting short scripts to work that they lose sight of how to build up a suite of useful methods and, from those methods, to develop ever-more-sophisticated applications. It isn't quite object-oriented programming, but that's clearly where Tisdall is headed with these samples, and given a few more chapters he probably would have started formally wrapping some of this code into OO packages.

If I have a complaint with the book, in fact, it's that Tisdall doesn't go any further: everything is good, but it ends too soon. Seemingly important topics such as OO programming, XML, graphics (charts & GUIs), CGI, and DBI are mentioned only in passing, under "further topics" in the last chapter. I also have a feeling that some of the biology was shorted, and the book barely touches upon the statistical analysis that probably is a critical aspect of the advanced bioinformaticist's toolbox. I can understand wanting to keep the length of a beginner's book relatively short, and this was probably the right decision, but it would have been nice to see some of the earlier sample problems revisited in these new contexts by, for example, formally making an OO library, showing a sample program that provided a web interface to some of the methods already written, or presenting code that presented results as XML or exchanged them with a database.

But these are minor quibbles, and if the reader is comfortable with the material up to this point, she shouldn't have a hard time figuring out how to go a step further and do these things alone. It's a solid book, and one that should be able to get people learning Perl, genetics, or both up to speed and working on real world problems quickly.

No need to have any previous programming knowledge
I had zero programming experience when I started reading this book. It allowed me, step by step, to get familiar with the language and start writing programs related to the field I am interested in.
It is fun and very helpful. You don't feel the frustration of being lost in the middle of unreadable code. The comments and explanations to the programs are great. It allows you to start learning the simple things first and then, as you get familiar with the language, go into more detail.
You can chose, as the author suggests, to go sometimes to the Perl documentation and read about the operators or functions introduced in the different programs; but what is great about the book is that you are given examples and exercises to use them. This is really the way to learn.

OK tutorial. Poor reference.
I have used this book in a beginning Perl programming course for biology majors. While it is good if you sift through it from start to the end, I often found it impossible to find things when I needed to go back to remind myself of something. The index does not help, and there is no concise language reference anywhere.

Also, I do not like the fact that it uses "quick and dirty" Perl (no "use strict" pragma). While it might be less confusing to skip it at the very beginning, very soon students start to waste too much precious class time trying to locate bugs that would make the program not compile with "use strict" in the first place (e.g. mistyped variable names).

Good intro for biologists;poor intro for computer scientists
"Bioinformatics" is the new sexy term for what used to be called simply "computational biology". Simply put, it involves pretty much any application of computation techniques to biological problems. The reason for the new nomenclature and the greatly increased interest in the topic is, like much in modern biology, a more-or-less direct consequence of the many genome sequencing projects of the last decade.

The consensus in the field seems to be that it's more productive (and certainly easier) to teach biologists how to program, rather than try to get programmers up to speed on the intracities of molecular biology. For similar reasons, Perl is a popular language to learn: it's easy to get off the ground and be productive with it, without requiring a heavy computer science background. (This, of course, has downsides as well...)

Never one to miss out on a trend, I'm going to be teaching a course on Bioperl and advanced Perl programming, starting next fall, which means I'm doing a lot of reading in this topic area, trying to develop lectures and find good background reading material. One of the first books I grabbed was _Beginning Perl for Bioinformatics_, which has been sitting on my "to read" shelf since O'Reilly sent me a review copy in December of 2001. It's a typical O'Reilly "animal" book (the cover bears three tadpoles), which does a decent job of introducing the basic features of the Perl language, and it should enable a dedicated student to get to the point where she can produce small useful programs. However, I'm not completely happy about the book's organization, and I think the occasional "if you're not a biologist, here's some background" interjections could have been cut without hurting anything.

The initial chapters in the book cover "meta" information, such as theoretical limits to computation, installing (or finding) the Perl interpreter on your computer, picking a text editor, and locating on-line documentation. Some general programming theory stuff is covered as well -- the code-run-debug cycle, top-down versus bottom-up design, the use of pseudocode. There's also some biology background, but it's very introductory level stuff -- DNA has four bases, proteins are made of 20 amino acids, and so on.

In chapter four, the book begins to get into actual Perl, with some coverage of string manipulation. Examples deal with simulating the transcription of DNA into RNA. Chapters five and six continue to flesh out the language, covering loops, basic file I/O, and subroutines. Chapter seven introduces the rand() function, in the context of simulating mutations in DNA. Subsequent chapters introduce the hash data type (using a RNA->protein translation simulation), regular expressions (as a way to store the recognition patterns of restriction endonucleases), and parsing database flat files and BLAST program output.

I'm clearly out of the target audience of the book, as I already have a strong working knowledge of Perl. Perhaps that's why I found the order that concepts were presented in to be a bit strange -- for example, hashes, which are a fundamental data type, aren't introduced until halfway through the book, and regular expressions (one of the key features of Perl) first appear even later. As I said above, I also found the biological background sections to be more distracting than anything, but I've also got a strong biology background, so perhaps I'm off base here too. That said, I think a person with a CS background would be better served with a copy of _Learning Perl_ and an introductory molecular biology text than with this particular book.

One of the things I did enjoy about the book were the frequent coding examples, all of which presented realistic computational biology sorts of problems and then demonstrated how to solve them. I'm sure that when I get around to writing lectures, I'll be leafing through this book looking for problems I can use in class.

Overall, recommended for biologists without programming experience who would like to get started using Perl for simple programming. Not recommended for people with computer science backgrounds looking to get into bioinformatics.

Great introduction
This is the first book that I read about Perl and also the first one that I read about Bioinformatics. I think it has an enough level of details so that readers who have little or no Bioinformatics background can easily understand the basics, and readers who wants to focus more on Perl usage in Bioinformatics can also get quick and useful examples such as interpreting BLAST output, and regular expressions. ... Read more

Book Description

The application of computational methods to DNA and protein science is a new and exciting development in biology. Bioinformatics: Sequence and Genome Analysis is a comprehensive introduction to this emerging field of study. The book has many unique and valuable features:

It is written for any biologist who wants to understand methods of sequence and structure analysis and how the necessary computer programs work

Sequence alignment, structure prediction, phylogenetic and gene prediction, database searching, and genome analysis are clearly explained and amply illustrated

Underlying algorithms and assumptions are clearly explained for the non-specialist

Examples are presented in simple numerical terms rather than complex formulas and notation

Theoretical underpinnings are linked to biological problems and their solutions

Extensive tables provide descriptions and Web sources for a broad range of publicly available software

Based on the author's extensive experience as a molecular geneticist and bioinformaticist at the University of Arizona, this is a uniquely educational book, ideal as a laboratory reference for investigators and also as teaching reference for graduate and undergraduate students studying this fast-changing discipline. ... Read more

Reviews (14)

good for computational biologist
If you are a biologist and just want to know some background information of how to apply bioinformatics to your research, do not read this book. My recommendation for you is "developing bioinformatics computer skills" and some other books like that.
If you are a student or scientist who study bioinformatics, this book is an excellent book and really worthy to read. This books gives very detailed information on algorithm to help us understand how the software such as BLAST and FASTA are designed. The illustrations are easy to understand compared with other books I have read, especially for the statistics part of any algorithm.
One weak point is that the book focus on nucleic acid sequence analysis while talk little about protein.

Horrible. A lot better books should be available nowadays
In short, the author does not have enough writing skill to write this text book.

I purchased this book a while ago. At that time, the book was really difficult to read. I thought that it is because I do not have enough knowledge to understand the material. So I stopped reading this book and studied bioinformatics by other means.

After gaining enough knowledge in bioinformatics, I re-opened this book, and it is funny to find that I still have the same amount of difficulty in understanding what the author wrote about topics that I have already built good understanding. Reading this book will only deteriorate one's understanding.

Several years ago, only just a few books were available on the market, so one needed to purchase this book. These days, there are lots of varieties to choose, and any choice is likely to be better than this book.

Horrible
While this book may, and I stress the word may, contain useful information, it is so badly written that it is incomprehensible. Dr. Mount seems to believe that ten words are better than one, making "Bioinformatics" very tedious to read. After awhile I felt like I was reading a Victorian novel.
Despite being wordy, the explanations are too brief and not clear. If you don't know what he is talking about before hand, you will never understand what he is explaining. He uses an excess of words, and rarely provides a clear, concise example of what he is referring to (or if he does it is in another chapter in the book).
It also appears that the book was never edited. For example, when trying to define "ortholog" and "homolog," he writes two opposing definitions for ortholog and none for homolog. Clearly this is a mistake and Dr. Mount accidentally used the word ortholog twice while meaning to use ortholog once and homolog the other time (pg 56). While it can be argued that this mistake is unimportant and the reader can look up the definitions, it makes me wonder what else in the book is wrong that I have no way of detecting (until I waste a bunch of time doing something incorrectly).

Strong foundation builder
This book will give you very strong foundations in
the basics of computation in the bio world. Though
this book does not give details of the computation
methods, it does give a very clear picture of math-
ematics and the science involved.

This book has a good coverage of FASTA and
BLAST. (Though a little bit short)

The programming techniques coverd are bare. Though
concepts like searching sequences using dynamic p-
rogramming are covered, you are better off reading
something like Proteome Research by wilkins et al.

I am yet to find a good book that deals only with
the technical and programming aspects of bio informatics
if you do find some thing interesting lemme know.

On the whole this book helped me understand a lot
about sequencing, alignment and prediction. The illustrations
and pictures provided are good and the text to the point.

If you are reading this review pls understand that I am
primarily a programmer trying to get into the
bio informatics business. I do not have any schooling
or degree or even experience in the bio informatics world.

Hope this helps

Santy

whatever
Decent qualitative overview. Some discussions of algorithms are so superficial that they are misleading. Slick presentation. Used at Stanford's intro to methods course - a good recommendation.

So far, the best there is for a survey course - but for depth and accuracy in sequence analysis algorithms, go to Durbin et al or Gussfield. ... Read more

Book Description

Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format. This book grew out of a need to teach bioinformatics to graduate students at the University of Pennsylvania. At the same time however, it is organized to appeal to a wider audience. In particular it should appeal to any biologist or computer scientist who wants to know more about the statistical methods of the field, as well as to a trained statistician who wishes to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, and will be accessible to students who have only had introductory calculus and linear algebra. Later chapters are immediately accessible to the trained statistician. Only a basic understanding of biological concepts is assumed, and all concepts are explained when used or can be understood from the context. Several chapters contain material independent of that in other chapters, so that the reader interested in certain areas can proceed directly to those areas.

Warren Ewens is Professor of Biology at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics, and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceeding of the Royal Society B and SIAM Journal in Mathematical Biology. He was recently awarded the Gold Medal of the Australian Statistical Society and elected as Fellow of the Royal Society. His research interests are in evolutionary population genetics, linkage analysis for human diseases, and bioinformatics.

Gregory Grant is a bioinformatics researcher at the University of Pennsylvania in the Computational Biology and Informatics Laboratory (CBIL), where he has been since 1998. In 1995 he received a Ph.D. in Mathematics from the University of Maryland and in 1999 a Masters in Computer Science from the University of Pennsylvania. His research interests are in bioinformatics in general and in particular in the statistical analysis of gene expression data and significance testing methods for IBD-mapping. ... Read more

Reviews (5)

Disappointing overview
This book is a tremendous disappointment, given other Amazon reviews and the impressive Table of Contents. I picked several topics about which I know something: Likelihoods, P-values, bootstraps. I would have had NO idea about either of these subjects based on the poor delivery in this book. Topics are not well introduced, there are virtually no examples, and the introduction/discussion of most topics is wordy and not informative.

A topic such as the two-sample t-statistic is scattered throughout the book, with the main part not even cited in the index!

Unfortunately there are not a lot of books in the field of Statistics in Bioinformatics. However, I would recommend "The Elements of Statistical Learning" (Hastie et al.) for classifiers etc (Duda and Hart's classic is also good). I would recommend "Biostatistical Analysis" by Zar for a general coverage, and Terry Speed's "stat Labs: Mathematical Statistics ..." which is not comprehensive but has good lab examples with associated statistical analysis.

Pretty good overview
This book is a timely introduction to the mathematical statistics used in computational biology and bioinformatics. The authors have done a superb job in the overview of a subject that students of biology and bioinformatics can rely on for study and for reference. The mathematics is done at an advanced undergraduate level, but the authors are pragmatic in their approach, and interlace the discussion with biological applications immediately after the appropriate mathematical background has been developed. It thus seems appropriate to discuss the quality of the presentation with these applications in mind.

Chapter one begins, appropriately, with an introduction to probability theory, with a consideration of discrete probability distributions of one variable beginning the chapter. The Bernoulli, binomial, uniform, geometric, generalized geometric, and Poisson distributions are discussed. The authors point out the use of geometric-like distributions in the BLAST application. The also caution the reader as to the difference between the mean and the average of a random variable. They then move on to consider continuous distributions, discussing briefly the uniform, Normal, exponential, gamma, and beta distributions. Moment-generating functions are also introduced, and they prove a "convexity" theorem for these functions that is important in the BLAST application. The authors also introduce the relative entropy and generalized support statistics, the later also being used in BLAST.

The next chapter is an overview of probability theory in many random variables. The results in chapter one are discussed in this context, and the authors give an interesting application to the sequencing of EST libraries. The authors also point out that the variance of the maximum of a collection random variables is finite as the number of variables increases, a fact that is used quite often in bioinformatics. Transformations of random variables are also discussed, with the goal of showing how these can be used to find the density function of a single random variable, this also being important in BLAST.

The most important subject of the book begins in chapter 3, wherein the authors introduce statistical inference. They begin with a very brief discussion of the differences between the frequentist and Bayesian approaches to statistical inference and then move on to classical hypothesis testing and nonparametric tests. This chapter is of great value to those readers, for example biologists/would-be bioinformaticists who are approaching statistics for the first time.

Chapter 4 introduces concepts that are of upmost importance in probabilistic computational biology, namely Markov chains. The discussion in this chapter sets up the strategies used in the next chapter on analyzing a single DNA sequence and a latter chapter on hidden Markov models. Shotgun sequencing is discussed as a tool to determine the an actual DNA sequence, and the authors discuss the probabilistic issues that arise in the reconstruction of long DNA sequences from shorter sequences. Missing in this chapter is a mathematical analysis of the advantages/disadvantages between shotgun and whole genome sequencing strategies.

Chapter 6 then generalizes the analysis of chapter 5 to multiple DNA and protein sequences. It is here that one begins to talk about alignments between sequences, which bring about some very subtle mathematical problems in computational biology. The computational complexity of the (global) alignment problem entails the use of softer techniques, such as dynamic programming, which is discussed in this chapter. The (local) alignment problem is also discussed in some detail, using the linear gap model. The alignment problem and the issues with scoring for protein sequences are also discussed in detail. The reader first encounters the famous PAM and BLOSUM matrices in this chapter. The authors do not discuss any connections with the protein folding problem, unfortunately.

The next chapter introduces the basic probability theory behind the BLAST algorithm, namely random walks. They do so with emphasis on moment generating functions, which might be a little abstract for the biologist reader.

The authors return to tatistical estimation and hypothesis testing in chapter 8, with maximum liklihood and fixed sample size tests discussed in some detail. Again connecting with the BLAST algorithm, the sequential probability ratio test is treated.

The authors finally get down to the BLAST algorithm in chapter 9, using an older version of the software (1.4). The connection of the algorithm with random walks and how to assign scores is immediately apparent, as is the ability of BLAST to do database queries against a chosen sequence. The algorithm is compared with the sequential analysis discussed in the last chapter.

The authors return to Markov chains in chapter 10, and give some numerical examples. In addition, they treat the important topic of Markov chain Monte Carlo via the Hastings-Metropolis algorithm, Gibbs sampling, and simulated annealing. An application of simulated annealing to the double digest problem is described. The authors also spend a litte time discussing continuous-time Markov chains.

Hidden Markov models are finally discussed in chapter 11. These have been the most effective tools in sequence analysis and the authors give a nice overview of their construction and properties in this chapter. The Pfam package is discussed as a software implementation of HMMs for determining protein domains. Unfortunately, they do not discuss the excellent package HMMER for implementing HMMs in sequence analysis.

Chapter 12 discusses computationally intensive methods in classical inference. One of these methods, the bootstrap procedure, which is used for large sample sizes, is described. Used to estimate confidence intervals in situations where there is not enough information to employ classical methods, the authors detail a method using quantiles to estimate the confidence interval for the standard deviation of the expression intensity of a gene. This is followed by a return to the multiple testing problem of chapter 3 in the context of the data analysis of expression arrays.

I did not read the last two chapters on evolutionary models and phylogenetic tree estimation so I will omit their review.

guide into the right direction
This is one of the books I have been waiting for. For a population geneticist who wants to learn bioinformatics, most texts are unacceptable: They present heuristic methods in a cookbook fashion, with little reference to what is going on biologically as well as mathematically.

This book is the first exception I know of. It builds, and rests on, solid foundations of genetic stochastic processes and still goes all the way to real-life problems. Let me illustrate this by means of an example, rather than enumerating all the topics in the book.

Chap. 14, entitled `phylogenetic tree estimation' (as opposed to the more common term `phylogenetic tree reconstruction' - not without reason, I presume) builds on, and is firmly interlaced with, Chap. 13 about `evolutionary models', which systematizes the zoo (if not jungle) of substitution models in both discrete and continuous time. On this basis, the overview of tree-building methods makes a lot of sense. Even better, it does not stop here, but presents an application (to real sequence data), followed by a careful analysis of where the various methods agree, and where - and maybe why - they disagree. This way, it clears away some common misconceptions; in particular, it presents a careful analysis of what bootstrap does and what it does not in this context. The chapter closes with a discussion of unresolved problems (like inhomogeneity of substitution rates), and methods and possible pitfalls related to testing of nested and non-nested hypotheses in tree estimation.

The book is written in an informal style without being imprecise, which makes it pleasant reading. It is particularly suitable for teaching at a high level. This is enhanced by realistic (and even real-life) examples that furnish the text, as well as carefully chosen exercises at the end of each chapter.

Certainly, this first edition of `Statistical Methods in Bioinformatics' cannot be the last word in this fast-moving field. But it is an excellent guide into the `right' direction.

poor delivery but potentially useful
The book is written for practicing statisticians who gave a good command of mathematical aspects of statistics. It presents classical topics in statistics (such as statistical inference, random variables and estimation theory) in a flavor of the author's impressions about bioinformatics. It is an excellent idea to present statistics that way. However I also feel that the authors failed to clearly distinguish statistical theory from its specific implementations. Departure from the typical definition-theorem-proof style of mathematical texts is hard on mathematically literate readers for whom this book was written in the first place. It is painful to try to find needed definitions and provable statements in the text even if formulas are numbered. In addition melanges of rigorous theorems with implementations of their consequences make parts of the book devoted to DNA sequence analysis difficult to read.

The authors appear not to have much personal experience with sequence analysis and their exposition seems to be dominated by suggestions from not very honest or objective colleagues. At least that much can be inferred from the list of references given at the end of the book and the content of sequence-analysis-oriented chapters 5, 6 and section 11.3 of chapter 11. On the other hand, chapters 9 (about BLAST statistics), 13 (about evolutionary models), and 14 (about phylogenetic trees) are excellent. Every practicing bioinformatician should read them as a required reading before doing anything with BLAST or with construction of evolutionary trees.

Chapter 12 about computationally intensive methods is also very well written. However, the authors fail to notify the reader that many of the methods (such as bootstrap) have a really bad reputation among researchers involved in sequence analysis. Perhaps at least one sentence of warning (with references) could be in order.

In summary: The book is a mixed blessing but I would recommend it to statisticians who desire to do some work in bioinformatics. I also believe that chapters 9, 13 and 14 should be read by all practicing bioinformaticians.

great book on hot new topic
This topic should be of prime interest to statisticians. The authors are mathematical biologists and they bring out the theory and methodology in probability and statistics that is applicable to DNA and protein sequencing and matching. They provide a treatment of probability, stochastic processes and statistics that starts with the very basics and builds up.

Topics include basic probability and statistical inference, Poisson processes and Markov chains, DNA sequencing, hidden Markov models, computer intensive methods, evolutionary models and phylogenetic tree estimation.

Of particular interest to me is the material on permutation methods and the bootstrap. The bootstrap has been applied in phylogenetics and there has been some controversy about its application there. The authors cover this in Chapter 14 where they appear to have a resolution for the controversy.

Permutation tests are first discussed in Chapter 3 "A Introduction to Statistical Inferrence" and are compared with other computer intensive methods in Chapter 12. In Section 12.3 they discuss the Behrens-Fisher problem pointing out why permutation tests are not possible due to the unequal variances. They give the bootstrap t solution. Section 12.2.2 gives a brief, but nicely described, account of bootstrap estimation and confidence intervals and provides a number of references including the following books: Efron and Tibshirani (1993), Davison and Hinkley (1997), Efron (1982), Hall (1992), Manly (1997), Sprent (1998) and Chernick (1999). Bootstrap and permutation approaches to multiple testing are covered in Section 12.4. ... Read more

Book Description

Wiley is proud to announce the publication of the first ever broad-based textbook introduction to Bioinformatics and Functional Genomics by a trained biologist, experienced researcher, and award-winning instructor. In this new text, author Jonathan Pevsner, winner of the 2001 Johns Hopkins University "Teacher of the Year" award, explains problem-solving using bioinformatic approaches using real examples such as breast cancer, HIV-1, and retinal-binding protein throughout. His book includes 375 figures and over 170 tables. Each chapter includes: Problems, discussion of Pitfalls, Boxes explaining key techniques and math/stats principles, Summary, Recommended Reading list, and URLs for freely available software. The text is suitable for professionals and students at every level, including those with little to no background in computer science. ... Read more

Reviews (3)

Highly Recommended
"...an intriguing work targeted toward biologists wanting to solve problems...provides a compendium of many biological insights and breakthroughs and will be a useful resource...highly recommended." (Choice, Vol. 41, No. 7, March 2004)

Excellent for bioinformatics from a user's perspective
Unlike the previous review, I found the user perspective, rather than the mathematical perspective refreshing. I have been teaching bioinformatics to CS students for several years and all too often the students are great at algorithms and theory but do not understand the user they are designing for. This book teaches just that -- how to use bioinformatics from a user or researcher's viewpoint. Medical students and biologists will find it useful for direct applicability to their work, but I also reccomend it for bioinformatics students who need to complement their theoretical background with practical use. All too often, CS students of bioinformatics can design a great database with powerful access tools, but with a horrible interface because they don't have this perspective.

Now, for the book itself. It is easy to read and covers all aspects of bioinformatics from a sequence perspective (information retrieval, BLAST, gene expression and microarrays, proteomics and protein bioinformatics, genomes and disease). The coverage of databases and URLs is thourough and the text is easy to read, yet useful. The book is comprehensive with one area seemingly missing -- it would have been useful to include a chapter on systems biology and/or cellular modeling and the tools available (i.e. E-Cell). The book is especially useful to a researcher who is trying to explore all aspects of a particular gene, protein, disease, or pathway using bioinformatics tools.

The book is in stark contrast to the other Pevser (that is Pevzner) who wrote a bioinformatics book that surveyed algorithm theory underlying bioinformatics.

This book is also useful for less technical professionals in industry -- the managers, lawyers and venture capitalists that pervade the biotech landscape all need to communicate effectively and they can surely learn that here, provided they have some background in cell biology first.

Bioinformatics for computational dummies
A genious attempt to present bioinformatics as if it is a discipline without any computational content. Perfect for students who lost any hope to understand what is the engine driving bioinformatics tools but want simply to memorize how to use them instead. Must be a very comfortable reading for biologists but is as exciting as a long carefully designed restaurant menu for a mathematician. If the author wants to raise a new generation of biologists with this book then biology and *real* bioinformatics will be divorced forever. ... Read more

Book Description

Discovering Genomics, Proteomics, and Bioinformatics combines integrated Web exercises with a problem-solving approach to train readers in basic hands-on genomic analysis. The authors present global problems, then provide the tools of genomic analysis to help readers dissect the answer, thus encouraging critical thinking skills. Short boxed readings called "Math Minutes" explain the math behind the biology.For anyone interested in genomics, proteomics, or bioinformatics. ... Read more

Reviews (1)

A novel approach!
Abstract: great and innovative book. I have seen many books, but none like this. It is still concise in this first edition, yet could become the "Lewin" of genomics.
Score: 9/10.

Recommended to students: yes, together with classic works like Brown.

Recommended to Central Library: yes.

1. The supplied CD-ROM is a nice teaching aid. Yet, it is difficult to "extract" pictures from it for teaching purposes. It would be much more useful if the pictures were individually supplied in standard high-quality graphic formats like TIFF, instead of PDF. The later is perfect for distributing text with pictures, but not to retrieve such pictures. Other publishers distribute the book artwork as individual TIFF files. That approach greatly enhances the book and boost sales. This is particularly useful for teachers. Actually, it is a must for us these days. Please, make sure that future versions of the CD-ROM or DVD-ROM are --as this one-- compatible with the open-source Unix-based Mac OS X platform. Thanks.

2. The associated web page "Instructor's Guide"3. The discovering questions are terrific. Please, expand them in future versions.

4. Math minutes are an excellent idea.

5. Boxes are welcome. Please, include more.

6. Also helpful are the boldface words on each chapter. Perhaps they could be also included in a keywords at the beginning of each chapter.

7. The index should be more comprehensive and should have all main entries in boldface. This is important to any index and very few books have it right.

8. The glossary is helpful. It should be more comprehensive,
including more terms.

9. The summaries and conclusions are great, yet should be expanded to include more relevant information. They should be like a "minichapter" an the end of each chapter or --better-- at the beginning. All partial summaries could be pooled into a larger summary that way.

10. Addendum sections could be included as separate notes or boxes.

11. The pronunciation tips for new words are also an excellent idea; mostly for non-English speakers.

12. The classified references are really useful. Well done. If they were commented or "annotated" they would be just perfect.

13. A list of abbreviations would be welcome. A list sorted by the full name would be very handy as well.

14. What about telomerase and aging? What about the fact that
unicellular organisms are immortal? Or stem cells? Or tumor cells? Death is a tax that multicellular organisms have to pay to nature in order to evolve. Yet we humans might change that soon.

15. It should be clearly indicated the organisms with genomes made of dsDNA, ssDNA, dsRNA and ssRNA.

16. Missing bioinformatics tools and step-by-step analysis of genes and mRNA (see next) and whole genomes.

17. It would be really helpful to explain clearly and analyze --even from a bioinformatics point of view-- the structure of genes, mRNA, CDS, introns, exons, promoters and terminators. It is not clear where do these elements start or end or how to recognize them. Diagrams and graphs would greatly help to explain these absolutely basic and fundamental concepts. In other words, imagine that you have cloned and sequenced a genomic gene as well as a full mRNA (cDNA). Now you want to publish your results and for that you do a comprehensive description of your gene (chromosome) and cDNA (mRNA). That is precisely the kind of information that is missing as a diagram and explanation. In this way, it should be indicated that you may encounter several ATG (or other) starting coding triplets in the mRNA, that if the 20 or so amino acid residues of the 5'-end of a peptide have a high percentage of hydrophobic residues, they are likely part of a leading peptide which would be further excised, that you may encounter several polyadenylation signals, etc. On the genome side, the promoter and terminator structures should be analyzed, as well as the intron-exon boundaries.

18. Likewise, it should be indicated the tools and current
possibilities to determine or predict the 3D structure of a protein (folding) from the primary structure of the peptide.

19. Does not mention Lasergene package of DNAStar20. Which genes are best to draw dendrograms? Differentiation between genes from the nucleus or organelles (mitochondrion or chloroplast). Likewise for DNA fingerprinting and molecular markers.

21. Differential display methodologies are missing (as well as other methodologies of gene expression like subtractive hybridization).

22. Large-scale sequencing is missing. For instance, sequencing of single-molecules will allow the sequencing of whole chromosomes or genomes.

23. Missing tables comparing different genomes with full details
(size, ploidy, percentage of genes, introns, exons, repetitive DNA, junk DNA, etc).

24. Reference to manufacturers is very useful. Please, include also links to web sites. Best if all manufacturers are included as an appendix.

25. All web sites (NCBI, etc) and web-based applications (BLAST, ORF Finder, etc) should be clearly indicted in an appendix.

26. It is not indicated that the PCR was in fact described with full details by Khorana et al 14 years before Mullis et al.

27. Please, include more drawings and pictures in the printed book and CD-ROM.

28. Suggestion: including chapters on eukaryotic-genomic DNA
libraries, cDNA libraries, subtractive libraries.

29. Suggestion: including chapters on plant and animal transformation.

30. Suggestion: including drawing of Maxam-Gilbert sequencing method and Sanger method (Applied Biosystems electropherograms,
electrofluorograms).

31. Prions, viroids and viruses could be also included.

32. A title index at the beginning of each chapter would be very
useful. Besides the goals for chapter, which are quite useful.

33. Bioinformatics could be significantly expanded.

34. QuickTime videos explaining some topics would be fantastic.
Please, make them in QuickTime (best quality, platform-independent).

35. All in all, a great novel approach. Keep up the great work! ... Read more

Book Description

An introductory text that emphasizes the underlying algorithmic ideas that are driving advances in bioinformatics. ... Read more

Book Description

Mastering Perl for Bioinformatics covers the core Perl language and many of its module extensions, presenting them in the context of biological data and problems of pressing interest to the biological community.This book, along with Beginning Perl for Bioinformatics, forms a basic course in Perl programming.This second volume finishes the basic Perl tutorial material (references, complex data structures, object-oriented programming, use of modules--all presented in a biological context) and presents some advanced topics of considerable interest in bioinformatics. Biologists and computer scientists who have conquered the basics of Perl and are ready to move even further in their mastery of this versatile language will appreciate the author's well-balanced approach to applying Perl's analytical abilities to the field of bioinformatics. Full of practical examples and real-world biological problem solving, this book is a must for any reader wanting to move beyond beginner level Perl in bioinformatics. ... Read more

Reviews (2)

Nice survey of topics, but not too deep on any one thing
"Mastering Perl for Bioinformatics" is the follow-up to Tisdall's earlier "Beginning Perl for Bioinformatics". Both books are part of O'Reilly's lauded "animal books" series; "Beginning" was graced with tadpoles, while "Mastering" sports a frog.

Naturally, this book picks up where the earlier one left off, diving headfirst into the details of Perl modules. Chapter two is a quick pass over some basic data structures, with discussion of how you'd implement each in Perl. Subsequent chapters cover object-oriented programming in Perl, using Perl with relational databases, using Perl with web services, generating graphics on the fly with Perl, and the use of the Bioperl suite of libraries.

As might be expected, all the coding examples in the book are drawn from reasonably realistic bioinformatics situations. There's a little bit less hand-holding on the biological side in this book, relative to the earlier volume -- which I think is a good idea, as it gives more space to focus on the programming material.

The one weakness of this book is that it covers quite a few topics, which means that it doesn't really go into great depth on any of them. The "survey" approach is well done, and it's very nice to have biologically relevant examples and exercises for the breath of material that is addressed, but I think the book might have been stronger if it forewent the "Perl and the Web" and "Perl and Graphics" chapters in favor of more focus on the Bioperl libraries.

If you're a bioinformatics programmer who enjoyed "Beginning Perl for Bioinformatics", and you want to get a better idea of what more advanced Perl programming looks like and what sorts of things you can do with Perl, this book is a nice place to start. However, if you're looking for more specific information, other more focused books might be a better choice, if you can live without the biologically focused code examples.

Not bad
Basically, this book further develops the author's previous work "Beginning Perl for Bioinformatics" on procedural Perl scripting to object-oriented Perl programming.

You will learn the OOP aspects of Perl in the context of biological problems. For example, this book explains the OOP concepts by developing a Gene class that stores information about a gene. In other books such as Conway's "Object oriented Perl", CD::Music class is given as an example, which I found very boring.

The leap from procedural Perl scripting to object-oriented Perl programming is nontrivial to learn, and I'm sure that this book helps.

Bioperl is the subject of chapter 9 (about 40 pages), and it could have been better if there were more thorough treatment about this module.

Readers may also find useful the chapters 6 (DBI), 7 (CGI), and 8 (GD).

If you are new to Perl, read first "Beginning Perl for Bioinformatics", then this book, because this book assumes that you already have the level of Perl knowledge that can be acquired by that previous book. ... Read more

Book Description

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods.The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes.The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).