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Beginning Perl for Bioinformatics
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.
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I felt the same about the lack of statistical approaches. While this book is probably great for biologists just learning to write code, for coders entering the field (bioinformatics) it contains too little biology or math to be really educational. My opinion.
What I'd love would be a dissection of the construction of various motif analysis tools, critiquing various impl's of HMMs, really going into detail. This seems like a perfect complementary work to OSS, so I might even find one, someday...
1) It is good for biologists who wants to learn how to program
2) It is not good for programmers who want to learn biology
Obviously, my friends disagree with reviewer Babbage on this point. However, a quick look on Amazon reveals that most reviewers who found the book interesting are biologists with no programming experience instead of the other way round.
"If you think education is expensive, try ignorance" - Derek Bok
Bioinformatics is probably the biggest challenge facing the biological sciences in the next few years. Its becomming more and more apparent that even slight changes in very small elements of a system (i.e., a small sequence of a protein, the behavior of a single neuron within a group of 10,000) can have a drastic effect on the behavior of the entire system. As a result, to really study the problem, you have to aquire massive amounts of data. For example, in our lab we routinely collect data from 64 channels of 16-bit data (monitoring neuron firing in culture) at 1KHz, in addition, we're simultaneously taking calcium imaging video at 100fps at 256x256 (at 256 colors). This results in about 200 MB of data gathered every second. Considering we run tests for over 10 minutes, just aquiring and storing this data is a challenge, but finding useful methods to analyze it is even more difficult. Its refreshing to see texts being written on how to bridge the gap between comp. sci. and biology. I've been working in the area for about 4 years now, and its really great to see the field growing and getting more mainstream attention.
Some men spend their entire lives trying to kill themselves for having been born. --Ross MacDonald
Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology by Dan Gusfield is usually very liked for people with a computer science background. And it's not only of use if you want to go into bioinformatics: most algorithms on strings are usable in everyday coding too.
"If you think education is expensive, try ignorance" - Derek Bok
Human Molecular Genetics 2: Looks to be a great primer on all the biology background.
Bioinformatics: A Practical Guide...: This book is a detailed tour of the online databases and existing tools for analysis of genes and proteins.
Algorithms on Strings, Trees and Sequences: This is a book for real computer science types who want to do high-performance implementations of new tools.
At Purdue University, there is a class specifically meant for CS majors and Biology majors, to address this same issue. I wonder if they use this book in the class.
Moderation: Put your hand inside the puppet head!
The BioPerl project (http://bio.perl.org/) has been going on for some time.
In their own words they are, "The Bioperl Project is an international association of developers of open source Perl tools for bioinformatics, genomics and life science research."
There bioinformatitians can find a wealth of useful Perl scripts and modules to use in their efforts.
Yet another example of an open source initiative serving the needs of science!
I would like to answer several questions that were raised in this discussion.
(1) How does a CS person learn biology? I recommend "Recombinant DNA, A short Course", as an accessible (Scientific American style) introduction to the cloning breakthroughs and discoveries that lead to genome science.
(2) How does a CS person learn "Bioinformatcs"? I strongly recommend "Bioinformatics - Sequence and Genome Analysis" by David Mount as an accessible and extremely comprehensive survey of current approaches in Biological Sequence Analysis.
(3) Why do Biologists use Perl? Much of the information Biologists want is on the WWW, and Perl's LWP makes it extremely easy to get it. We don't use Perl for sophisticated text analysis (similarity searching, motif searching, etc) because the algorithms that are appropriate are typically not exact (or even regular expression) matches. But it's difficult to beat Perl for getting stuff off the WWW.
(4) Why do Biologists use Flat files? Several reasons - (a) the most useful information is sequence information, and it can be read much more quickly out of a flatfile (esp. one that is memory mapped) than a DB; (b) flat files solve some versioning problems that DB's make very complex and slow. (c) Most data providers only provide flatfiles. This will change, however, over the next 2 - 3 years, mySQL and postgresQL are moving into biology labs.
It is very exciting that Bioinformatics has high visibility now, and many people with CS background are considering bioinformatics problems. Unfortunately, many of the introductory books on bioinformatics (particularly the O'Reilly books) do not adequately present the substantial foundations of bioinformatics that have been build over the past 15 - 20 years, and some newcomers are mislead into believing there are simple problems looking for a few good programmers. Most of the simple problems have been solved; many of the complicated problems are challenging not because we do not know enough CS, but because we do not know enough biology.