Beginning Perl for Bioinformatics [Paperback]

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.

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).

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.