A response to "The myths of bioinformatics software"

This is a response to (parts of) Dr. Lior Pachter's post, "The myths of bioinformatics software". (You can also see my post on bioinformatics software licensing for at least some of the background arguments.)

I agree with a lot of what Lior says: most bioinformatics software is not very good quality (#1), most bioinformatics software is not built by a team (#2), licensing is at best a minor component of what makes software widely used (#3), software should have an expiration date (#5), most URLs are unstable (#6), software should not be "idiot proof" (#7), and it shouldn't matter whether you use a specific programming language (#8).

I strongly disagree with Lior's point #4, in almost every way. I try make my software free for everyone, including companies, for both philosophical reasons and for simplicity; I explained my reasoning in my blog post. (Anyone who doesn't think linking against GPL software is reasonably complicated and nuanced should through the tweets and comments on that post!) From my few involvements with working on non-free software, I would also add that selling software is a tough business, and not one that automatically leads to any profits; there's a long tail, just as with everything else, and I long ago decided that my time is worth more to me than the expected income from selling software would be. (I would be thrilled if a student wanted to try to make money off of our work, but my academic work would remain open source.)

Regardless, Lior's opinion isn't obviously wrong, and I appreciate the discussion.

What surprises me most about Lior's post, though, is that he's describing the present situation rather accurately, but he's not angry about it. I'm angry, frustrated, and upset by it, and I really want to see a better future -- I'm in science, and biology, partly because I think it can have a real impact on society and health. Software is a key part of that.

Biology and genomics are changing. Large scale data analysis is becoming more and more important to the biomedical sciences, and software packages like kallisto and khmer are almost certainly going to be used in the clinic at some point. (I believe some of Broad's variant calling software is already used in diagnosis and treatment for cancer, for example, although I don't know the details.) Our software is certainly being used by people doing basic biomedical research, although it may not be directly clinical yet - and I think the quality of computation in basic research matters too.

And this means bioinformatics should grow up a bit. If bioinformatics is a core component of the future of biology (which I think is obvious), then the quality of bioinformatics software matters.

To quote Lior, "Who wants to read junk software, let alone try to edit it or build on it?" Certainly not me - but then why are we producing it? Are we settling for this kind of software in biomedical research? Are we just giving up on producing decent quality software altogether, because, uh, it's hard? How is this different from doing bad math, or publishing bad biology - topics that Lior and others get really mad about?

Lior also quotes a Computational Biology interview with James Taylor, who says,

A lot of traditional software engineering is about how to build software effectively with large teams, whereas the way most scientific software is developed is (and should be) different. Scientific software is often developed by one or a handful of people.

That was true in a decade ago, and it may have been a reasonable reason to avoid using decent software engineering techniques then, but the landscape has changed significantly in the last decade, with a wide variety of rapid prototyping, test-driven development, and lean/agile methodologies being put into practice in startups and large companies. So I think James is mistaken here.

I wager that the reason a lot of scientists do bad software engineering is because they can get away with it, not because there are no techniques they could profitably use. Heck, if they wanted to learn something about it, Software Carpentry will come teach workshops for you on this very topic, and I'd be happy to offer both Lior and James a workshop to bring them up to speed. (Note: I don't think either of them needs my advice, which is actually kind of my point.)

(As for languages, Lior's point #8, there is a persistent expansion of the Python and R toolchains around bioinformatics and a convergence on them as the daily workhorses of bioinformatics data analysis. So even that's changing.)

Fundamentally the blithe acceptance of badly engineered software in science baffles me. I can understand (and even endorse) not requiring good software engineering for algorithmic proofs of concept, but clearly we want to have good, robust libraries for serious work. To claim otherwise would seem to lead to the conclusion that much of bioinformatics and genomics should seek to be incorrect and irrelevant.

I want there to be a robust community of computational scientists and software developers in biology. I want people to be able to build a new variant caller without having to reimplement a FASTQ or SAM parser. I think we need people to file bug reports, catch weird off-by-one problems, and otherwise spot check all the software they are using. And I don't think it's impossible or even terribly difficult to achieve this.

The open source community has been developing software with distributed teams, with no single employer, and with uncertain funding for decades. It's not easy, but it's not impossible. And in the end I do think that the open source community has a lot of the solutions the computational science community needs, and in fact is simply a much better exemplar for how to work reproducibly and with high technical quality. Why we continue to ignore this mystifies me, although I would guess it has to do with how credit is assigned in academic software development.

If we went back to the 80s and 90s we'd see that many of the same arguments that Lior is making were applied to open source software in contrast to commercial software. We know how that ended - open source software now runs most of the Internet infrastructure. And open source has had other benefits, too; to quote Bill Janeway, "open source and the cloud have dramatically decreased the friction of innovating", and the scientific community has certainly benefited from the tremendous innovation in software and high tech. I would love to see that same innovation enabled in genomics and bioinformatics. And that's why we try to practice good software development in my lab; that's why we release our software under the BSD license; and that's why we encourage people to do whatever they want with our software.

Ultimately I think we should develop our software (and choose our licenses), for the future we want to see, not the present that we're stuck with.


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