A farewell to bioinformatics (2012)

John Graham-Cumming (jgrahamc here) co-authored a piece on making scientific code open. It was received well-enough that Nature published it [0]. This approach has inspired others to do better work by describing a concrete problem, then outlining steps to fix it on an individual and institutional level.<p>When someone finds fault with the way a field conducts itself, I would implore them to constructively influence that field. You might be surprised how many are actually sympathetic to your concerns.<p>I'm not dismissing this author's concerns: to do that would really require knowing the molecular biology field (which is more than sequencing, it turns out). I do neuroscience right now, and programming can be a problem for some. But a constructive suggestion to change can have much more impact than a long rant.<p>[0] <a href="http://www.runmycode.org/data/MetaSite/upload/nature10836.pdf" rel="nofollow">http://www.runmycode.org/data/MetaSite/upload/nature10836.pd...</a>

&#62; the software is written to be inefficient, to use memory poorly, and the cry goes up for bigger, faster machines! When the machines are procured, even larger hunks of data are indiscriminately shoved through black box implementations of algorithms in hopes that meaning will emerge on the far side. It never does, but maybe with a bigger machine…<p>I spent five years working in bioinformatics, and this is exactly the attitude of both the researchers and the other developers on the projects I worked on. It was very frustrating.

I have some experience working at a genomics research company and I'll broadly +1 Fred's experience about the industry, although in less negative terms. I got out before I got jaded, so my perspective is a bit more "oh, that's a shame" than his. I really like genetics, bioinformatics, hardware, deep-science, and all that but the timing and fit wasn't right.<p>The tools are written by (in my experience) very smart bioinformaticians who aren't taught much computer science in school (you get a smattering, but mostly it's biology, math, chemistry, etc.). Ex:<p><a href="http://catalog.njit.edu/undergraduate/programs/bioinformatics.php" rel="nofollow">http://catalog.njit.edu/undergraduate/programs/bioinformatic...</a><p><a href="http://www.bme.ucsc.edu/bioinformatics/curriculum#LowerDivisionRequirements" rel="nofollow">http://www.bme.ucsc.edu/bioinformatics/curriculum#LowerDivis...</a><p><a href="http://advanced.jhu.edu/academic/biotechnology/ms-in-bioinformatics/course-requirements/index.html" rel="nofollow">http://advanced.jhu.edu/academic/biotechnology/ms-in-bioinfo...</a><p>The tools themselves are written by smart non-programmers (a very dangerous combination) and so you get all sorts of unusual conventions that make sense only to the author or organization that wrote it, anti-patterns that would make a career programmer cringe, and a design that looks good to no one and is barely useable.<p>Then, as he said, they get grants to spend millions of dollars on giant clusters of computers to manage the data that is stored and queried in a really inefficient way.<p>There's really no incentive to make better software because that's not how the industry gets paid. You get a grant to sequence genome "X". After it's done? You publish your results and move on. Sure, you carve out a bit for overhead but most of it goes to new hardware (disk arrays, grid computing, oh my).<p>I often remarked that if I had enough money, there would be a killing to be made writing genome software with a proper visual and user experience design, combined with a deep computer science background. My perfect team would be a CS person, a geneticist, a UX designer, and a visual designer. Could crank out a really brilliant full-stack product that would blow away anything else out there (from sequencing to assembly to annotation and then cataloging/subsequent search and comparison).<p>Except, I realized that most folks using this software are in non-profits, research labs, and universities, so - no, there in fact is <i>not</i> a killing to be made. No one would buy it.

I sympathize with the author, but this piece fails because many of the specific criticisms are off-base, and he's not trying to be at all constructive.<p>For example, it isn't true at all that microarray data is worthless. The early data was bad, and it was very over-hyped, but with a decade of optimization of the measurement technologies, better experimental designs, and better statistical methods, genome-wide expression analysis became a routine and ubiquitous tool.<p>The claim that sequencing isn't important is ridiculous. It's the scaffold to which <i>all</i> of biological research can be attached.<p>However:<p>There is a great deal of obfuscation, and reinventing well-known algorithms under different names (perhaps often inadvertently). There's also a lot of low-quality drivel on tool implementations or complete nonsense. This is driven largely by the need in academia to publish.<p>The other side of this problem is that in general, CS and computer scientists don't get much respect in biology. People care about Nature/Science/Cell papers, not about CS conference abstracts. Despite bioinformatics/computational biology not really being a new field anymore, the cultures are still very different.

I agree with him, and have been complaining about the same shit for ages (I work in bioinformatics too). Sadly, biologists don't care. We're treated as the number crunchers. The real problem isn't that we waste computational resources, it's that many biologists download programs, run their data through it, and if it spits out an answer rather than an error, they trust it. Since that program probably has zero unit test coverage, and the results may be fed into pharmaceutical decisions, disease diagnostics, etc, you're basically fucked if something went wrong. Lots of us have said this[0].<p>Minor quibble: genome assembly is definitely still an open problem that's computationally difficult. So is robust high dimension inference, but that falls more under statistics.<p>I've wanted to leave at least a dozen times too, for the better pay, for working with programmers that can teach me something, and to not have my work be interrupted by academic politics. But the people pissed at the status quo are the ones that are smart enough to see it's broken and try to fix it, and if we all leave, science is really fucked.<p>[0] <a href="http://www.johndcook.com/blog/2010/10/19/buggy-simulation-code-is-biased/" rel="nofollow">http://www.johndcook.com/blog/2010/10/19/buggy-simulation-co...</a>

If you really want to get a feel for how deluted the Bioinformatics community is, look for a job in the field as an outsider. It's not uncommon to see requirements like:<p>"Must be an expert in 18 technologies" "Must have a PHD in Computer Science or Molecular Biology" "Must have 12 years experience and post doctoral training" "Pay: $30,000"<p>It's delusional because they apply the requirements it took for themselves to get a job in Molecular Biology (long PHD, post doc, very low pay for first jobs) and just apply it carte blanche to all fields that may be able to aid in their pursuits. Especially when it comes to software engineering where it can often be extremely difficult to explain why you did not pursue a PHD.

This is pretty hilarious, from my brief experience with bioinformatics I can very well imagine someone writing the opposite rant, about CS people getting into bioinformatics not knowing sh*t about biology. I mean, browse through bioinformatics textbooks, those are either written by computer scientists and those are little more than string algorithm textbooks or by biologists and then the layer of jargon for someone coming from CS is just impenetrable. Same with bioinformatics teachers, I come from a CS background, but spent one solid month seriously trying to understand the basics of molecular biology and my bioinformatics seminar instructor sometimes seemed to know less about it than me. Terrifying, no wonder nonsense results are produced.

I always feel awkward reading these rants, mainly because I've burned my bridges before and it really wasn't worth it. Even if it is true, it's better to leave it and move on.<p>If you really feel strongly about something, write it dispassionately (normally some time after the event) and treat it like a dissertation, backed with case studies and citations.

Basic science moves forward slowly limited by the pace of fortuitous discoveries. I have found that many people from the field of computer programming have unrealistic expectations of what can be done in biology and other sciences.

Sounds like a fed up academic with a stick up his backside.<p>Sh*tty data? Comes from the community. If the data and algorithms are so poor, and the author so superior, he should have been able to improve the circumstances.<p>This whole screed reads like an entitled individual who entered a profession, didn't get the glory, oh and yeah, academia doesn't pay well.<p>In the realm of bioinformatics, lets ignore the work done on the human genome and the like.

My experience working as a scientific programmer is this: my colleagues aren't forthcoming. I could list case after case of failure to document or communicate crucial details that cost me days, weeks and even months of effort. But I won't, until I have another job lined up. If I were in the author's position (I'm in another field), I would insist that my colleagues--all of them, in whatever field I ended up working, were forthcoming about their work. This is non-negotiable. Being over-busy is no excuse. (It may be an excuse for not being forthcoming, but right or wrong, I couldn't care less--I would not work with such people if I could avoid it, for whatever reason.)<p>Academia rewards journal publication and does not adequately reward programming and data collection and analysis, although these are indispensable activities that can be as difficult and profound as crafting a research paper. At least the National Science Foundation has done researchers a small favor by changing the NSF biosketch format in mid-January to better accommodate the contributions of programmers and "data scientists": the old category <i>Publications</i> has been replaced with <i>Products</i>.<p>Naming is important to administrators and bureaucrats. It can be easy to underestimate the extent to which names matter to them. Now there is a category under which the contribution of a programmer can be recognized for the purpose of academic advancement. Previously one had to force-fit programming under <i>Synergistic Activities</i> or otherwise stretch or violate the NSF biosketch format. This is a small step, but it does show some understanding that the increasingly necessary contributions of scientific programmers ought to be recognized. The alternative is attrition. Like the author of the article, programmers will go where their accomplishments are recognized.<p>Still, reforming old attitudes is like retraining Pavlov's dogs. Scientific programmers are lumped in with "IT guys." IT as in ITIL: the platitudinous, highly non-mathematical service as a service as a service Information Technocracy Indoctrination Library. There is little comprehension that computer science has specialized. For many academics, scientific programmers are interchangeable IT guys who do help desk work, system and network administration, build websites, run GIS analyses, write scientific software and get Gmail and Google Calendar synchronization running on Blackberries. It is as if scientists themselves could be satisfied if their colleagues were hired as "scientists" or "natural philosophers" with no further qualification, as opposed to "vulcanologist" or "meteorologist" (to a first order of approximation).

"I’m leaving bioinformatics to go work at a software company [...]"<p>"[bioinformatics] software is written to be inefficient, to use memory poorly, and the cry goes up for bigger, faster machines! [...]"<p>Well, the author is heading for a very bitter surprise...

You know, I'd be more inclined to listen to him if he didn't also completely decry almost all of modern biology, which (in my view) has been to the late 20th and early 21st centuries what physics was to the late 19th and early to mid 20th centuries.

I spent a year in a bioinformatics PhD program and got the feeling I was studying to be science's version of the business analyst. Not knowing enough about the biology or computation, but expected to speak the language of both. And what would my research consist of in such an applied science? Luckily I had another opportunity and became a software developer (which I'm happy with). The worst thing about the experience was listening to so many research presentations where I could tell the presenter didn't understand the science and could barely explain it.

Some thoughts on this article:<p>- This guy clearly has a limited understanding of the field. This quote is laughable: "There are only two computationally difficult problems in bioinformatics, sequence alignment and phylogenetic tree construction."<p>- As a bioinformatician, I feel sorry for this guy. Just like any other field, there are shitty places to work. If I was stuck in a lab where a demanding PI with no computer skills kept throwing the results of poorly designed experiments at me and asking for miracles, I'd be a little bitter too.<p>- Just like any other field, there are also lots of places that are great places to work and are churning out some pretty goddamn amazing code and science. I'm working in cancer genomics, and we've already done work where the results of our bioinformatic analyses have <i>saved people's lives</i>. Here's one high-profile example that got a lot of good press. (<a href="http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-for-leukemia-glimpses-of-the-future.html?pagewanted=all&#38;_r=0" rel="nofollow">http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-...</a>)<p>- I'm in the field of bioinformatics to improve human health and understand deep biological questions. I care about reproducibility and accuracy in my code, but 90% of the time, I could give a rat's ass about performance. I'm trying to find the answer to a question, and if I can get that answer in a reasonable amount of time, then the code is good enough. This is especially true when you consider that 3/4 of the things I do are one-off analyses with code that will never be used again. (largely because 3/4 of experiments fail - science is messy and hard like that). If given a choice between dicking around for two weeks to make my code perfect, or cranking out something that works in 2 hours, I'll pretty much always choose the latter. ("Premature optimization is the root of all evil (or at least most of it) in programming." --Donald Knuth)<p>- That said, when we do come up with some useful and widely applicable code, we do our best to optimize it, put it into pipelines with robust testing, and open-source it, so that the community can use it. If his lab never did that, they're rapidly falling behind the rest of the field.<p>- As for his assertion that bad code and obscure file formats are job security through obscurity, I'm going to call bullshit. For many years, the field lacked people with real CS training, so you got a lot of biologists reading a perl book in their spare time and hacking together some ugly, but functional solutions. Sure, in some ways that was less than optimal, but hell, it got us the human genome. The field is beginning to mature, and you're starting to see better code and standard formats as more computationally-savvy people move in. No one will argue that things couldn't be improved, but attributing it to unethical behavior or malice is just ridiculous.<p>tl;dr: Bitter guy with some kind of bone to pick doesn't really understand or accurately depict the state of the field.

Really makes me want to learn more about molecular biology.<p>Any solid factual resources besides the references mentioned in this justified rant?

Also, yes molecular biologists with few exceptions know little more than fuck all about ecology. Hence the mostly gung-ho attitudes to GM of crop foods for example. Honestly. I've done real molecular biology work (simple commercial protein chemistry and molecular phylogenetics of mitochondrial DNA) and tried to start a PhD in ecology (failed due to funding issues and realising it was a dead end job wise).

There are a lot of problems in bioinformatics. Mainly, lack of reproducibility (ie "custom perl scripts"), poorly organized and characterized data and plenty of wheel reinvention (I heard Jim Kent, who first assembled the human genome, created his own version of wc [word of mouth, citation needed]).<p>The fact of the matter is that through high-throughput sequencing, microarrays, what have you, generation of biologically-meaningful results is possible.<p>There are a lot of problems in bioinformatics that need to be solved. Github has helped. More of bioinformaticians are learning about good software development practices, and journal reviewers are becoming more enlightened of the merits of sharing source code.

Also see the discussion at the bioinformatics subreddit: <a href="http://www.reddit.com/r/bioinformatics/comments/179e9k/a_farewell_to_bioinformatics_since_i_am_about_to/" rel="nofollow">http://www.reddit.com/r/bioinformatics/comments/179e9k/a_far...</a>

Interesting to read since I made the same career move last year. I agree with about half of it but don't see a lot of value or useful advice here.<p>I find it curious that he stops to salute ecologists, since I was in an ecology lab. I liked my labmates and our perspective, but we didn't have any magical ability to avoid the problems he aludes to here.<p>I think a lot of his frustration comes down to not being more involved in the planning process. That's not a new problem. R.A. Fisher put it this way in 1938: “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”<p>Perhaps the idea that we can have bioinformatics specialists who wait for data is just wrong. Should we blame PIs who don't want to give up control to their specialists, or the specialists who don't push harder, earlier? Ultimately the problem will only be solved as more people with these skills move up the ranks. But the whole idea that we need more specialists working on smaller chunks of the problem may be broken from the start (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1183512/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1183512/</a>).

OK, I agree that there are some shitty work on this field, but he can't think they we all in the same boat. For example "Irene Pepperberg’s work with Alex the parrot dwarfs the scientific contributions of all other sequencing to date put together." this is not true. Bioinformatics is not just blinding sequencing new DNA, but analyzing data and almost every new breakthrough in medicine is based in a direct (or indirect) bioinformatics analysis. I used to work in an agrobiotech company and the sequencer was the first source of data for any breeding program. Bioinformatics was used to design primers for PCR to find molecular markers. There is bad software out there? Yes, but I see this as an opportunity than a problem. And the cause is not the need to hide something, but the lack of ability of biologists with no CS background in the field.

Maybe overblown, but it echoes complaints I've heard from other bioinformatics people.<p>Surely this means there's a goldmine waiting there for someone to produce a non-broken toolchain for bioinformatics?<p>Or is it even possible to produce standard tools? Maybe all the labs are too bespoke?

i totally disagree on Fred's negative view of Bioinformatics. as "software is eating the world", it's actually bioinformatics is eating biology. today's main-stream biology is dealing with exploding amount of data from modern instruments, images or clinical data collected every day and mostly machine readable. to stay up-to-date a modern biologist / bioinformatist need to think biological problems in a "big-data" (i know, cliche) way, then try to gain some insight from the data with (computational) tools. today it's the algorithms, mathematical models and software packages on top of databases to pinpoint cancer SNPs and drive drug discovery. and today it's these same algorithms and math models driving how web bench works are designed. if you think biological data are "shitty", i guess you never see other kind of unstructured data out there. so many scholars in other fields envy biologist and medical scientists for something called "PubMed". on the other hand, for those purely wet bench "biologists" who think computers are magic boxes to give answers, insights, models with one push of the button, i do feel sorry for them. they are so last-gen as they just don't have the essential techniques nowadays (just like a molecular biologist not knowing pcr).

This is a little discouraging - BioInformatics was my top choice for a Master's program I'm planning to start this year. The program at Melbourne Uni looks really good (accepts from three streams, Math/Stats, Biology or Computing and tailors the course based on your background). Maybe I should go for a more generic Machine Learning one and try to apply that to healthcare in some other field if things are really this bad.

Some things are going to suck in academia, as this guy points out. But, its a necessary step and todays progress is almost always going to be tomorrows shit. So quit bitching.<p>Biologists are almost never good coders, if they can code at all. But thats not what they do, they signed up for pipettes, not python.<p>Its the programmers who wrote said shitty code that are to be blamed, but you can't hate under-paid and over-worked phd students who write this code even though it usually has nothing to do with their thesis (the math/algorithm is the main part, the deployable implementation is usually not the most important).<p>If you want good code and organized/accountable databases, go to industry. Theres nothing new about this transition. The IMPORTANT part, is that industry gives back to academia. So when you get an office with windows and a working coffee machine, remember to help make some phd student's life a little easier by making part of your code open source.

Where does the Rosalind project (rosalind.info) fit into all of this, I'm wondering? It seems to be written by people who have actual understanding of the mappings between biology and informatics, with clear explanations of problems in terms of the programming challenge involved.<p>Surely they can't get that far without having some kind of sensible method?

Why is this on the front page or why is it relevant? It's kind of a rant. I did some work on a publication in this field and was published once; I don't think it is a horrible research program. There may exist some of the issues in bioinformatics described here but I don't think it is terribly productive.

Having working in the bioinformatics industry as an SE for 9 years I can both agree and disagree.<p>1. I agree that SE standards and good coding practice are completely absent in the bioinformatics world. I remember being asked to improved the speed of some sequence alignment tools and realized that the source code was originally Delphi that had been run through a C++ converter. No comments, single monolithic file. The vast majority of the bioinformatics code I worked with was poorly written/documented Perl. In addition a lot of bioinformatics guys don't understand SE process and so rather than having a coordinated engineering effort, you end up with a lot of "coyboy coding" with guys writing the same thing over and over.<p>2. I agree that productivity is very slow. This is a side product of research itself though. In the "real world" (quoted) where people need to sell software, time is the enemy. It's important to work together quickly to get a good product to market. In the research world, you get a 2/5 year grants and no one seems have much of a fire under them to get anything done (Hey we're good for 5 years!). You would think that the people would be motivated to cure caner quickly (etc), but it's not really the case. Research moves at a snail's pace - and consequently the productivity expectations of the bioinformatics group.<p>3. I disagree that research results from the scientists are garbage. Yes it's true that some experiments get screwed up. However, if you having a lot of people running those experiments over and over, the bad experiments clearly become outliers. Replication in the scientific community is good because it protects against bad data this way. Somehow the author must have had a particularly bad experience.<p>4. Something the author didn't mention that I think is important to understand: most scientists have no idea how to utilize software engineering resources. The pure biologists, many times are the boss, and don't really understand how to run a software division like bioinformatics. Many times PHD's in CS run a bioinformatics group, who have never worked in industry and don't know anything about good SE practice or how to run a software project. A lot of the problems in the bioinformatics industry is directly related to poor management. Wherever you go you're going to have team members that have trouble programming, trouble with their work ethic, trouble with following direction. However, in a bioinformatics environment where these individuals are given free reign and are not working as a cohesive unit, you can see why there is so much terrible code and duplication.

This piece seems to have touched a nerve in the bioinformatics community, though I have no idea why. Much of what is said here is obvious to anyone working in academic research that requires programming expertise.<p>Yes, industry typically pays more than academia. Yes, most molecular biologists cannot code and rely on bioinformatics support. Yes, biological data is often noisy. Yes, code in bionformatics is often research grade (poorly implemented, poorly documented, often not available). These are all good points that have been made many times more potently by others in the field like C. Titus Brown (<a href="http://ivory.idyll.org/blog/category/science.html" rel="nofollow">http://ivory.idyll.org/blog/category/science.html</a>). But they are not universal truths and exceptions to these trends abound. Show me an academic research software system in any field outside of biology that is functional and robust as the UCSC genome browser (serving &#62;500,000 requests a day) or the NCBI's pubmed (serving ~200,000 requests a day). To conclude from common shortcomings of academic research programming that bioinformatics is "computational shit heap" is unjustified and far from an accurate assessment of the reality of the field.<p>From looking into this guy a bit (who I've never heard of before today in my 10+ years in the field), my take on what is going is here is that this is the rant of a disgruntled physicist/mathematician is a self-proclaimed perfectionist (<a href="https://documents.epfl.ch/users/r/ro/ross/www/values.html" rel="nofollow">https://documents.epfl.ch/users/r/ro/ross/www/values.html</a>), who moved into biology but did not establish himself in the field. From what I can tell contrasting his CV (<a href="https://documents.epfl.ch/users/r/ro/ross/www/cv.pdf" rel="nofollow">https://documents.epfl.ch/users/r/ro/ross/www/cv.pdf</a>) to his linkedin profile (<a href="http://www.linkedin.com/pub/frederick-ross/13/81a/47" rel="nofollow">http://www.linkedin.com/pub/frederick-ross/13/81a/47</a>), it does not appear that he completed his PhD after several years of work, which is always a sign of something something going awry and that someone has had a bad personal experience in academic research. I think this is most important light to interpret this blog post in, rather than an indictment of the field.<p>That said, I would also like to see bioinformatics die (or at least whither) and be replaced by computational biology (see differences in the two fields here: <a href="http://rbaltman.wordpress.com/2009/02/18/bioinformatics-computational-biology-same-no/" rel="nofollow">http://rbaltman.wordpress.com/2009/02/18/bioinformatics-comp...</a>). Many of the problems that apparently Ross has experienced come from the fact that most biologists cannot code, and therefore two brains (the biologist's and the programmer's) are required to solve problems that require computing in biology. This leads to an abundance of technical and social problems, which as someone who can speak fluently to both communities pains me to see happen on a regular basis. Once the culture of biology shifts to see programming as an essential skill (like using a microscope or a pipette), biological problems can be solved by one brain and the problems that are created by miscommunication, differences in expectations, differences in background, etc. will be minimized and situations like this will become less common.<p>I for one am very bullish that bioinformatics/computational biology is still the biggest growth area in biology, which is the biggest domain of academic research, and highly recommend students to move into this area (<a href="http://caseybergman.wordpress.com/2012/07/31/top-n-reasons-to-do-a-ph-d-or-post-doc-in-bioinformaticscomputational-biology/" rel="nofollow">http://caseybergman.wordpress.com/2012/07/31/top-n-reasons-t...</a>). Clearly, academic research is not for everyone. If you are unlucky, can't hack it, or greener pastures come your way, so be it. Such is life. But programming in biology ain't going away anytime soon, and with one less body taking up a job in this domain, it looks like prospects have just gotten that little bit better for the rest of us.

I agree that a lot of effort that is put into bioinformatics is wasted. But it's silly to say that bioinformatics hasn't contributed much to science, and naive to think that dysfunctional software development is less widespread outside of bioinformatics.

Fascinating HN thread. I work in the geoinformatics domain where many of the same comments apply. I agree scientists turned programmers are often poor software developers. Moreover, this group often belittles industry established best practices in software development. But in truth, the "pure" software engineer/computer scientist lacks sufficient domain expertise to accomplish something useful. Learning fluid dynamics requires many years of education. Ideally, you would like these two groups to work closely together and with mutual respect.

I largely agree with Fred's opinion on the shortcomings of bioinformaticians and the general attitude in the industry, but my personal experience was actually pretty positive. My past research was on building visualizations of the complicated biochemical processes, for use in educating undergrads. It was certainly more interesting than slogging through mounds of crappy data.<p>Just another data point for someone contemplating a career in BINF, although some purists might say that my work did not really fall under the same category.

Spelling error: 'technically apt', not 'ept'.<p>"Ept" means effective. As in "inept"<p>I don't understand this part:<p>&#62; No one seems to have pointed out that this makes your database a reflection of your database, not a reflection of reality. Pull out an annotation in GenBank today and it’s not very long odds that it’s completely wrong.<p>In fact this entire article seems to be a rant on why bioinformatics as a field is rotting. But instead of ranting, surely something can be done about it?<p>Shouldn't we as hackers see this as an opportunity to revolutionize the field?

Say for the purposes of argument that this thesis were true. What is there (if anything) to be done about it? I ask as a naive interested party with a CS background.

That's a shame. I just finished a uni module about bioinformatics. It seemed like a cool field where progress was being made, and as an undergraduate I could generate meaningful looking results by following very recent papers. I hope the field has some saving graces even if this is all true. The idea of CompSci folk working with biology folk to solve human problems inspired me a lot.

The author is exactly right about the quality of data in bioinformatics. There are datasets with genes named MAR1, DEC1, etc. getting mangled to 1-Mar, 1-Dec, because of Microsoft Excel autoformatting.<p><a href="http://nsaunders.wordpress.com/2012/10/22/gene-name-errors-and-excel-lessons-not-learned/" rel="nofollow">http://nsaunders.wordpress.com/2012/10/22/gene-name-errors-a...</a>

The bio in bioinformatics is the important bit. Informatics plays second fiddle, even in the name. Very few will appreciate your beautiful code, but many will appreciate you finding a cure for cancer. That is the reality of bioinformatics, most of the code has a short shelf life. If you luck out, your software may live longer, as is the case with samtools. That samtools code is crappy is true, still the much cleaner code alternatives, sambamba and bamtools, are not much used! Go figure.<p>Maybe bioinformatics is not the place to aim for great informatics. We do bioinformatics because of love of science first and foremost. This is frontier land, the wild west, and it pays to play quick and dirty. I would suggest to hang on to some best practices, e.g. modularity, TDD and BDD, but forget about appreciation. Dirty Harry, as a bioinformatician you are on your own.<p>To be honest, in industry it is not much different. These days, coders are carpenters. If you really want to be a diva, learn to sing instead.

molecular biology has been dead for years now, but the amount of money poured into it makes it impossible to publish its death certificate. Here is why and how it happened (among other things): <a href="http://www.youtube.com/watch?v=Y0b11S1FjXY" rel="nofollow">http://www.youtube.com/watch?v=Y0b11S1FjXY</a>

Come work with me in my genomic interpretation company. Fun application building, no data mess, big money!

&#62;&#62; I’m leaving bioinformatics to go work at a software company with more technically ept people and for a lot more money.<p>More money, good on you. Starting off your critique of your former colleagues with "technically ept people'...not going to get a lot of sympathy for the correctness of your work.

Someone's got a bad case of God Complex.

i'm not into bio, but read articles on latest development. my sister also took bioinformatics but the scope in India is very less it seems.<p>have you checked out synthetic biology? will it be easy to understand when you have a degree in bioinformatics?