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A farewell to bioinformatics (2012)

madhadron.com

131–140 of 179 posts

Re: A farewell to bioinformatics (2012)

#131
post #100
post #94

Earlier quoted context omitted.

How complicated was the bureocracy that you couldn't push the change into production yourself after verifying that it is a strict speed-up and doesn't break anything? I think such barriers are incompatible with the word 'research', where the first you need is freedom.

Research is highly competitive business mixed with industry involvement (or government involvement). You have to publish and fast. You have to develop your discoveries into something that can be monetized. You have to collaborate with industry to get funded. You have to cut costs to keep doing what you want to do. And so on. The idea of freedom in (fundamental) research seems long dead. How I long for the freedom in…

> The idea of freedom in (fundamental) research seems long dead

Is it so in the US? Or where? Here in Russia it is far from true, at least in the top institutes. As long as you produce publishable results, you may do virtually whatever you want, and nowadays pretty much anything is publishable. And this way you get funding, too, because the funding agency doesn't seem to want you to solve some particular problem, it just wants to be sure your science keeps up with the world.

The downside here is that the academy usually pays bad. Thus it seems most successful labs work like 70/30 on commercial projects and "pure science". Anyway, when you work on commercial projects you usually get much more interesting results than you'd care to publish.

Re: A farewell to bioinformatics (2012)

#132
post #103
post #101

Earlier quoted context omitted.

One that comes immediately to mind is genome assembly, which is a hugely complex problem, and essential to a variety of fields that rely on re-piecing together the genome without a reference (or with a reference that is highly divergent from the sequence data).

Genome assembly relies heavily on sequence alignment. So: Is genome assembly hard just because sequence alignment is hard? Or would genome assembly present separate algorithmic problems even if there was a super-efficient solution to sequence alignment?

It is far more difficult than sequence alignment. Sequence alignment has quadratic complexity, while fragment assembly is NP-hard. Se for example

http://scholar.google.com/scholar?cluster=131745416915434219...

Re: A farewell to bioinformatics (2012)

#133
post #8

Sounds like a fed up academic with a stick up his backside. 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. 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. In the realm of bioinformatics, lets ignore the work done…

he should have been able to improve the circumstances

Was anyone asking him to? Was anyone paying him to? No? Then it's an uphill battle and also not his responsibility. Leaving is saner.

Re: A farewell to bioinformatics (2012)

#134
post #122

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: "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" It's delusional because they apply the requirements it took for themselves to…

As someone on a bioinformatics team in a public research institution, salaries range from $75k to $100K for developers on our team. This includes a number of people, including myself, who do primarily normal IT things (data management, small webapps for various clinical and research needs) and also for devs doing pipeline/workflow mgmt software, novel dev (e.g. new research code for sequencing), and variant calling w…

I don't disagree with most of your post, though I cannot resist commenting on the whiteboard interview test. Writing anything other than rough pseudo code or algorithm sketches on the whiteboard is a silly exercise. It's not reflective of any sort of reality, probably indicates to candidates that you are not working on any interesting problems, and people won't remember exact syntax or library functions for any language that they don't use fairly regularly.

The whiteboard is only useful as an aid in explaining an algorithm. If a candidate can do that without the whiteboard, even better.

Re: A farewell to bioinformatics (2012)

#135
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.

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 (http://ivory.idyll.org/blog/category/science.html). 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 >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.

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 (https://documents.epfl.ch/users/r/ro/ross/www/values.html), who moved into biology but did not establish himself in the field. From what I can tell contrasting his CV (https://documents.epfl.ch/users/r/ro/ross/www/cv.pdf) to his linkedin profile (http://www.linkedin.com/pub/frederick-ross/13/81a/47), 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.

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: http://rbaltman.wordpress.com/2009/02/18/bioinformatics-comp...). 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.

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 (http://caseybergman.wordpress.com/2012/07/31/top-n-reasons-t...). 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.

Re: A farewell to bioinformatics (2012)

#136
post #129
post #103

Earlier quoted context omitted.

Genome assembly relies heavily on sequence alignment. So: Is genome assembly hard just because sequence alignment is hard? Or would genome assembly present separate algorithmic problems even if there was a super-efficient solution to sequence alignment?

An extra difficulty with genome assembly is that DNA often has lots and lots of repeated junk sequences that can confuse the algorithms. I don't work with bioinformatics to know how they usually get around this though.

Repeats aren't necessarily junk (e.g. TAL Effectors http://en.wikipedia.org/wiki/TAL_effector#DNA_recognition). Resolving them requires long reads. PacBio is currently of interest as an alternative to Sanger sequencing for this, although the error rate of PacBio reads is a bit of an issue.

Re: A farewell to bioinformatics (2012)

#138
post #134
post #122

Earlier quoted context omitted.

As someone on a bioinformatics team in a public research institution, salaries range from $75k to $100K for developers on our team. This includes a number of people, including myself, who do primarily normal IT things (data management, small webapps for various clinical and research needs) and also for devs doing pipeline/workflow mgmt software, novel dev (e.g. new research code for sequencing), and variant calling w…

I don't disagree with most of your post, though I cannot resist commenting on the whiteboard interview test. Writing anything other than rough pseudo code or algorithm sketches on the whiteboard is a silly exercise. It's not reflective of any sort of reality, probably indicates to candidates that you are not working on any interesting problems, and people won't remember exact syntax or library functions for any langu…

I'm kind of with you on the whiteboard code issue (I was sitting in on the interview in question), especially for a "hard" coding exercise.

My bigger concern is that for a job that specifically highlighted the need for at least some SQL skills and some Java expertise, a candidate that can not, even after prompting, write a for loop in Java (or in any language, when offered the chance to do so in a "favorite" language") or write a SQL statement that joins two tables probably can't do much of anything, let alone work on interesting problems.

Here is the cold, hard truth - I know, both because of my own limitations and the opportunity of the job, that we are not going to get top % hackers. But if you apply to a job where the primary need is coding in blub, I think its fair to expect a simple question or two about basic blub constructs. I myself would be nervous about whiteboard coding for something complex, but also generally offer (in a cover letter) to provide some code examples to talk through at an interview ahead of time.

I think it behooves us all to have at least some baseline expectation to demonstrate some competence. Remember, I'm not thinking that whiteboard coding of an algorithms or anything.

I think a very fair (and concerning to me) insight might be: if you can use Google and an IDE, can you do all that this job requires?

Re: A farewell to bioinformatics (2012)

#139
post #96

Some thoughts on this article: - 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." - 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…

Out of curiosity, what other computationally difficult problems are there? I'm very interested in bioinformatics, but sadly don't know as much about the field as I'd like.

Protein folding is an interesting and computational challenging task. So challenging that some groups have sort of given up on it and move to other fields. Look up David Baker and Rosetta for more info. This is just an example, there are many many problems to work on. I feel sorry for the author of the post, bioinformatics is only getting more interesting as our capacity to make experimental measurements grow. There have been so many interesting findings that are just the product of bioinformaticians digging into existing databases and analyzing them to come up with new theories that have since then been experimentally validated.

Re: A farewell to bioinformatics (2012)

#140
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.

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.

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.”

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 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1183512/).

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