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A farewell to bioinformatics (2012)

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Re: A farewell to bioinformatics (2012)

#101

Post by zmmmmm »

Inufu wrote: post #96
chrisamiller wrote: post #62

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.

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).

Re: A farewell to bioinformatics (2012)

#102

Post by zmmmmm »

lemming wrote: post #46

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…

I'm just starting a PhD at Melbourne Uni in bioinformatics after working in the field for several years. Don't pay any attention to this is my advice. Bioinformatics is a field currently pulling itself up by its own bootstraps out of the realm of research into the clinic. That's a painful process to be sure, but IMHO it's the most profoundly exciting time to be part of any discipline. You are literally being a part of and watching history in the making. It's going to be messy, but there are chances to contribute here like no other field going around.

Re: A farewell to bioinformatics (2012)

#103

Post by sampo »

zmmmmm wrote: post #101
Inufu wrote: post #96

Earlier quoted context omitted.

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.

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?

Re: A farewell to bioinformatics (2012)

#104

Post by gabeiscoding »

carbocation wrote: post #79
gabeiscoding wrote: post #48

Earlier quoted context omitted.

I live in this field, as a computer scientist learning the biology, and trying to make a living with a bootstrapped company. I wrote a post about why GATK - one of the most popular bioinformatic tools in Next Generation Sequencing should not be put into a clinical pipeline: http://blog.goldenhelix.com/?p=1534 In terms of your ideal software strategy, I can speak to that as well, as I am actually attempting to do almo…

> I wrote a post about why GATK - one of the most popular bioinformatic tools in Next Generation Sequencing should not be put into a clinical pipeline: I've seen you link to your blog post a couple of times now, and I still think it's misleading. I do wonder whether your conflict of interest (selling competing software) has led you to come to a pretty unreasonable conclusion. (My conflict of interest is that I have a…

On your first point, my post detailed that 23andMe confirmed it was a GATK bug that introduced the bogus variants and the bug was fixed in the next minor release of the software. There are comments on the post from members of 23andMe and the GATK team that go into more details as well.

On your second point. 23andMe had every incentive to pay attention to their output, but it is fair to say it's their responsibility for letting this slip through. But, it's worth noting in the context of the OP rant, that 23andMe probably paid much more attention to their tools than most academics who often treat alignment and variant calling as a black box that they trust works as advertised.

So what I actually argue in the post (and should have stated more clearly in my summary here) was that GATK is incentivised, as an academic research tool, to quickly advance their set of features with the cost of bugs being introduced (and hopefully squashed) along the way.

This "dev" state of a tool is inappropriate for a clinical pipeline, and GATK's teams' answer to that is a "stable" branch of GATK that will be supported by their commercial software partner. Good stuff.

Finally, I actually have no conflict of interest here as Golden Helix does not sell commercial secondary analysis tools (like CLC Bio does). I wrote this from the perspective of someone who is a 23andMe consumer as well as being informed as I give recommendations of upstream tools with our users (which I might add, I would still recommend and use GATK for research use, with the caution to potentially forgo the latest release for a more stable one).

You know though, the conflict of interest dismissal is something I run into more than I would expect. I'm not sure if some commercial software vendor has acted in bad faith in our industry to deserve the cynicism or if this is defaultly inherited by the "academic" vs "industry" ethos.

Re: A farewell to bioinformatics (2012)

#105

Post by sampo »

stiff wrote: post #60

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 c…

My friend said: Bioinformatics means that computer scientists – who don't know mathematics and don't know biology – are trying to do mathematical biology.

Re: A farewell to bioinformatics (2012)

#106

Post by [deleted] »

chewxy wrote: post #38
saraid216 wrote: post #31

Earlier quoted context omitted.

As a general rule, the people on the short end of the stick are the people least capable of producing change. Worse, change that they bring about tends to be good from a strict, technical viewpoint but has huge negative side effects that go unnoticed or deliberately ignored until it becomes difficult to distinguish the resultant system as a better one. Rants like this, and providing interviews to third parties, are a…

I don't know, but I think Fred is in a prime position to disrupt bioinformatics. He knows all the flaws, he knows all the problems. If I were him, I'd have seized the opportunity and work on a hard problem. Then again, I am in no position to judge what Fred should or should not do

[deleted]

Re: A farewell to bioinformatics (2012)

#107

Post by Paradigma11 »

aheilbut wrote: post #3
mscarborough wrote: post #2

>> I’m leaving bioinformatics to go work at a software company with more technically ept people and for a lot more money. 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.

Everyone is jumping on that, but (while I had to look it up too) 'ept' actually is a real word: from the OED: ept, adj. Pronunciation: /ɛpt/ Etymology: Back-formation Used as a deliberate antonym of ‘inept’: adroit, appropriate, effective. 1938 E. B. White Let. Oct. (1976) 183, I am much obliged..to you for your warm, courteous, and ept treatment of a rather weak, skinny subject. 1966 Time 30 Sept. 7/1 With the excep…

Isn't it more likely that he just mispelled "apt".

Re: A farewell to bioinformatics (2012)

#108

Post by barrkel »

epistasis wrote: post #37
michaelhoffman wrote: post #21

Earlier quoted context omitted.

Hi, I'm a bioinformatics researcher. Apparently I work for this guy's ex(?)-employer although I have never heard of him before. My single most limited resource is programmer time. My time and the time of other people who work with me. I have access to loads of computers that sit idle all the time, even if it is on nights and weekends. There is zero opportunity cost to me in using these computers more fully. I have en…

>There can be a big opportunity cost in trying to rework a workflow so that it is more efficient and then test it thoroughly ensure correctness. Hi, I recognize your name as a legit bioinformatician, am a huge fan of the lab that you're currently in, and others should listen to you. I'd like to add that for many projects, general reusable software engineering is not necessarily a huge advantage. Instead of verifying…

If Java is your idea of a good time, it's unlikely that you'll be an effective researcher (though it's not unheard of), because it's not good at maximizing single-programmer output, and not good at maximizing I/O or CPU or string processing.

FWIW, I have in the past gotten good results out of Java and C# (it's a lot easier in C#) by writing programs that generate bytecode at runtime, so they can use the JIT to further optimize performance. Getting the same results out of C would require a lot more work. This includes string processing - I wrote a regex compiler for Java at one point, easily outperforming java.util.regex.

And such things are not difficult - or at least, not difficult to me now, knowing all I know - perhaps 10 hours work for simple regex compiler. And that is how I would use tools like Java to optimize my own performance: adapt them to interpret or compile a language that is close to the problem domain. A slightly higher constant cost, with the aim of a much lower per-idea cost.

Re: A farewell to bioinformatics (2012)

#109

Post by drosophila »

Inufu wrote: post #96
chrisamiller wrote: post #62

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.

1. gene networks is a big one: some proteins turn genes on or off. Some of those genes get translated into other proteins that turn genes on or off. How can you infer the interactions from experimental data? How can you figure out what these complex networks DO? 2. Predicting gene expression: where do proteins bind to the DNA? How can you predict what these proteins do once they are bound ( add chemical tags to structural proteins, knock off structural proteins by bending DNA, etc)? How can you predict how frequently the gene will be transcribed? How does the 3D shape of the DNA effect this?

These are just two of many questions ( biased towards my research interests of course ). It is really funny that he mentions sequence alignment and phylogenetically as the two big problems, because people generally consider these to be boring, uncool, solved-well-enough-for-our-purposes problems nowadays and just trust the algorithms described by Durbin decades ago. It sounds like the writer really doesn't know bioinformatics that well...

Re: A farewell to bioinformatics (2012)

#110

Post by drosophila »

ChristianMarks wrote: post #90

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,…

Right now experimentalists generate data and then try to find computer people to analyse their data. However, in the not too distant future computer models will drive experimental research as hypothesis generation tools. Then the computer people will be seeking biology people ( or robots) to run experiments to validate their hypothesis and there will be more respect for the field.
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