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A farewell to bioinformatics (2012)

madhadron.com

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Re: A farewell to bioinformatics (2012)

#151

Post by carbocation »

gabeiscoding wrote: post #104
carbocation wrote: post #79

Earlier quoted context omitted.

> 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 f…

> 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.

Sure, I agree with that. And I would agree if you would say "Using bleeding-edge nightly builds of %s for production-level clinical work is a bad idea," whether %s was the GATK or the Linux kernel. I would be in such complete agreement that I wouldn't even feel compelled to respond to your posts if that's what you would say originally, rather than saying, "the GATK ... should not be put into a clinical pipeline". The former is accepted practice industry-wide; the latter reads like FUD and cannot be justified by one anecdote.

> You know though, the conflict of interest dismissal is something I run into more than I would expect.

Regarding conflict of interest, my point in trying to understand your potential interests, and also disclosing my own so that you can see where I'm coming from. That's not a dismissal, it's a search for a more complete picture. Interested parties are often the most qualified commenters, anyway, but their conclusions merit review.

Hopefully people wouldn't dismiss my views because of my Broad connection, anymore than they would dismiss yours if you sold a competing product.

Re: A farewell to bioinformatics (2012)

#153

Post by dalke »

sampo wrote: post #103
zmmmmm wrote: 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?

These are different sorts of alignments, with different sorts of math behind them.

Genome assembly is the shortest common super sequence problem. It involves finding the best rearrangement and overlap of reads which minimize the overall sequence, given the expected errors in the read technology. It would still be hard even if all of the reads were perfect.

Sequence alignment looks at two or more sequences in their entirety, and does a best fit alignment using a given model of how substitutions and gaps can occur. This model may be based on chemical or evolutionary knowledge.

A "super-efficient solution to sequence alignment" doesn't lead to a way to tell how the reads should be assembled into a single large sequence, even ignoring possible read errors.

Re: A farewell to bioinformatics (2012)

#154

Post by dalke »

alephnil wrote: post #132
sampo wrote: 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?

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...

Yes, for pairwise sequence alignment. The globally optimized multiple sequence alignment problem is NP-complete.

Re: A farewell to bioinformatics (2012)

#155

Post by ChristianMarks »

drosophila wrote: post #110
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.

This seems to presume that scientific programming is merely a service to the important and more deserving persons who generate scientific hypotheses, from whom it can be decoupled and isolated, instead of being the collaborative effort that it is--if elevating the professional standing of scientific programmers must wait for the widespread adoption of automated hypothesis generation software. For example, the computation of ecosystem service indicators--what you might call the interface between biogeophysical models of Earth systems and economic and policy modeling--is an interdisciplinary and collaborative activity that relies heavily on computational technique and technology.

Re: A farewell to bioinformatics (2012)

#157

Post by scott_s »

moondowner wrote: post #120
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…

" 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 the only bad point that a lot of people are aligned with. The more time a program needs to finish, the more time you will need to run it again with some other dataset, and in turn - more time to find the right answer. I really fee…

Spending time optimizing a program that you will use once is a waste. Sitting idle while waiting for a program to finish is also a waste. So I think it's reasonable to optimize for programmer time the first time, and then re-visit the design if you discover the code is getting reused and fed larger data sets.

Re: A farewell to bioinformatics (2012)

#158

Post by latj »

ansgri wrote: post #94
zerohp wrote: post #25

Earlier quoted context omitted.

I have enough experience to know if this is true or not. Many times it was faster to buy more machine, but often it was not. We already had 10000 cores. I proposed, implemented, and tested an 8 line change to our alignment tool that saved 6% cpu time. It took me two days, most of which was my spare time at home. This one program was using 15 cpu years every month. Nobody cared. It never went into production. I starte…

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.

Some labs are conservative because they are worried that they will not be able to reproduce the same analysis. For example, imagine that a lab had been collecting samples and executing the current code as they came in. Now imagine, two years later, someone starts drawing some conclussions based on an aggregation of the results over a petabyte of that data. On one hand, you could just say- nope, we cant reproduce the same analysis, but we can use all of our computational power for a month and reanalyze all of the data using the current packages/code. On the other hand, a more conservative idea might be to try to record the entire state of the environment when particular samples are recorded, so that in theory you could replay all that analysis- fire up the vm from 2 years ago, install the same version of all the packages, install the code with the same tags, and analysis that data set, then do the same thing for every other data set. Smaller labs I think are just hoping that no one tries to replicate their studies or asks them if they can reproduce their results.

Re: A farewell to bioinformatics (2012)

#159

Post by FreeKill »

kevin_rubyhouse wrote: post #99
FreeKill wrote: post #92

Earlier quoted context omitted.

I'm not an outsider and the 30K was a bit of an exaggeration, and I apologize for that. The point I was trying to make was that if you look in as an outsider, you would see the requirements being extremely daunting compared to what you might see elsewhere with a pay scale that is very low and unappealing to anyone who might match it. Unless, of course, you just finished your degree in some biological discipline where…

If 30k is the inaccurate number, what's the accurate one? I'm curious as to what the realistic requirements are from your experience with the field.

I've seen a lot of job listings, at very large companies and academics for the 45-50 range. Keep in mind, these are jobs requiring a PHD, 10 years of experience, and a dozen or so technologies.

It's not really the money that's skewed, it's their idea about the person they need for the job. They don't need someone with that background (most of the time), they just need a junior level software engineer in which case the pay scale may not be too bad. There's a problem in realizing this, however, when the standards for your own field (molecular biology for example) are extremely high, so you expect it of all others as well...

Re: A farewell to bioinformatics (2012)

#160

Post by googoobaby »

I was really amused by this when I read it earlier today, doubly so when I realized I knew the author slightly and his work environment very well. All I can add is that given how computer-centric this discipline is, it boggles the mind how computer and software illiterate its practitioners actually are. It's also a kingdom where the one eyed man rules, where the "best" seem to be hipster cool chasers.
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