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A farewell to bioinformatics (2012)

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Re: A farewell to bioinformatics (2012)

#171

Post by ank286 »

gabeiscoding wrote: post #167
ank286 wrote: post #163

Earlier quoted context omitted.

Why wouldn't anyone buy your product? If it is easy to use, and SPEEDS UP RESEARCH TIME, your researcher/PI who is spending thousands on computing clusters will buy your software for their graduate students. Hell, my PI keeps asking me if I need a faster computer so I can run Matlab better/quicker. Really, if I had a software that helped me perform research faster/better/quicker and compare my results to ground truth…

Ahh the efficiency argument. The trick is, academics often have excess manpower capacity in the form of grad students and post-docs. Even though personell is usually one of the highest expenses on any given grant, they often don't look at ways to improve the efficiency of their research man-hours. That's not a blank rule, as we have definitely had success with the value proposition of research efficiency, but in gene…

I disagree with you. If there was excess manpower, graduate students wouldn't be stressed out with overwhelming work. Obviously, there is a lot more work to go around and less bodies to give it to. Most of the research man-hours is gone trying to implement other people's research-methods so you have a 'baseline.' A complete waste of time just to have one graph in the Results section of your publication. The height of research inefficiency is to replicate someone else's results and hope (finger's crossed) that you followed their 8-page paper (that took them 10 months to develop) meticulously. Academic researchers only care about results, it is the graduate students that need to be efficient. The efficiency software should be bought by the PIs for their graduate students.

Re: A farewell to bioinformatics (2012)

#172

Post by ank286 »

jk4930 wrote: post #168
gabeiscoding wrote: post #167

Earlier quoted context omitted.

Ahh the efficiency argument. The trick is, academics often have excess manpower capacity in the form of grad students and post-docs. Even though personell is usually one of the highest expenses on any given grant, they often don't look at ways to improve the efficiency of their research man-hours. That's not a blank rule, as we have definitely had success with the value proposition of research efficiency, but in gene…

After researching this field (biomedical R&D) a bit, I found that the mindset and workflow is mostly pre-computers. The relevant decision makers in the labs usually don't see a need to change something because "it works" and "it's done always this way".

"its always done this way" is the ultimate motivation of any startup. We wouldn't have any competing startups if everyone just accepted that, probably, not have any entrepreneurs or have a better world for that matter. The fitness function of the world will flatline.

Re: A farewell to bioinformatics (2012)

#174

Post by MattRogish »

ank286 wrote: post #163
MattRogish wrote: post #20

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. The tools are written by (in my experience) very smart bioin…

Why wouldn't anyone buy your product? If it is easy to use, and SPEEDS UP RESEARCH TIME, your researcher/PI who is spending thousands on computing clusters will buy your software for their graduate students. Hell, my PI keeps asking me if I need a faster computer so I can run Matlab better/quicker. Really, if I had a software that helped me perform research faster/better/quicker and compare my results to ground truth…

I'd be happy for you to be right. At least back when I worked there it wasn't clear the total addressable market was there. It's not that they couldn't buy it, it's that they didn't see the need. Perhaps that has changed. :)

Re: A farewell to bioinformatics (2012)

#175

Post by apathy »

SilasX wrote: post #149
east2west wrote: post #67

Earlier quoted context omitted.

Interestingly, the fraudsters were caught because of a false claim on a CV, and that finally destroyed their creditability. It is intentional fraud, no doubt about it; they restarted halted clinical trials. I was just pointing out they did sloppy work too.

Wow, that's messed up. Fundamental methodological error -> "Come on, these are competent people, you have to trust that whatever error they made didn't effect the final result." False claim of accolade -> "How dare you fucking try to pass off this garbage as legitimate science?!?!?"

Welcome to academia

Re: A farewell to bioinformatics (2012)

#176

Post by apathy »

njbooher wrote: post #136
ufo wrote: post #129

Earlier quoted context omitted.

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.

pacbio is dead, they just don't know it yet. BGI (or somebody, doesn't matter, BGI is just the obvious candidate) would need to buy 50 SMART sequencers a year just for PacBio to stay in business. That seems unlikely given the lower cost and complexity of Illumina and Life sequencers

Re: A farewell to bioinformatics (2012)

#177

Post by carbocation »

gabeiscoding wrote: post #166
carbocation wrote: post #151

Earlier quoted context omitted.

> 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 b…

They key is 23andMe was not using bleeding-edge nightly builds but official "upgrade-recommended" releases. GATK currently has no concept of a "stable" branch of their repo (Appistry is going to provide quarterly releases in the future, which is great). The flag I am raising is that a "stable" release is needed before it get's integrated into a clinical pipeline. Because the Broad's reputation is so high, it is impor…

Good call. Much like a Ubuntu LTE, having stable freezes of the GATK (now that it's relatively mature) that only get bug-fixes but no new (possibly bug-prone) features is a great idea.

Re: A farewell to bioinformatics (2012)

#178

Post by leont »

michaelhoffman wrote: post #82
FreeKill wrote: post #41

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…

I assume these are separate requirements. I have not seen any doctoral-level positions advertised for a salary of $30,000. The minimum NIH salary for postdoctoral trainees is more than that. It's only delusional if they can't find people to fill the jobs. The idea that, as an outsider, you know what requirements they should use in their hiring process better than they do is perhaps more delusional.

Check the Sanger Institute's job page (https://jobs.sanger.ac.uk/wd/plsql/wd_portal.show_page?p_web...). They offer «£29,750 to £37,525» for a "senior bioinformatician", for example.

Re: A farewell to bioinformatics (2012)

#179

Post by jtmcmc »

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.

any type of network reconstruction - gene - gene / protein - protein , gene - protein , interaction network are all very challenging and important computational problems in biology
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