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August 2005


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Bracing for impact

Preparing for coming bioinformatics conferences, I shudder to think of the talks that sound great in the program but turn out just to be the nth naive bioinformatician. Five things I really don't want to hear again this (but probably will) later this year.
  1. Reductionism and Better understanding used in conjuction with biologist, who supposedly don't get the whole picture because they only study one protein in their life. Used by computer science graduates that just downloaded two different data sets from the web, whacked it through R and think the biologists were just waiting for it.
  2. Overviews that consist of introduction, methods, results, discussion and conclusion but no word on the subject.
  3. Pointing out groups of genes as interesting and ask for experimental validation thereof. Every gene is interesting be it conserved or specific, abundant or crucially regulated. The most boring gene would be highly interesting, wouldn't it?
  4. Complaining about errors in public data bases. A common theme in presentation from people who were incapable of restocking the pipet tip racks are pointing out errors in annotations or relabeling of genes. Such behaviour should be punished by taking those peoples code and highlight the errors in their comments (or lack thereof) on the conference web site.
  5. Names of proteins - If you've been working on isolating a protein it's your undisputed right to christen it. If you want to call it after the teenage mutant ninja turtles or simply p4.1, go ahead. If you have named your variables $aaa, or britney.h you are just as guilty anyway.
Thanks, but no, thanks, Dr. Freeman, I have no use for your crowbar now that I wrote this rant.
Greg Tyrelle (guest) - 2005-08-24 18:01

I couldn't agree more

This is a classic post, brought a smile to my face on every point.

The condescending tone taken by some "computer scientists" towards "biologists" during their presentations has often had me on the verge of angry outbursts. I don't think this will last much longer. There seem to be many biologists with sufficient computational skills to feel comfortable in both areas, this was noticed by many at ISMB (see point 2.).

The end result of this is that the importance of "just doing bioinformatics" will diminish. I predict that to make real contributions in the future, a lab will not be just computational or purely wet-lab based, but a combination of both (this is what systems biology is really about).

See also Sean Eddy's rant in PLOS.

spitshine - 2005-08-24 19:38

On the other hand

There are many computer scientists who have great respect for the complexity of the biology out there - and the number of CS pundits with little respect for the biologist is decreasing, fortunately.
I met a number of people with pure mathematics background that aquired deep insights into the biological systems they study, at least from my simplistic point of view. In fact, I know an economics graduate with an astounding insight into bioinformatics - it's just the pure naivity (and arrogance) of a small regrowing group that happens to be around in every bioinformatics conference.
Steve Mount (guest) - 2005-08-26 06:15

Thanks for the link

Well, I'm in general agreement with this whole discussion, but I really enjoyed the rant by Sean Eddy, particularly since I have participated a bit in the NIH roadmap process. Thanks for the link.
FiReaNG3L (guest) - 2005-08-25 05:53

An issue with the unregulated naming of genes / proteins

"If you've been working on isolating a protein it's your undisputed right to christen it"

Sure, I agree. But the problem arise when 2-5 groups discover the same protein / gene and name it. Not only in their 'discovery' paper, but in subsequent ones as well. And then, 2-5 organisms decide that this is a nonsense, and decide to rename the protein. With different names.

I'm doing microarray data analysis and honestly, subsequent data mining to understand the biological significance of a list of changed genes is a nightmare. It's not rare to see 15-20 different names, for a single protein. [NAME A] OR [NAME B] OR ... [NAME Z] in pubmed is lots of fun. Luckily, some tools can partially automate this research now (Chilibot, among others), but this is far from perfect.

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