Instruction precedes innovation

25 April, 2012 (16:07) | Genomics Research | By: Trey

OpenHelix is founded on the premise and Mary made the point again recently on a guest post on Nature Blogs: SciVee – Making Science Visible.

Do you RTFM? C’mon—tell the truth. When you approach some new software, do you read the freakin’ manual first? Yeah. Thought so. Not to worry, it’s a common phenotype. In fact, we’re pretty sure that the scientists at OpenHelix are among a tiny fraction of people with a rare allele for software manual reading. But the good news is we’ve found a way to help non-rare allele people.

(RTFM… “Read the ‘forlorn’ manual” … F being something else… this is a family-friendly blog after all).

We absolutely know that a hour or a few of some structured training and learning about a biological database or analysis tool will save a researcher days, sometimes months, of work. Sometimes it will mean the difference between making an amazing discovery… or hitting a dead end.

Well, it was nice to read today in a comment in Nature  (David Piston, p440, behind a subscription wall) a perfect example of something we are evangelists about:

As head of Vanderbilt University’s core microscopy labs, I recently met a colleague and his student to discuss their confusing results from an experiment studying protein interactions in cells. After applying a treatment that should have disrupted the interaction of two particular proteins inside mitochondria, they still saw the proteins interacting. The student said that to measure the interaction he had used a commercial automated image- analysis system. He didn’t understand how it worked, so he just used a colleague’s settings from a different experiment. But, without him realizing, this had masked all of the cell except for the mitochondria. If he had

modified the settings to leave the entire cell unmasked, he would have seen that the proteins were now present within the mitochondria in relatively small amounts compared with the rest of the cell, and so their interaction had been disrupted — the treatment was, in fact, working.

In this case, it wasn’t inspiration that was lacking — it was instruction.

 He goes on to discuss how automation in research has been a boon, yet at the same time a bane.
It’s made it easier to get research done. This is going to sound like a ‘up the hill both ways… in the snow’ story, but a short (yes, I say.. short) 20 years ago when I started sequencing retrotransposable elements in drosophila, I used the Sanger method and used to fill lanes and lanes of  polyacrylimide gels. A few thousand base pairs of sequence took a long time, as in days (or weeks when I made a stupid mistake and cracked the huge glass set up our brilliant lab technician set up). Today, it’s automated and fast, blazing fast. As in hundreds of millions of base pairs in the same amount of time.
But, as Dr. Piston explains in the comment, this leads to researchers not understanding fully how the tool works and the parameters best to use:
they waste time by using a tech- nique improperly or, equally tragically, miss something exciting when they assume that a strange result means that they did something wrong and they never follow it up.
He speaks mainly to experimental tools at the bench and the need for more education and instruction. But it goes doubly so for databases and computational analysis tools, databases and tools that pretty much any and all biological researchers now need to access and use regularly… or should.
We have so many of our own anecdotes along these lines. There was the researcher in mycology who, when we showed them a couple databases of mycological information, were ecstatic. My best anecdote was a prominent researcher (won’t say who, when or where :D ) who took a workshop with us on the UCSC Genome and Table browsers. This researcher came up to me afterwards and showed me the research being done in the lab and the month they had spent in a dead end trying to analyze the data. At the end of the workshop, they figured out their answer and told us that the 4 hour course probably just saved them 6 months of work.

We have a lot of anecdotes like that.

Read the comment, Dr. Piston has some very valid and important points and suggestions.

Oh, and… here comes the totally shameless plug…  you could always go watch our sponsored open access tutorials and do the exercises on UCSC Genome and Table browsers, ENCODE, PDB and others. Or our other 100 tutorials (subscription). Or one of our nearly 200 weekly tips (open access).

And we will be having a series of webinars in the next half year, the first to be announced in just about a day. So keep watch here.

Remember, instruction comes before innovation. You could save months of work… or even better, make a discovery that you’d never have made otherwise.

Piston, D. (2012). Research tools: Understand how it works Nature, 484 (7395), 440-441 DOI: 10.1038/484440a

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