Simply Statistics Podcast #3: Interview with Steven Salzberg

07 Sep 2012

Interview with Steven Salzberg about the ENCODE Project.

In this episode Jeff and I have a discussion with Steven Salzberg, Professor of Medicine and Biostatistics at Johns Hopkins University, about the recent findings from the ENCODE Project where he helps us separate fact from fiction. You’re going to want to watch to the end with this one.

Here are some excerpts from the interview.

Regarding why the data should have been released immediately without restriction:

If this [ENCODE] were funded by a regular investigator-initiated grant, then I would say you have your own grant, you’ve got some hypotheses you’re pursuing, you’re collecting data, you’ve already demonstrated that…you have some special ability to do this work and you should get some time to look at your data that you just generated to publish it. This was not that kind of a project. These are not hypothesis-driven projects. They are data collection projects. The whole model is…they’re creating a resource and it’s more efficient to create the resource in one place…. So we all get this data that’s being made available for less money…. I think if you’re going to be funded that way, you should release the data right away, no restrictions, because you’re funded because you’re good at generating this data cheaply….But you may not be the best person to do the analysis.

Regarding the problem with large-scale top-down funding approaches versus the individual investigator approach:

Well, it’s inefficient because it’s anti-competitive. They have a huge amount of money going to a few centers, they’ll do tons of experiments of the same type—may not be the best place to do that. They could instead give that money to 20 times as many investigators who would be refining the techniques and developing better ones. And a few years from now, instead of having another set of ENCODE papers—which we’re probably going to have—we might have much better methods and I think we’d have just as much in terms of discovery, probably more.

Regarding best way to make discoveries:

I think a problem I have with it…is that the top-down approach to science isn’t the way you make discoveries. And NIH has sort of said we’re going to fund these data generation and data analysis groups—they’re doing both…and by golly we’re going to discover some things. Well, it doesn’t always work if you do that. You can’t just say…so the Human Genome [Project], even though, of course there were lots of promises about curing cancer, we didn’t say we were going to discover how a particular gene works, we said we’re going to discover what the sequence is. And we did! Really well. With these [ENCODE] projects they said we’re going to figure out the function of all the elements, and they haven’t figured that out, at all.

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[NOTE: Due to clumsy camera operator (who forgot to turn the camera on), we lost one of our three camera angles and so the there’s no front-facing view. Sorry!]