gene logic

Prof. Marie-Claire Shanahan writes about the band Whitehorse and the sounds of science over at Scientific American. A teaser:

Throughout the conversation, they were both contemplative about what had drawn them into science and what they’d learned about its importance in the current social and political climate. “One of things that really struck me and has fueled my passion to have these kind of conversations,” Doucet explained, “is the fact that science is often viewed as some kind of bully pulpit of collective thought where the elitists have agreed upon this collection of ideas and are going to try to ram them down people’s throats. While occasionally that may be the case, as with any institution, what I found so important from reading those books is that science is not an agreed upon set of theories or rules or regulations, it’s a process. Scientific method is what it’s all about. It’s about seeking evidence.” McClelland interjected with the simple but solemn statement, “And the respect around that method has to come back. I mean it’s all we have.”

I offer some final thoughts on the media coverage of ENCODE over at the Huffington Post. A teaser:

[The ENCODE publication] was a fantastic opportunity for scientists and science journalists to explain to the public some of the exciting and important research findings in genome biology that are changing how we think about health, disease, and our evolutionary past. But we blew it, in a big way…

Influenced by misleading press releases and statements by scientists, story after story suggested that debunking junk DNA was the main result of the ENCODE studies. These stories failed us all in three major ways: they distorted the science done before ENCODE, they obscured the real significance of the ENCODE project, and most crucially, they mislead the public on how science really works.

Mike Eisen on why ENCODE-like projects damage science:

For a project like ENCODE to make sense, one has to assume that when a problem in my lab requires high-throughput data, that years in advance, someone – or really a committee of someones – who has no idea about my work predicted precisely the data that I would need and generated it for me.

This made sense with genome sequences, which everyone already knew they needed to have. But for functional genomics this is nothing short of lunacy. There are literally trillions of cells in the human body. Multiply that by life stage, genotype, environment and disease state, and the number of possible conditions to look at is effectively infinite. Is there any rational way to predict which ones are going to be essential for the community as a whole, let alone individual researchers? I can’t see how the answer is possibly yes. What’s more, many of the data generated by ENCODE were obsolete by the time they were collected. For example, if one were starting to map transcription factor binding sites today, you would almost certainly use some flavor of exonuclease ChIP, rather than the ChIP-seq techniques that dominate the ENCODE data…

Think about it this way. If you’re an NIH agency looking to justify your massive investment in big science projects, you are inevitably going to look more favorably on proposals that use data that has already, or is about to be, generated by expensive projects that feature in the institute’s portfolio. And the result will be a concentration of research effort on datasets of high technical quality, but little intrinsic value, with scientists wanting to pursue their own questions left out in the cold, and the most interesting and important questions at risk of never being answered, or even asked.

John Timmer of Ars Technica provides a lucid, accessible account of the ENCODE media disaster.

Sunday science poem at The Finch and Pea:

Half a century before the mechanized mass slaughter of WWI, Melville saw what the effect of science would be on war:

No passion; all went on by crank.

Pivot, and screw,

And calculations of caloric.

Every science student needs to try this: start on the scale of a coffee bean, and zoom in to the scale of cells, DNA, and carbon atoms.