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Move Fast and Embrace Change: How Agrigenomics Science is Changing. And Quickly!

by
Estelle Giraud, Ph.D.
| Jan 13, 2015

Science is changing fundamentallyI’m a PAG novice, and three days into the 23rd Annual Plant and Animal Genome meeting, I’m still in awe. The work that scientists are doing is pushing the boundaries of everything that we know about those things fundamental to humans on this planet: crops and livestock. Researchers in plant and animal genomics are challenging what is technically possible today by envisioning what might be possible tomorrow; and often just go on and make it happen themselves. This has been reflected in the themes of every one of the many, many meetings and discussions that I’ve had in past few days. The pace at which I have been moving has me on the edge of my seat all day, every day.

On Sunday, I was in a packed ballroom for the keynote plenary session by Philip Bourne, NIH Associate Director for Data Science (whose slides are online if you missed the presentation). I was struck by the pace at which science is moving now. At Illumina, one of the our philosophical pillars is to move fast…sometimes very fast, but when you take a step back, you realize that the entire sphere of SCIENCE is moving fast and embracing change, from the funding bodies to industry and institutes, through directors, researchers, and students.

In Philip’s talk, he outlined the current NIH thinking about data. Biology is very quickly becoming a data science. Today’s technology generates data at such a pace that its rapid interpretation becomes absolutely essential to use it and build on it in future work. Dr. Bourne emphasized that with the sheer amount of data that is generated, it is critical to protect and curate it, make it useful, and preserve its value. The NIH is taking this issue very seriously, and changing how they do things with the program: BD2K – Big Data to Knowledge. The program is focused on making a system more efficient at finding, accessing, integrating and re-using digital information in biological science. Because right now one of the main problems is we can find just about anything with a Google search, but we can’t Google data.

Here are some highlights from Philip’s talk, with some of my commentary.

We are currently in the Second Machine Age, with Google cars, 3D printers, crowd-sourced traffic apps, etc. Visual enterprise is growing rapidly and is still considered to be in its infancy. Within the span of one researcher’s career, we have gone from having 15 structures in TOTAL in the protein databank, which took three months to be delivered via mail, to being able to structurally model and visualize Ebola proteins in a few minutes. (Don’t even get me started on the sequencing innovations over the last few years…)

Biological science is becoming increasingly digital, and the nature of the work itself is changing. Experiments that were traditionally observational are now routinely analytical/digital. And it’s happened incredibly fast, a fundamental change brought about in the past 10 years.

The lag in our ability to deal with big biological data is challenging but also opens up many opportunities. The fact that there were 4,500 data scientist jobs available recently will perhaps not surprise anyone until I add that this was in San Diego alone. Despite flat budgets and burgeoning databases, there is good news coming from emerging communities with a willingness to tackle big data issues. And NIH, along with other science and biotechnology industry innovators, are helping change the way scientists interact with biological data. Illumina has recently developed an entire business unit focused on enterprise informatics to engage the community that interacts with data produced by our systems. These are exciting times, and we have a lot to learn!

Speaking of lots to learn, today is the last day of the PAGXXIII exhibits. If you find me at the booth near the front of the hall, the conversation will probably go something like this:

  • How are you changing the world?
  • What ploidy is the species are you working with and is there a reference genome/transcriptome/annotation/ANYTHING available to you?
  • How can we help find the best genomics approaches for your research?

And it will likely end with me saying: “Wow that’s truly exciting work and good luck with those challenges as you push forward in ways that we can’t imagine.”

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