In the competition, scientists were judged on solving real world problems using comprehensive computational approaches, large data sets, and high-end visualization technology to display results - which means it had to look good and be easy to use.
PNNL's Chris Oehmen led a multi-disciplinary team composed of Scott Dowson, Chandrika Sivaramakrishnan, Justin Almquist, Lee Ann McCue, Bobbie-Jo Webb-Robertson, and Jason McDermott to the win. The team used resources at PNNL and at EMSL, DOE's Environmental Molecular Sciences Laboratory on the PNNL campus, to develop the interactive programme.
Past finalists have been in areas as varied as orthodontics, atomic energy, and music classification. PNNL's winning entry in genomics combined multiple databases, analysis software, and a home-grown "visualization technology" called Starlight that presents data in unique visual patterns and allows users to interactively explore them.
"Our entire team is thrilled that we won", Chris Oehmen stated. "It's an honour to be a part of this international competition. We could not have completed the challenge without the support of our sponsors at the Department of Energy, the National Science Foundation, and internal investments from the Pacific Northwest National Laboratory."
A common problem for genomics researchers, said Chris Oehmen, is that desktop computers often can't handle the volume of calculations needed to analyse many genomes at once. At the other end of the spectrum, high performance computers often limit researchers' ability to guide the analysis along the way.
"We wanted to demonstrate that high-performance computing can be integrated into an iterative work flow because this is the way biologists really work", Chris Oehmen stated. "It was the MeDICi middleware that really helped us pull the various data, analysis, and visualization together."
In genomic studies, computer programmes compare DNA sequences of different living things to find shared proteins or uncover the function of a mystery protein, generating ideas that can then be tested in laboratory experiments. This interactive programme gives laboratory researchers a place to start in looking for proteins and genes with interesting functions.
Chris Oehmen demonstrated that their interactive programme and high computing power could explore the complement of proteins found in an organism, allow them to focus on a protein that intrigued them, and investigate its possibilities.
Browsing through all the proteins in various Shewanella bacterial species, the team noticed more proteins than expected with tell-tale iron-detecting components. "We thought, there are a lot of iron-sensing proteins here. What are they doing?" commented Chris Oehmen.
As it happens, many species of Shewanella have the ability to transfer electrons to an electrode, thus forming a simple biological fuel cell, an alternate means of generating energy. Iron is involved in this activity, so the team decided to identify proteins that may help the bacteria sense the iron and form a biofilm on an electrode.
Starting with a known, non-Shewanella protein that senses iron, the programme allowed researchers to guide the search for similar proteins out of 42.000 proteins from 10 Shewanella species. After rounding up about 550 possible iron-sensing proteins, the researchers switched gears and determined which of these might also be involved in biofilm formation, based on other criteria. Ultimately, the team zoomed in on one protein that had potential roles in both activities. In addition, some of the species had two copies, suggesting those species might have some sort of biofilm advantage.
"Letting users find this sort of information interactively is the main motivation for this work and for the visual representations we have chosen", stated Chris Oehmen. The visual representations of the data included colourful "graphical clusters" that looked like pie charts to the untrained eye, and other images that looked like stars connected through space. In the Shewanella example, the star-graph clued the researchers into the presence of the extra protein copies.
"Presenting data visually can let important information rise to the top", stated Chris Oehmen. And there at the top, the secrets buried in DNA data just can't stay hidden.
Software developed by PNNL that were used for the demonstration included SHOT, a sequence analysis algorithm that transforms protein sequences into sets of features to identify homologous pairs using a support vector machine; ScalaBLAST, an algorithm for performing gene and protein sequence analysis; Starlight, now commercially available from Menlo Park-based Future Point Systems Inc, an information visualization application to perform advanced interactive visual analysis. Bringing them together was MeDICi, a "plumbing" software technology developed as part of PNNL's Data Intensive Computing Research Initiative that supports the integration of applications, data and computing resources.
EMSL, the Environmental Molecular Sciences Laboratory, is a national scientific user facility sponsored by the Department of Energy's Office of Science, Biological and Environmental Research programme that is located at Pacific Northwest National Laboratory. EMSL offers an open, collaborative environment for scientific discovery to researchers around the world. EMSL's technical experts and suite of custom and advanced instruments are unmatched. Its integrated computational and experimental capabilities enable researchers to realize fundamental scientific insights and create new technologies.
Pacific Northwest National Laboratory is a Department of Energy Office of Science national laboratory where interdisciplinary teams advance science and technology and deliver solutions to America's most intractable problems in energy, national security and the environment. PNNL employs 4200 staff and has an $850 million annual budget. Ohio-based Battelle has managed PNNL since the lab's inception in 1965.
A video demonstration of the programme can be found at the PNNL website.