Samuel M. D. Seaver
,
Argonne National Laboratory, Argonne, IL
Doreen Ware
,
Cold Spring Harbor Laboratory/USDA-ARS, NY, NY
Sergei Maslov
,
Brookhaven National Laboratory, Upton, NY
Shinjae Yoo
,
Brookhaven National Laboratory, Upton, NY
Dantong Yu
,
Brookhaven National Laboratory, Upton, NY
Michael Schatz
,
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
James Gurtowski
,
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Sunita Kumari
,
Cold Spring Harbor Laboratory, NY, NY
Shiran Pasternak
,
Cold Spring Harbor Lab, Cold Spring Harbor, NY
Jim Thomason
,
Cold Spring Harbor Laboratory, NY, NY
Mark Gerstein
,
Yale University, New Haven, CT
Gang Fang
,
Yale University
Darryl Reeves
,
Yale University, New Haven, CT
Pamela Ronald
,
Joint BioEnergy Institute (JBEI), Emeryville, CA
TaeYun Oh
,
University of California, Davis, Davis, CA
Christopher Henry
,
Argonne National Laboratory, Argonne, IL
Dave Weston
,
Oak Ridge National Laboratory, Oak Ridge, TN
Priya Ranjan
,
Oak Ridge National Laboratory, Oak Ridge, TN
Mustafa Syed
,
Oak Ridge National Laboratory, Oak Ridge, TN
Miriam Land
,
Oak Ridge National Laboratory, Oak Ridge, TN
Adam Arkin
,
Lawrence Berkeley National Laboratory, Berkeley, CA
The U.S. Department of Energy Systems Biology Knowledgebase (KBase, http://kbase.us) is a collaborative effort designed to accelerate our understanding of microbes, microbial communities, and plants. It is a community-driven, extensible and scalable open-source software framework, and application system. KBase offers free and open access to data, models and simulations, enabling scientists and researchers to build new knowledge, test hypotheses, design experiments, and share their findings.
KBase integrates commonly used core tools, reference and experimental data, and overlays them with new capabilities for visualization, exploration, and predictive analysis with KBase-generated recommendations. KBase is distinguished from a database or existing biological tools by its focus on interpreting missing information necessary for predictive modeling, on aiding experimental design to test model‐based hypotheses, and by delivering quality‐controlled data. The project leverages the power of cloud-based high‐performance computing resources across the USDOE system of labs to handle the anticipated rapid growth in data volumes and computing requirements of the community.
KBase empowers bioenergy science with a variety of plants resources and analytical services. Reference plant genomes such as poplar and Arabidopsis are integrated with phenotype experiments, gene expression profiles, regulatory, interaction, and metabolic networks, for building models and generating new hypotheses. User-furnished data can be uploaded, analyzed using high-performance bioinformatics tools, and overlaid on the data already available, visually and analytically. As the project matures, partnerships with plants resources such as iPlant and Gramene/Ensembl will lead to a broader research platform for predictive plant and microbial biology.