in 2011 BER launched the DOE System Biology Knowledgebase (KBase, KBase.us). KBase is an open-source and open-architecture computational environment for integrating large, diverse datasets generated by the scientific community and for using this information to advance predictive understanding, manipulation, and design of biological processes in an environmental context. KBase is meant to be a community resource that enables users to integrate a wide spectrum of genomics and systems biology data, models, and information for microbes, microbial communities, and plants. Powerful tools available within KBase can analyze and simulate data to predict biological behavior, generate and test hypotheses, design new biological functions, and propose new experiments.
KBase provides predictive biology capabilities based in part on a common data model that is expected to be extended over time to support more comprehensive and accurate models of biological systems. To achieve high-quality predictions, it is imperative that the data and associated metadata be also high quality. Currently, the supported high-level data types include genomes (bacteria, archea, eukaryotes), transcriptomes, phenotypes, 16s amplicons, metagenomes, proteomes (mapped to genomes), variation, and interactomes. KBase will be pulling these data types from existing international repositories. Thus, data submitted to these standard resources will ultimately be integrated into the KBase system. It is possible to submit certain forms of the above data directly to KBase (see https://www.kbase.us/about/kbase-data-policy/kbase-data-types/), although integration into KBase must pass minimal quality and metadata standards before acceptance.
February 2013: First Major KBase Public Release is Now Available.
May 2012: KBase website launches Alpha Release with draft tutorials, database loads, unified prototypes, workflow drafts, cloud and cluster services.
Leading the collaboration will be principal investigator Adam Arkin of Lawrence Berkeley National Laboratory (LBNL), with co-principal investigators Rick Stevens of Argonne National Laboratory (ANL), Robert Cottingham of Oak Ridge National Laboratory (ORNL), and Sergei Maslov of Brookhaven National Laboratory. Also participating as investigators in the multi-institutional program are Pamela Ronald of the University of California, Davis; Matthew DeJongh of Hope College in Michigan; Gary Olsen of the University of Illinois at Urbana-Champaign; Doreen Ware of the Cold Spring Harbor Laboratory; and Mark Gerstein of Yale University.
2010: BER awarded funding to 11 university-led projects for research to develop new computational biology and bioinformatic methods to enable KBase. These awards were given in response to Funding Opportunity Announcement DE-FOA-0000143. (Learn more about these university projects and the completed Recovery Act pilots.)
September 2010: DOE Systems Biology Knowledgebase Implementation Plan published.
2009: With funding from the American Recovery and Reinvestment Act, a year-long R&D project was carried out to support the conceptual design and implementation planning necessary to develop KBase. Completed in September 2010, this effort included a series of community planning workshops and five pilot projects. Together, these workshops and pilots informed the scientific objectives, software requirements, and design approaches detailed in the DOE Systems Biology Knowledgebase Implementation Plan, the final product of the R&D project.
May 2008: DOE BER holds computing workshop on GTL Systems Biology Knowledgebase. The subsequent report was published in March 2009.
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