In 2011, DOE Office of Biological and Environmental Research (BER) launched KBase (KBase.us), an open-source, open-architecture software and data environment for systems biology research. . As a community resource, KBase provides a computational framework and tools for integrating and analyzing large, diverse datasets generated by the scientific community to advance predictive understanding, manipulation, and design of biological processes in an environmental context. The purpose of KBase is to enable users to integrate a wide spectrum of genomics and systems biology data, models, and information for microbes, microbial communities, and plants. Powerful tools within KBase will allow users to 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 data and associated metadata also be high quality. High-level data types currently supported by KBase include genomes (of bacteria, archea, and eukaryotes), metagenomes, transcriptomes, proteomes (mapped to genomes), interactomes, phenotypes, 16s amplicons, expression data, enzymes, ontologies, pathway data, protein annotations, protein-protein interactions, regulons, and ribosomes. Because KBase pulls these data types from existing international repositories, future data submitted to these standard resources ultimately will be integrated into the KBase system. Submitting certain data types directly to KBase is possible (see https://www.kbase.us/about/kbase-data-policy/kbase-data-types/), but they 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.
July 2011: KBase funded. National Laboratories and Universities Team up to Build a Community Systems Biology Knowledgebase.
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.
September 2010: DOE Systems Biology Knowledgebase Implementation Plan published as the capstone product of the 2009 ARRA project outlined below. The document was based on community duscussions of KBase and includes issues pertinent to its development.
2009: With funding from the American Recovery and Reinvestment Act (ARRA), 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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