To the Editor: Over the past two decades, the scale and complexity of genomics technologies and data have advanced from sequencing genomes of a few organisms to generating metagenomes, genome variation, gene expression, metabolites, and phenotype data for thousands of organisms and their communities.A major challenge in this data-rich age of biology is integrating heterogeneous and distributed data into predictive models of biological function, ranging from a single gene to entire organisms and their ecologies.The US Department of Energy (DOE) has invested substantially in efforts to understand the complex interplay between biological and abiotic processes that influence soil, water, and environmental dynamics of our biosphere.The community that has grown around these efforts recognizes the need for scientists of diverse backgrounds to have access to sophisticated computational tools that enable them to analyze complex and heterogeneous data sets and integrate their data and results effectively with the work of others.In this way, new data and conclusions can be rapidly propagated across existing, related analyses and easily discovered by the community for evaluation and comparison with previous results 1-3 .Here we present the DOE Systems Biology Knowledgebase (KBase, http://kbase.us),an open-source software and data platform that enables data sharing, integration, and analysis of microbes, plants, and their communities.KBase maintains an internal reference database that consolidates information from widely used external data repositories.This includes over 90,000 microbial genomes from RefSeq 4 , over 50 plant genomes from Phytozome 5 , over 300 Biolog media formulations 6 , and >30,000 reactions and compounds from KEGG 7 , BIGG 8 , and MetaCyc 9 .These public data are available for integration with user data where appropriate (e.g., genome comparison or building species trees).KBase links these diverse data types with a range of analytical functions within a web-based user interface.This extensive community resource facilitates large-scale analyses on scalable computing infrastructure and has