The Department of Energy (DOE) funds different Science Focus Areas (SFAs) among the national laboratories to further science, pool each laboratory’s strengths, and encourage collaborations. The research efforts of these SFAs support the goals and mission of the Office of Biological and Environmental Research (BER). Currently, KBase has ongoing collaborations with five of these SFAs. The combined research efforts of these collaborations span more than 90 people across 7 national laboratories, 17 universities, and 2 DOE User Facilities. See summaries of each of the SFAs below, and follow the links to learn more.
Bacterial-fungal interactions within the soil are the focus of this SFA, located at Los Alamos National Laboratory (LANL). Learn more.
The ENIGMA SFA is located at Lawrence Berkeley National Laboratory (LBNL) and strives to understand the relationship between molecules, microbes, communities, and the ecosystems they inhabit. Learn more.
The first of two SFAs located at Lawrence Livermore National Laboratory (LLNL), the Biofuels SFA is focused on the community systems biology of microbial consortia that are closely associated with bioenergy-relevant plants and algae. Learn more.
Also located at LLNL, the Soil Microbiome SFA studies soil metagenomes to uncover the mechanisms of the critical role that microbes play in the carbon cycle within the soil. Learn more.
Studying lignocellulosic biomass and how it can be used to generate renewable biofuels is the focus of this SFA, located at Oak Ridge National Laboratory (ORNL). Learn more.
KBase is working with Dr. Dan Jacobson and his lab at ORNL to integrate data from various sources to construct multi-layer ensemble networks. These models allow data from multiple sources, such as the 1001 Genomes Project, to be integrated into the KBase relation engine.
There is a large and growing body of data for model species in all levels of life, from genome-level sequencing to protein-protein interactions. However, each individual data layer provides only limited insights. This project seeks to integrate the data from many layers and sources to understand how these layers interact in the context of the entire system. The collaboration is focusing on the wealth of community data from the primary plant model species, Arabidopsis thaliana. Using supercomputing resources available at ORNL, the Jacobson Group will develop network models to connect the diverse data layers, and the KBase team at Lawrence Berkeley National Lab will integrate these data into KBase. The main aims of the collaboration include:
Aim 1: Generate multiple ‘omic networks in Arabidopsis using supercomputing for integration into KBase.
The Jacobson group will be developing multiple networks connecting different data layers, such as genome-wide association studies (GWAS) on 1,100 A. thaliana leaf transcriptomes, and Genotype by Environment Association (GxE), which relates environmental conditions and genetic variation to phenotypes.
Once the networks are generated, they will be integrated into the KBase environment allowing users access to both the generated networks and the underlying data.
Aim 2: Develop a systems-biology resource enabling KBase users to rank candidate genes and to predict the function of unknown genes.
The networks created in Aim 1 will utilize the entirety of these data. However, such high-throughput data tend to reduce data complexity. The project will identify subsets of variables that strongly correlate with each other to extract higher order interactions.
Our SFA collaborations benefit KBase users and SFAs alike through the development and implementation of new and exciting tools. To make the process of adding the SFA’s tools to KBase flow smoothly, each collaboration begins with a week-long in-person software development kit (SDK) training session. This is followed by continual developmental support to ensure SFAs are able to build, test, and deploy Apps on the KBase platform. All Apps are open source and available to everyone with a free KBase account. More information on our SDK can be found here.