Community-Driven Cyberinfrastructure for Sharing and Integrating Data and Analytical Tools to Accelerate Predictive Biology
The Department of Energy Systems Biology Knowledgebase (KBase) is an open-software FAIR biological data platform that aims to enable researchers to collaboratively drive discovery for prediction, control, and design of function in plants, microbes, and their communities. KBase’s unified data model allows users to perform integrated analyses across plants, microbes, and their communities with a wide range of tools that interoperate across the tree of life, and to publish their data, methods, results, and thoughts in persistent, citable, executable, and reusable electronic narratives that allow scientists to build on the work of others. KBase’s open platform enables external developers to integrate their analysis tools, facilitating distribution, comparative tool analysis, and access to enterprise-class computing.
GSP Science Focus Area (SFA) Collaborations
These collaborations benefit KBase users and SFAs alike through the development and implementation of new and exciting tools. To smooth the process of adding SFA tools to KBase, 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.
Omics-Enabled Global Gapfilling (OMEGGA) for Phenotype-Consistent Metabolic Network Reconstruction of Microorganisms and Communities
- SFA: Phenotypic Response of the Soil Microbiome to Environmental Perturbations
- KBase Collaboration: https://www.kbase.us/research/hofmockel-sfa/
- PI: Kirsten Hofmockel, PNNL
Building pipelines for long read assembly of microbial isolates and metagenomes in the DOE Systems Biology KnowledgeBase
- SFA: ENIGMA: Ecosystems and Networks Integrated with Genes and Molecular Assemblies
- KBase Collaboration: https://www.kbase.us/research/adams-sfa/
- PI: Paul Adams, LBNL
Improved Protein Annotation in KBase Using Machine Learning, Multi-Omics Data Integration, and Structural Prediction
- SFA: Persistence Control of Engineered Functions in Complex Soil Microbiomes
- KBase Collaboration:https://www.kbase.us/research/egbert-sfa/
- PI: Robert Egbert, PNNL
Design and Omics Exploration of Synthetic Microbial Communities
- SFA: Plant-Microbe Interfaces (PMI)
- KBase Collaboration: https://www.kbase.us/research/doktycz-sfa/
- PI: Mitch Docktycz, ORNL
Probabilistic Annotation and Ensemble Metabolic Modeling in KBase
- SFA: A Systems Biology Approach to Interactions and Resource Allocation in Bioenergy-Relevant Microbial Communities
- KBase Collaboration: https://www.kbase.us/research/stuart-sfa/
- PI: Rhona Stuart, LLNL
Microbes Persist: Towards quantitative theory-based predictions of soil microbial fitness, interaction and function in KBase
- SFA: Microbes Persist: Systems Biology of the Soil Microbiome
- KBase Collaboration: https://www.kbase.us/research/pett-ridge-sfa/
- PI: Jennifer Pett-Ridge, LLNL
See additional information about ongoing SFA collaborations on the KBase Website.