Category
Description
The research goal of this project is to develop a computational approach known as Variation-leveraged Phenomic Association Study (VaLPAS) to address the challenge of using functional dark matter (proteins, metabolites, lipids) for bioeconomy applications.
The project has four objectives:
1. development of the VaLPAS framework based on existing multi-omic data sets from the Agile BioFoundry.
2. application of VaLPAS to the fungal omics data collected in the PPI project "Leveraging Phenomics to Accelerate Improvement in Bioprocess Robustness" to provide predictions of proteins and pathways associated with industrial scale-up relevant stress responses that impact production of lipids and other bioproducts (secondary metabolites)
3. application of the VaLPAS framework to bacterial and/or algal datasets to demonstrate generalizability.
4. establishment of controlled, systematic data matrices to provide a basis for future AI/ML studies aimed at understanding the contribution of metabolic function to phenotypic outcomes.
Funding Acknowledgments
The research data described here was funded by the Predictive Phenomics Science & Technology Initiative (PPI), conducted under the Laboratory Directed Research and Development Program, at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory, operated by Battelle, U.S. Department of Energy, Office of Science under Award Number DE-AC05-76RL01830.
Citation Policy
In efforts to enable discovery, reproducibility, and reuse of PPI-funded project dataset citations in accordance with best practices (e.g., FORCE11 Data and Software Citation Principles), we ask that all reuse of project data packages and linked materials acknowledge all primary and secondary dataset citations, source code citations, and corresponding peer-reviewed journal articles where applicable.
Data Licensing