Category
Description
Welcome to the PPI Project Dataset Catalog Collection!
The Predictive Phenomics Initiative (PPI) is an internal LDRD investment at Pacific Northwest National Laboratory focused on unraveling the mysteries of molecular function in complex biological systems. Explore PPI research project data package downloads, containing processed data results (and curated experimental metadata) linked to raw measurement dataset accessions, software source code analysis deliverables, and corresponding peer-reviewed publications where applicable. For a complete catalog of available PPI project software source code, supporting PPI data analysis and reuse, visit the PPI GitHub and corresponding PPI Zenodo Community.
PPI Research Project Thrust Areas
- TA1 - Enhancing Multi-Scale Phenomics Measurements
- TA2 - Science Drivers for Collaboration
- TA3 - Computational Methods for Predicting & Controlling the Phenome
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 (as outlined by the FORCE11 Data Citation Principles), we ask that all reuse of project data and metadata download materials acknowledge all primary and secondary dataset citations and corresponding journal articles where applicable.
Data Licensing
Creative Commons Attribution 4.0 International (CC BY 4.0)
Last updated on 2025-07-14T18:56:42+00:00 by LN Anderson
Projects (12)
The research goal of this project is to use stimuli-specific, synthetic nanobodies to target functional mediators without prior knowledge of the response networks or manipulating the biological system.
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0
The research objective of this project is to develop an integrative and automated multi-PTM profiling capability with deep proteome coverage.
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Datasets
2
The science objective of this project is to apply structural proteomics technologies to map the molecular interactome.
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Datasets
5
The science objectives of this project are to: Functionally enrich microbial communities and generate multi-omics to correlate biochemical mechanisms to activity. Integrate PhenoProfiling with Thrust Areas 2 and 3 to develop models for phenotype prediction and interspecies interactions. Evaluate...
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Datasets
1
The research goal of this project is to develop computational methods to predict cell regulation phenotypes using small molecule and proteome data to understand outcomes in complex biological systems.
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0
By developing explainable, predictive metabolic models of individual microbes, we aim to design consortia that convert light and abundant atmospheric gases into high-value molecules through microbial division of labor.
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0
The research goal of this project is to construct and streamline an approach to identify phenotype-relevant signatures by integrating various proteomics data. Leveraging protein structures and interaction networks, we will map structural changes and post-translational modifications to identify...
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0
The research goal of this project is to establish model synthetic microbial communities to understand the rules regulating their biological function in order to utilize them as next generation bioproduction platforms capable of reducing carbon and nitrogen footprints in biomanufacturing processes.
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Datasets
2
The research goal of this project is to develop a biologically informed machine learning (ML) model that integrates datasets from different studies, and leverages current biological knowledge in an automated manner, to improve predictions in biological data analysis.
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0
The research goal of this project is to develop new theory and tools that leverage evolutionary perspectives and knowledge of the energetics of reactions to predict the most likely regulation in a given environment. These methods will accelerate exploration, modeling and understanding of cell...
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Datasets
3
The research goal of this project is to build and understand model communities that show carbon storage phenotypes
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Datasets
0
The research goal of this project is to identify and control host functions hijacked during viral infection through use of PNNL ‘omics technologies and modeling capabilities.
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5
