A universal, cloud-based data hub for metabolomics data processing
Official title: A reproducible database for untargeted metabolomics data processing across laboratories
Funded at University of California at Davis · NIH-11382889 · Data updated 2026-11-12
A cloud-based workflow and shared database will harmonize and standardize metabolomics data from different labs to make results more reliable and easier to interpret for biomedical researchers.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California at Davis NIH-funded |
| Lab location | 1 site (Davis, United States) |
| Project start | September 15, 2024 |
| Project end | July 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $501,930 |
| NIH project number | 5R01GM155383-03 |
| Project ID | NIH-11382889 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not a patient-facing eligibility criterion in this grant; the work is method development for metabolomics data.
✕Less likely to help
The project does not involve direct patient participation or clinical testing.
What this research studies
This project creates LC-BinBase, a unified data processing workflow and database for high-resolution metabolomics and lipidomics data generated by LC-MS/MS. It aims to enable across-lab compatibility by validating that different laboratories can produce consensus data when using standardized acquisition methods. The researchers will develop algorithms to assign metabolite confidence scores using retention times, masses, MS/MS similarity, and ion mobility. They will test usability by comparing results across laboratories and instruments from multiple vendors. The work could lead to faster, more reproducible data processing and clearer reports for biomedical researchers to interpret.
Why it matters
Potential benefit: If successful, patients could benefit from more reliable metabolomics findings that support faster translation of biomarkers and therapies.
How similar studies have gone: Past studies have shown that standardized data processing improves reproducibility across labs, but this project advances a unified, cloud-based approach for broad adoption.
Where this research is happening
-
University of California at Davis
Davis, United States Active
Researchers
- Principal investigator: Fiehn, Oliver — University of California at Davis
- Study coordinator: Fiehn, Oliver
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.