How microbes in your body change with health, time, and environment
DMS/NIGMS 2: Bayesian Statistical Methods for Comprehensive Inferences on Microbial Community Dynamics Using High-Throughput Sequencing Data
['FUNDING_R01'] · UNIVERSITY OF CALIFORNIA SANTA CRUZ · NIH-11195682
This project develops new statistical tools to better read microbiome sequencing data so researchers can learn how microbial communities shift with health, time, and the environment.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF CALIFORNIA SANTA CRUZ (nih funded) |
| Locations | 1 site (SANTA CRUZ, UNITED STATES) |
| Trial ID | NIH-11195682 on ClinicalTrials.gov |
What this research studies
Researchers are building new Bayesian statistical methods to make sense of complex microbiome sequencing data from people and environments. The work will combine different types of 'omics' data and handle repeated samples over time to track how microbial communities evolve. The methods focus on multivariate count data and aim to give clearer estimates with honest measures of uncertainty. If you join related studies you might be asked to provide stool, skin, or other samples or share clinical and environmental information to help the models learn.
Who could benefit from this research
Good fit: Ideal candidates are people willing to contribute microbiome samples (for example stool, oral, or skin) and clinical or lifestyle information to longitudinal or multi-omics research studies.
Not a fit: People seeking immediate treatment benefits or those not participating in microbiome sampling studies are unlikely to receive direct benefit from this methods-focused project.
Why it matters
Potential benefit: Could lead to clearer links between microbiome changes and health, pointing to new targets for prevention or personalized treatments.
How similar studies have performed: Related Bayesian and statistical approaches have been used in microbiome research before, but this project aims to create more flexible, general methods for longitudinal and multi-omics settings and improved uncertainty estimation.
Where this research is happening
SANTA CRUZ, UNITED STATES
- UNIVERSITY OF CALIFORNIA SANTA CRUZ — SANTA CRUZ, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LEE, JU HEE — UNIVERSITY OF CALIFORNIA SANTA CRUZ
- Study coordinator: LEE, JU HEE
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 below.