Developing statistical methods to understand microbial communities using advanced sequencing data
Official title: DMS/NIGMS 2: Bayesian Statistical Methods for Comprehensive Inferences on Microbial Community Dynamics Using High-Throughput Sequencing Data
Funded at University of California Santa Cruz · NIH-11043535 · Data updated 2026-11-12
This study is looking at how tiny microbes in our bodies work together and affect our health by using advanced methods to analyze complex data, which could help us understand their role in diseases and improve treatments.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Santa Cruz NIH-funded |
| Lab location | 1 site (Santa Cruz, United States) |
| Project start | September 20, 2024 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $133,246 |
| NIH project number | 1R01GM157612-01 |
| Project ID | NIH-11043535 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals involved in microbiome studies or those with conditions influenced by microbial dynamics.
✕Less likely to help
Patients who do not have any microbiome-related health issues or are not participating in microbiome studies may not benefit from this research.
What this research studies
This research focuses on creating advanced Bayesian statistical methods to analyze and interpret complex data from microbial communities. By utilizing high-throughput sequencing data, the project aims to uncover how these microbial communities function and interact with their environments. The researchers will address challenges in modeling this data, particularly in longitudinal and multi-omics studies, to provide more accurate insights into microbial dynamics. This could lead to better understanding of the microbiome's role in health and disease management.
Why it matters
Potential benefit: If successful, this research could lead to improved strategies for managing health and disease through a better understanding of microbial communities.
How similar studies have gone: Other research has shown success in using Bayesian methods for analyzing complex biological data, indicating a promising approach in this area.
Where this research is happening
-
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
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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.