Faster, more accurate computer tools to read microbes in environmental and clinical samples
Phylogenetic and computational methods for accurate and efficient analyses of large-scale metagenomics datasets
This project develops faster, more accurate software to identify microbes, including SARS‑CoV‑2, from large DNA sequencing datasets to help public health and environmental monitoring.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Hawaii at Manoa NIH-funded |
| Lab location | 1 site (Honolulu, United States) |
| Project ID | NIH-11299048 on NIH RePORTER |
What this research studies
You can think of this work as building better computer tools that look through huge collections of genetic data to find which microbes are present and how they relate to known species. The team will improve and use an open‑source program called tronko that places genetic sequences on a tree of life so researchers can classify organisms more reliably and scale to very large reference databases. They will apply statistical and computational methods, including rigorous species delineation and phylogenetic placement, to make analyses faster and more accurate. Part of the project focuses on using these tools to improve detection and composition estimates of SARS‑CoV‑2 from environmental samples like wastewater.
Who could benefit from this research
Good fit: Ideal participants are laboratories, public‑health programs, or communities that can provide environmental or clinical sequencing samples (for example wastewater surveillance programs or clinics sharing viral sequences).
Not a fit: People who are not part of environmental surveillance efforts or whose care does not rely on genomic monitoring are unlikely to see direct benefits from this work.
Why it matters
Potential benefit: If successful, the tools could make environmental and clinical surveillance more reliable and faster, helping detect outbreaks earlier and guide public health actions.
How similar studies have performed: Related computational methods and wastewater surveillance have previously helped track SARS‑CoV‑2, but this project applies a novel, scalable phylogenetic placement approach to handle much larger reference databases.
Where this research is happening
Honolulu, United States
- University of Hawaii at Manoa — Honolulu, United States (Active)
Researchers
- Principal investigator: Pipes, Lenore — University of Hawaii at Manoa
- Study coordinator: Pipes, Lenore
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.