Developing advanced computational tools for analyzing genomic data
Official title: Scalable Computational Methods for Genealogical Inference: from species level to single cells
Funded at University of California Berkeley · NIH-10981028 · Data updated 2026-11-12
This study is working on new computer tools to help scientists make sense of large amounts of genetic information, which could lead to exciting discoveries about how our bodies work and improve health for everyone.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Berkeley NIH-funded |
| Lab location | 1 site (Berkeley, United States) |
| Project start | September 10, 2024 |
| Project end | June 30, 2028 |
| Funding year | 2024 |
| Award (this year) | $592,707 |
| NIH project number | 1R01HG013117-01A1 |
| Project ID | NIH-10981028 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with genetic conditions or those interested in the genetic basis of diseases.
✕Less likely to help
Patients without genetic conditions or those not interested in genetic research may not benefit from this research.
What this research studies
This research focuses on creating innovative computational methods to analyze vast amounts of genomic data, which can help biomedical researchers understand biological processes better. By developing scalable algorithms and user-friendly software, the project aims to improve the accuracy of genealogical inference from genetic sequences. This will enable researchers to make new biological discoveries that could enhance human health. The approach includes using probabilistic models and efficient estimation techniques to interpret complex genomic information.
Why it matters
Potential benefit: If successful, this research could lead to breakthroughs in understanding genetic diseases and improving personalized medicine.
How similar studies have gone: Other research has shown success in using computational methods for genomic analysis, indicating that this approach is promising and builds on established techniques.
Where this research is happening
-
University of California Berkeley
Berkeley, United States Active
Researchers
- Principal investigator: Song, Yun S — University of California Berkeley
- Study coordinator: Song, Yun S
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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.