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Our laboratory develops statistical and computational methods for the analysis of massive genomic datasets. We are interested in genome evolution, in particular identifying genome sequences that differ significantly between or within species and their relationship to biomedical traits of interest. We pioneered the statistical phylogenetic approach for identifying Human Accelerated Regions (HARs), the fastest evolving sequences in the human genome. Most HARs are non-coding elements, such as regulatory signals, structural sites, and RNA genes. One of our aims is to identify specific DNA alterations in HARs that are responsible for variation in gene expression.
We are also developing methods for characterizing microbial communities from metagenomic data, the pool of DNA from different microorganisms in a sample. We designed PhylOTU, the first computational tool for estimating the taxonomic composition of metagenomic samples from short, next-generation sequencing reads. Our current emphasis is to extend this approach to measure the functional composition of microbial communities. The goal of this project is to relate DNA based measurements of microbial communities from the human gut and other body sites to patient health status through niche modeling.
- American Society of Human Genetics
- American Statistical Association
- International Society for Computational Biology
- Pomona College
- University of California, Berkeley
- "University of California, Berkeley"