Aaron J. Mackey
- Assistant Professor, Public Health Sciences
Statistical and computational genomics, including next-gen SNP discovery, comparative gene structure prediction, functional RNA-seq and ChIP-seq analyses.
The advent of ultra-high throughput second-generation DNA sequencing has provided an ever-increasingly detailed view of the human genome, from both a structural and functional perspective. Our research leverages such genomic datasets to expose the biological processes underlying complex biology. Ongoing projects in the lab include: discovery of genetic polymorphisms associated with multigenic human diseases (type 1 diabetes and cancer); genomic studies of human immune system development and response to infectious disease; and the development of bioinformatic algorithms for sensitive and accurate consensus gene structure prediction across closely-related organisms (e.g. Plasmodium, Drosophila, and primates). We also collaborate on a variety of functional genomics projects using "next-gen" sequencing methods for ChIP-seq, RNA-seq, MeDIP-seq, CLIP-seq, etc. For most projects, we develop and employ state-of-the art statistical machine learning methods, customized to integrate and interrogate genomic data specific to the particular experimental context.