I direct the Oregon National Primate Research Center’s Bioinformatics & Biostatistics Core. We specialize in computational analysis of omics data, particularly RNA- and DNA-sequencing, quantitative analysis, and variant calling. I have been studying and working in the field of bioinformatics since I discovered it as a freshman in college in 2000. As an undergraduate, I double majored in computer science and biochemistry/biophysics. Alongside my Ph.D. in biomedical informatics, I pursued a graduate certificate in applied statistics. For my Ph.D. dissertation, I generated a quantitative proteomics dataset from the brain striatum of mouse strains widely divergent in alcohol drinking behavior. I integrated the proteomics dataset with genetic QTL and transcriptomic RNA-seq data from the same strains and showed their complementarity. For my postdoctoral training, I focused on cancer genome sequencing, variant calling, and integration with RNA-seq data. I led a large whole genome sequencing kidney cancer project and served on two kidney and one pancreatic analysis working groups for The Cancer Genome Atlas (TCGA). After my postdoc, I worked as an analyst in my current core for 2.5 years before transitioning to my current role as director. In my role as director of the core, I analyze data, help design omics experiments, and supervise/train other analysts in the analysis of omics data.    

In the core, I have analyzed RNA- and DNA-seq data for diverse projects in many different species. RNA-seq projects are the most common project type in the core, and since joining the core, I have been involved in the analysis of at least 20 RNA-seq projects, primarily to identify differentially expressed genes and pathways between experimental groups. DNA-seq projects are also common and typically involve identifying variants in single samples compared to the reference genome or between multiple samples compared to each other. As new technologies become available, more researchers are utilizing low input and single cell sequencing. We now have expertise and pipelines established in the core to cover copy number and SNP-based analysis in single-cell low-input DNA-seq as well as single-cell RNA-seq using low-input or 10x technologies, including CITE-seq. Our years of experience and complementary expertise in the core allow us to efficiently meet the most common bioinformatics needs of biomedical researchers utilizing genomics.