Yale University, School of Medicine
Recent advances in biotechnology and medicine allow us to collect an immense amount of omics data at the personalized and population level. This surge in data gives rise to a paradigm shift in biology and medicine towards data intensive discoveries. While this provides the perfect opportunity to study human biology and disease, it also presents daunting challenges in data analysis, privacy, and sharing at scale. First, I will discuss the scalable tools I have developed to overcome privacy concerns associated with sharing functional genomics data. These tools are based on statistical genetic privacy threats and data sanitization grounded in privacy and utility. Second, I will discuss the computational tools I have developed to address the challenge of high-throughput functional genomics data analysis. These tools focus on understanding the relationship between nuclear organization, chromatin state, and gene expression based on both reference and personal genomes.