Epigenomics and Gene Expression Control
Our lab applies our expertise in human pluripotent stem cell research and genomics to understand the molecular mechanisms underlying normal and abnormal human development, in order to improve the health of mothers and babies.
We are interested in the analysis and modeling of the three-dimensional chromatin structure from high-throughput sequencing experiments. We develop methods that are based in statistics, machine learning, optimization and graph theory to understand how changes in the 3D genome affect cellular outcome such as development, differentiation and gene expression. We have ongoing interests in the systems level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression and integration of three-dimensional chromatin structure with one-dimensional epigenetic measurements in the context of cancer, malaria, asthma and several autoimmune diseases.
Our overall goal is to understand how chromatin structure is employed in making cellular fate decisions, its dynamics, and how it is shaped and maintained by different chromatin regulators (CRs). We merge basic biology, genomics and technology development.
The Gaulton lab studies the effects of human genetic variation on gene regulation and diabetes risk. We use computational and statistical methods to integrate genome sequence information with epigenomic annotation and molecular QTL data.
The McVicker laboratory aims to understand how chromatin state and organization are encoded by the human genome. Our approach to this problem is to exploit naturally occurring human genetic variation to identify sequence variants that disrupt chromatin function. We are currently focused on chromatin within immune cells and we are also interested in how variants that affect chromatin and gene regulation lead to disease risk. The problems that we work on often require the development of sophisticated computational and statistical methods that can extract subtle signals from noisy experimental data.