Professor of Gene Regulation
Gene regulation in health and disease.
We study the basic biology of how genes are regulated in the mammalian genome in concert with how sequence variation in the human population affects this and predisposes towards disease. Due to the PI's combined molecular and bioinformatic background, the group uses a fusion of molecular genomics, genome engineering, synthetic biology, computational biology, and machine learning approaches as tools in its research. We have a track record in developing genomics-based technologies to investigate genome biology such as the Capture-C family of Chromosome Conformation Capture (3C) technologies, transcriptomic methods such as scaRNA-seq, as well as Machine Learning approaches such as deepC to predict function from genome sequence.
Our recent work has shown that enhancer elements predominately control the loading or initiation of Pol II, rather than polymerase pausing, at gene promoters during cellular differentiation. We have also shown that this activity is independent of another important class of genomic elements, CTCF sites, which instead act to prevent the misregulation of surrounding genes. We have also shown that multiple enhancers and promoters cluster in the 3D space of the nucleus to form regulatory hubs, which formed concurrently with gene activation. Combining this understanding with molecular techniques and machine learning approaches we have produced an end-to-end framework capable of interpreting the effects of human sequence variation on gene expression that underlie common human disease.