Recently, we have made significant progress in understanding how DNA sequence features specify cell-type specific mammalian enhancer activity by using kmer-based SVM machine learning approaches. Our work uses functional genomics DNase-seq, ChIP-seq, RNA-seq, and chromatin state data to computationally identify combinations of transcription factor binding sites which operate to define the activity of cell-type specific enhancers. Our models are then used to predict the impact of regulatory variation associated with common human disease, and have been validated in a wide range of cell-specific reporter assays.
Associate Professor of Biomedical Engineering and McKusick-Nathans Institute of Genetic Medicine
Specialization: Computational Regulatory Genomics
The ultimate goal of our research is to understand how gene regulatory information is encoded in genomic DNA sequence.
To advance neuroscience discovery by uniting neuroscience, engineering and computational data science to understand the structure and function of the brain.