Current Research and Scholarly Interests
Our research focusses on development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for
1. Adaptive thresholding and normalization of massive collections of functional genomic data (e.g. ChIP-seq and DNase-seq)
2. Dissecting combinatorial transcription factor co-occupancy within and across cell-types
3. Predicting cell-type specific enhancers from chromatin state profiles
4. Exploiting expression and chromatin co-dynamics with to predict enhancer-target gene links
5. Jointly modeling sequence grammars at regulatory elements and their chromatin state dynamics, expression changes of regulators and functional interaction data to learn unified multi-scale gene regulation programs
6. Elucidating the heterogeneity of chromatin architecture at regulatory elements
7. Improving the detection and interpretation of potentially causal disease-associated variants from Genome-wide association studies
More recently, we have also been developing methods to
1. Decipher the functional heterogeneity of transcription factor binding
2. Learn long-range, three-dimensional regulatory interactions
3. Infer causal regulatory mechansisms by integrating diverse functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments
4. Model the complex relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals
5. Deep learning frameworks for genomics
