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Anshul Kundaje is Associate Professor of Genetics and Computer Science at Stanford University. His primary research area is large-scale computational regulatory genomics. The Kundaje lab specializes in developing statistical and machine learning methods for large-scale integrative analysis of heterogeneous, high-throughput functional genomic and genetic data to decipher regulatory elements and long-range regulatory interactions, learn predictive regulatory network models across individuals, cell-types and species and improve detection and interpretation of natural and disease-associated genetic variation. Previously as a postdoc at Stanford and Research Scientist at MIT, Anshul was the lead computational analyst of the ENCODE Project and the Roadmap Epigenomics Project. Anshul is also a recipient of the 2016 NIH Director's New Innovator Award and the 2014 Alfred Sloan Fellowship.
The project generates a resource of cell-type specific genome-wide regulatory maps in the human genome. We develop statistical processing methods for next-gen sequencing based functional genomic data and machine learning methods to predict regulatory events, learn combinatorial regulatory effects of transcription factors, cell-type specific regulatory networks
The project generates genome-wide epigenomic maps in 200 human cell types. We develop computational methods and analyses to infer cell-type specific regulatory elements (e.g. enhancers) and their activity states, learn cell-type specific regulatory networks and use these maps to interpret GWAS and disease studies.
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-types3. Predicting cell-type specific enhancers from chromatin state profiles4. Exploiting expression and chromatin co-dynamics with to predict enhancer-target gene links5. 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 programs6. Elucidating the heterogeneity of chromatin architecture at regulatory elements7. Improving the detection and interpretation of potentially causal disease-associated variants from Genome-wide association studiesMore recently, we have also been developing methods to 1. Decipher the functional heterogeneity of transcription factor binding2. Learn long-range, three-dimensional regulatory interactions3. 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) experiments4. Model the complex relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals5. Deep learning frameworks for genomics