Su-In Lee, Ph.D.

Assistant Professor of Computer Science and Engineering, and Genome Sciences, University of Washington

“Learning the human chromatin network from all ENCODE ChIP-seq data”

Abstract:

Introduction: A cell’s epigenome arises from interactions among regulatory factors — transcription factors, histone modifications, and other DNA-associated proteins — co-localized at particular genomic regions.  Identifying the network of interactions among regulatory factors, the chromatin network, is of paramount importance in understanding epigenome regulation.

Methods: We developed a novel computational approach, ChromNet, to infer the chromatin network from a set of ChIP-seq datasets.  ChromNet has four key features that enable its use on large collections of ChIP-seq data.  First, rather than using pairwise co-localization of factors along the genome, ChromNet identifies conditional dependence relationships that better discriminate direct and indirect interactions.  Second, our novel statistical technique, the group graphical model, improves inference of conditional dependence on highly correlated datasets.  Such datasets are common because some transcription factors form a complex and the same transcription factor is often assayed in different laboratories or cell types.  Third, ChromNet’s computationally efficient method allows joint network learning across across 115 cell types, which greatly increases the scope of possible interactions. Finally, the genomic context causing any network edge can be inferred to aid understanding.

Results: We applied ChromNet to all available ChIP-seq data from the ENCODE Project, consisting of 1,451 ChIP-seq datasets, which revealed previously known physical interactions better than alternative approaches.  ChromNet also identified previously unreported regulatory factor interactions.  We experimentally validated one of these interactions, between the MYC and HCFC1 transcription factors.

Discussion: ChromNet provides a useful tool for understanding the interactions among regulatory factors and identifying novel interactions.  We have provided an interactive web-based visualization of the full ENCODE chromatin network and the ability to incorporate custom datasets at http://chromnet.cs.washington.edu.

Bio:  Professor Su-In Lee is an Assistant Professor in the Departments of Computer Science & Engineering and Genome Sciences at the University of Washington. She received her Ph.D. degree in Electrical Engineering from Stanford University in 2009. Before joining the UW in 2010, she was a Visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University.

Her interest is in developing advanced machine learning (ML) algorithms to analyze high-throughput molecular data 1) to discover molecular mechanisms of disease initiation and progression, 2) to identify therapeutic targets, and 3) to develop personalized therapy based on individual patients’ molecular profiles. She has been named an American Cancer Society Research Scholar in 2015 and received the NSF CAREER award in 2016. Her lab is currently funded by the American Cancer Society, the National Institutes of Health, the National Science Foundation, the Institute of Translational Health Sciences and the Solid Tumor Translational Research.