Integrative Data Science Approaches for Studying Transcriptional Regulation in the Genome

Transcriptional regulation plays a critical role in many biological processes including cell development and cancer formation. Identification of active enhancers and transcription factors responsible for regulating gene expression is an essential problem in functional genomics. With novel integrative modeling approaches, massive publicly available multi-omics data can be a useful resource for gene regulation studies. In this talk I will present a few computational methods we developed for modeling gene regulation using public omics data and our recent work in studying cancer transcriptional regulation in the 3-dimensional genome using public data integration approaches. This work will demonstrate the power of data integration in both developing new methods and finding new biology.

Speaker Bio: Dr. Chongzhi Zang is an Assistant Professor at the Center for Public Health Genomics, University of Virginia School of Medicine. His research focuses on developing computational and statistical methods for analyzing high-throughput data from innovative omics technologies and using integrative data science approaches to study gene regulation in mammalian cell systems. He develops several popular computational tools for ChIP-seq data analysis, transcriptional regulator inference and multi-omics data integration.