Abstract：

Epigenetic regulation of gene expression plays a critical role in many biological processes including cancer formation and progression. Prediction of enhancers and transcription factors regulating genes with differential expression is an essential problem in functional genomics research. In this talk I will present a series of computational methods for modeling gene regulation using massive publicly-available data from human and mouse. We develop MARGE, a logistic regression and semi-supervised learning-based approach for predicting genomic cis-regulatory profiles that regulate a given gene set by leveraging a compendium of public H3K27ac ChIP-seq datasets. We develop BART to predict TFs associated with MARGE-predicted cis-regulatory profiles using thousands of TF ChIP-seq datasets. Integrating these approaches on The Cancer Genome Atlas (TCGA) molecular profiling data, we reconstruct the functional enhancer profiles and predict active transcription factor targets for each TCGA cancer type. Our work demonstrates the power of utilizing public data for computational studies of epigenomics.
 
Brief Biograph：

Chongzhi Zang completed his undergraduate studies in Physics from Peking University in 2005 and got his PhD in Physics from the George Washington University in 2010. He has focused his research on computational biology and epigenomics since his PhD work on ChIP-seq data analysis. He did his postdoctoral training at Harvard University’s Dana-Farber Cancer Institute from 2010 to 2016. Since Fall 2016, he has been Assistant Professor at the University of Virginia (UVA) School of Medicine. Primarily affiliated with Center for Public Health Genomics, he holds faculty appointments at Departments of Public Health Sciences, Biomedical Engineering, Biochemistry and Molecular Genetics, as well as the Cancer Center and the Data Science Institute at UVA.

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grsgxf@pku.edu.cn