Chongzhi Zang Lab  

Research


News Releases


Research Description

The research in the lab focuses on two aspects: 1) developing quantitative models and computational methods for analyzing high-throughput data generated from emerging genomics technologies; and 2) using innovative computational and data science approaches to study epigenetics and transcriptional regulation of gene expression in mammalian cell systems and human diseases such as cancer.

How gene expression is regulated in chromatin is a fundamental question in molecular biology. The transcriptional program is a major determinant of cell identity; its dysregulation is involved in many diseases, including cancer. High-throughput genomics technologies enable us to obtain massive data measuring numerous factors and elements in the genome that affect chromatin states and gene regulation. We leverage big data and conduct computational research at the intersection of functional genomics, epigenetics, and cancer biology. Some research directions include:


1. Next-generation sequencing bioinformatics

We are interested in developing innovative statistical methods and new algorithms for analyzing massive data from next-generation sequencing (NGS) coupled with various assays for studying genomic chromatin profiles, such as ChIP-seq for transcription factor and histone modification profiling, ATAC-seq and DNase-seq for chromatin accessibility profiling, etc. As a pioneer in ChIP-seq bioinformatics, we developed SICER (Bioinformatics 2009), one of the most widely used methods for ChIP-seq data analysis with exceptional performance for board histone marks. We are developing novel quantitative models for analyzing DNase/ATAC-seq data and for studying chromatin dynamics at both population and single-cell levels.



2. Chromatin, epigenetics, and transcriptional regulation

Our ultimate goal is to understand the fundamental mechanisms in transcriptional regulation and the functions of chromatin. We characterized dozens of histone modifications and histone modifying enzymes at the genomic scale (Nat Genet 2008, Nat Genet 2009, Cell 2009, Cell Stem Cell 2009). Leveraging the large amount of publicly available ChIP-seq data, we developed several computational methods, including MARGE (Genome Res 2016) and BART (Bioinformatics 2018), for predicting cis-regulatory elements and transcriptional regulators from differentially expressed gene sets using integrative learning approaches. We are specifically interested in studying functional enhancer regulation of gene expression in the high-order dynamic structure of chromatin in cancer and immune systems.



3. Genomic data integration for global regulatory networks

High-dimensional genomic data analysis is challenging because of noises and biases in high-throughput experiments. We developed MANCIE (Nat Commun 2016), a method for bias correction and data integration of cross-platform genomic profiles on the same samples, using a Bayesian-supported principal component analysis (PCA)-based approach. We are interested in using statistical modeling and innovative data science approaches to integrate public multi-omics data for characterizing physical properties of mammalian chromatin structures and dynamic interactions between chromatin and DNA in human cell systems.





Collaborations

Computational biology is an interdisciplinary science. It cannot thrive without close collaborations between researchers with different backgrounds and expertise. We have established a collaborative research team and a cross-disciplinary environment in the lab, and we always commit to collaborations and team science on a variety of research projects with experimental biologists, clinicians, as well as statisticians, computer scientists, mathematicians, and physicists.

Our collaborators include:


Funding Support



Last modified: August 11, 2021