Chongzhi Zang, Ph.D.

Assistant Professor of Public Health Sciences
Assistant Professor of Biochemistry and Molecular Genetics

Center for Public Health Genomics
University of Virginia School of Medicine
P. O. Box 800717, Charlottesville, VA 22908

Office: West Complex (MSB) 6131C
Phone: 434-243-5397

Lab website:

Education and Training

B.S., Physics, Peking University, 2005

Ph.D., Physics, The George Washington University, 2010

Postdoc, Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard University, 2010–2016

Research Interests

Bioinformatics methodology development; Epigenetics and chromatin biology; Transcriptional regulation; Cancer genomics and epigenomics; Statistical methods for biomedical data integration; Theoretical and computational biophysics

Research Description

The research in my lab focuses on developing computational methodologies and integrative genomics approaches to study epigenetics and transcriptional regulation of gene expression in a variety of mammalian cell systems and human diseases such as cancer.

How gene expression is regulated in chromatin is a fundamental question in molecular biology. High-throughput technologies such as next-generation sequencing (NGS) have become powerful tools for studying gene regulation at the genomic scale. We conduct computational research that leverages these genomics technologies. Some research directions include:

1. Next-generation sequencing bioinformatics

We are interested in developing statistical methods and novel 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 mark 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 modification marks. We are developing novel statistical models and computational methods for analyzing DNase/ATAC-seq data and for studying chromatin dynamics.

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 MARGE (Genome Res 2016), a computational method to predict cis-regulatory profiles from differential expression gene sets using integrative learning approaches. We are specifically interested in studying functional enhancer regulation of gene expression in cancers.

3. Genomic data integration for chromatin dynamics and 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 machine-learning approaches to integrate public genomic data for characterizing physical properties of mammalian epigenomes and dynamic interactions between chromatin and DNA in human cell systems.

Selected Publications

Selected from 30+ journal articles. A complete publication list can be found at my Google Scholar profile.
* indicates authors with equal contributions.

  1. High-dimensional genomic data bias correction and data integration using MANCIE
    Chongzhi Zang*, Tao Wang*, Ke Deng, Bo Li, Sheng’en Hu, Qian Qin, Tengfei Xiao, Shihua Zhang, Clifford A. Meyer, Housheng Hansen He, Myles Brown, Jun S. Liu, Yang Xie, X. Shirley Liu
    Nature Communications 7, 11305 (2016)

  2. Modeling cis-regulation with a compendium of genome-wide histone H3K27ac profiles
    Su Wang*, Chongzhi Zang*, Tengfei Xiao, Jingyu Fan, Shenglin Mei, Qian Qin, Qiu Wu, Xujuan Li, Kexin Xu, Housheng Hansen He, Myles Brown, Clifford A. Meyer, X. Shirley Liu
    Genome Research 26, 1417–1429 (2016)

  3. NF-E2, FLI1 and RUNX1 collaborate at areas of dynamic chromatin to activate transcription in mature mouse megakaryocytes
    Chongzhi Zang*, Annouck Luyten*, Christina Chen, X. Shirley Liu, Ramesh A. Shivdasani
    Scientific Reports 6, 30255 (2016)

  4. NOTCH1-RBPJ complexes drive target gene expression through dynamic interactions with superenhancers
    Hongfang Wang*, Chongzhi Zang*, Len Taing, Kelly Arnett, Yinling Joey Wong, Warren S. Pear, Stephen C. Blacklow, X. Shirley Liu, Jon C. Aster
    Proceedings of the National Academy of Sciences USA 111, 715–710 (2014)

  5. Active enhancers are delineated de novo during hematopoiesis with limited lineage fidelity among specified primary blood cells
    Annouck Luyten*, Chongzhi Zang*, X. Shirley Liu, Ramesh A. Shivdasani
    Genes and Development 28, 1827–1839 (2014)

  6. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification
    Housheng Hansen He, Clifford A. Meyer, Sheng’en Shawn Hu, Mei-Wei Chen, Chongzhi Zang, Yin Liu, Prakash K. Rao, Teng Fei, Han Xu, Henry Long, X. Shirley Liu, Myles Brown
    Nature Methods 11, 73–78 (2014)

  7. Targets analysis by integration of transcripome and ChIP-seq data with BETA
    Su Wang, Hanfei Sun, Jian Ma, Chongzhi Zang, Chenfei Wang, Juan Wang, Qianzi Tang, Clifford A. Meyer, Yong Zhang, X. Shirley Liu
    Nature Protocols 8, 2502–2515 (2013)

  8. PTIP promotes chromatin changes critical for immunoglobulin switch recombination
    Jeremy A. Daniel, Margarida A. Santos*, Zhibin Wang*, Chongzhi Zang*, Mila Jankovic, Anna Gazumyan, Kristopher R. Schwab, Arito Yamane, Darius Filsuf, Young-Wook Cho, Kai Ge, Weiqun Peng, Michel C. Nussenzweig, Rafael Casellas, Gregory R. Dressler, Keji Zhao, André Nussenzweig
    Science 329, 917–923 (2010)

  9. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data
    Chongzhi Zang, Dustin E. Schones, Chen Zeng, Kairong Cui, Keji Zhao, Weiqun Peng
    Bioinformatics 25, 1952–1958 (2009)

  10. Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes
    Zhibin Wang*, Chongzhi Zang*, Kairong Cui*, Dustin E. Schones, Artem Barski, Weiqun Peng, Keji Zhao
    Cell 138, 1019–1031 (2009)

  11. H3.3/H2A.Z double variant-containing nucleosomes mark ‘nucleosome-free regions’ of active promoters and other regulatory regions
    Chunyuan Jin*, Chongzhi Zang*, Gang Wei, Kairong Cui, Weiqun Peng, Keji Zhao, Gary Felsenfeld
    Nature Genetics 41, 941–945 (2009)

  12. Chromatin signatures in multipotent hematopoietic stem cells indicate the fate of bivalent genes during differentiation
    Kairong Cui*, Chongzhi Zang*, Tae-Young Roh, Dustin E. Schones, Richard W. Childs, Weiqun Peng, Keji Zhao
    Cell Stem Cell 4, 80–93 (2009)

  13. Combinatorial patterns of histone acetylations and methylations in the human genome
    Zhibin Wang*, Chongzhi Zang*, Jeffrey A. Rosenfeld*, Dustin E. Schones, Artem Barski, Suresh Cuddapah, Kairong Cui, Tae-Young Roh, Weiqun Peng, Michael Q. Zhang, Keji Zhao
    Nature Genetics 40, 897–903 (2008)


Honors and Awards


My lab is recruiting motivated young students and scholars to work on a variety of topics in computational biology in a collaborative research team. Postdocs, graduate students, and undergraduate students are all welcome. Please contact me for any questions.

Prospective postdocs can find the job details and submit applications here.

"While the art of printing is left to us science can never be retrograde; what is once acquired of real knowledge can never be lost."

—Thomas Jefferson, 1799

Last modified: March 12, 2017