Assistant Professor, Department of Genetics and BiochemsitryVisit the Duren Lab
- 2008-2012: earned BS in Mathematics and Applied Mathematics from Beihang University (China).
- 2012-2017: earned Ph.D in Operational Research and Cybernetics from Academy of mathematics and systems science, Chinese Academy of Sciences.
- 2015-2020: works in Professor Wing Hung Wong’s lab at Stanford University as a visiting Ph.D student (2015-2017) and postdoc research fellow (2017-2020).
A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. It is challenging to understand the mechanism of how these genetic variants contribute to disease because over 90% of trait-associated genetic variants are located in non-coding regions which don’t encode any protein-coding genes but may have regulatory functions. My research goal is to answer the key scientific question” How non-coding genetic variant act through cellular context specific gene regulatory network to influence phenotype”. To do this, we develop novel statistical machine learning methods and bioinformatics tools. Specific directions include 1) inference method of gene regulatory network by integrating different types of genomics data, 2) single-cell genomics data analysis, and 3) identification of causal variants for complex diseases by using cellular context specific gene regulatory networks.
Duren Z, Chen X, Xin J, Wang Y and Wong WH. 2020. Time course regulatory analysis based on paired expression and chromatin accessibility data. Genome Res 30: 622-634. doi: 10.1101/gr.257063.119.
Li W, Duren Z, Jiang R, Wong WH. 2020. A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proc Natl Acad Sci. 35: 21364-72. doi: 10.1073/pnas.1922703117.
Ren L, Gao C, Duren Z, and Wang Y. 2020. GuidingNet: revealing transcriptional cofactor and predicting binding for DNA methyltransferase by network regularization. Brief Bioinform. doi: 10.1093/bib/bbaa245.
Xin J, Zhang H, He Y, Duren Z, Bai C, Chen L, Luo X, Yan DS, Zhang C, Zhu X, Yuan Q, Feng Z, Cui C, Qi X, Ouzhuluobu, Wong WH, Wang Y and Su B. 2020. Chromatin accessibility landscape and regulatory network of high-altitude hypoxia adaptation. Nat. Commun. 11: 4928. doi: 10.1038/s41467-020-18638-8.
Zhang X, Hong D, Ma S, Ward T, Ho M, Pattni R, Duren Z, Stankov A, Bade Shrestha S, Hallmayer J, Wong WH, Reiss AL and Urban AE. 2020. Integrated functional genomic analyses of Klinefelter and Turner syndromes reveal global network effects of altered X chromosome dosage. Proc Natl Acad Sci USA 117: 4864-4873. doi: 10.1073/pnas.1910003117.
Zhu X, Duren Z and Wong WH. Modeling regulatory network topology improves genome-wide analyses of complex human traits. 2020. BioRxiv doi: 10.1101/2020.03.13.990010.
Lingjie L, Wang Y, Torkelson JL, Shankar G, Pattison JM, Zhen HH, Fang F, Duren Z, Xin J, Gaddam S, Melo SP, Piekos SN, Li J, Liaw EJ, Chen L, Li R, Wernig M, Wong WH, Chang HY and Oro AE. 2019. TFAP2C-and p63-dependent networks sequentially rearrange chromatin landscapes to drive human epidermal lineage commitment. Cell Stem Cell 24: 271-284. doi: 10.1016/j.stem.2018.12.012.
Zeng W, Chen X, Duren Z, Wang Y, Jiang R and Wong WH. 2019. DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nat Commun 10: 4613. doi: 10.1038/s41467-019-12547-1.
Zhana Duren, Xi Chen, Mahdi Zamanighomi, Wanwen Zeng, Ansuman T. Satpathy, Howard Y. Chang, Yong Wang, and Wing Hung Wong. Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proceedings of the National Academy of Sciences 115.30 (2018): 7723-7728.
Mahdi Zamanighomi, Zhixiang Lin, Timothy Daley, Xi Chen, Zhana Duren, Alicia Schep, William J. Greenleaf, Wing Hung Wong. Unsupervised clustering and epigenetic classification of single cells. Nature Communications, 9.1 (2018): 2410.
Zhana Duren, Xi Chen, Rui Jiang, Yong Wang, and Wing Hung Wong. Modeling gene regulation from paired expression and chromatin accessibility data. Proceedings of the National Academy of Sciences (2017): 201704553.