2005年，福州大学生物工程专业，学士学位；
2008年，福州大学计算机软件与理论专业，硕士学位；
2012年，中国科学院遗传与发育生物学研究所生物信息学专业，博士学位；
2011-2013年，葛兰素史克上海医药研发有限公司，科学家；
2013-2018年，哈佛大学Dana-Farber癌症研究所/波士顿儿童医院，博士后；
2018年至今，伟德BETVlCTOR1946，闽江学者特聘教授。
B.S. 2005, Fuzhou University, Bioengineering;
M.S. 2008, Fuzhou University, Computer Software and Theory;
Ph.D., 2012, Chinese Academy of Science, Bioinformatics;
Scientist, GlaxoSmithKline (China) R&D Co., Ltd., Shanghai, 2011-2013;
Postdoc, Dana-Farber Cancer Institute/Boston Children's Hospital, Harvard Medical School, 2013-2018;
Principal Investigator, School of Life Sciences, Xiamen University, 2018 to Present.

哺乳动物发育和癌症的表观遗传调控机制
本课题组利用生物信息学（干）和分子生物学（湿）等方法，研究哺乳动物发育和癌症的表观遗传调控机制。围绕某一特定生物学问题，基于现有/产生表观基因组学等图谱，利用生物信息学方法在系统层面提出具体的生物学假设，并采用CRISPR/cas9基因编辑等手段开展实验验证。具体研究方向包括（1）设计生物信息学算法，解决生物组学数据分析的定量问题；（2）整合已有表观基因组学数据，构建发育过程中的增强子动态调控网络，寻找关键调控因子；（3）与临床医生/生物学家合作，利用单细胞组学等技术，探索癌症发生和治疗的细胞异质性，寻找潜在药物靶标。
Our lab is interest in understanding the epigenetic regulation mechanisms of mammalian development and cancer using computational (dry) and experimental (wet) methods. Starting from a specific biological question, we generate or integrate publicly available genomic and epigenomic data to make a specific testable hypothesis at system level, following by the experimental validation using CRISPR/cas9 genome-editing assays. Our current research includes: (1) developing computational methods for quantification problems for various omics data; (2) elucidate the gene regulatory networks during development through integrating genomic, transcriptomic, and epigenomic data; (3) investigate the cellular heterogeneity during cancer progression and treatment response using single-cell analysis, which helps to identify potential drug targets..

代表性论文(^# co-first author, ^* Corresponding author):

1. M. Shu#, D. Hong#, H. Lin, J. Zhang, Z. Luo, Y. Du, Z. Sun, M. Yin, Y. Yin, L. Liu, S. Bao, Z. Liu, F. Lu^*, J. Huang^*, J. Dai^*. Single-cell chromatin accessibility reveals enhancer networks driving gene expression during spinal cord development. Developmental Cell, 2022.
2. D. Hong#, H. Lin#, L. Liu, M. Shu, J. Dai, F. Lu, M. Tong, J. Huang^*. Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. Briefings in Bioinformatics, 2022.
3. Z. Wang#, T. Wang#, D. Hong#, B. Dong#, Y. Wang, H. Huang, W. Zhang, B. Lian, B. Ji, H. Shi, M. Qu, X. Gao, D. Li, C. Collins, G. Wei, C. Xu^*, H. J. Lee^*, J. Huang^*, J. Li^*. Single-cell transcriptional regulation and genetic evolution of neuroendocrine prostate cancer. iScience, 2022.
4. Y. Kai#, B. E. Li#, M. Zhu#, G. Y. Li, F. Chen, Y. Han, H. J. Cha, S. H. Orkin, W. Cai^*, J. Huang^*, G.-C. Yuan^*. Mapping the evolving landscape of super-enhancers during cell differentiation. Genome Biology, 2021.
5. W. Cai#, J. Huang#, Q. Zhu, B. Li, D. Seruggia, P. Zhou, M. Nguyen, Y. Fujiwara, H. Xie, Z. Yang, D. Hong, P. Ren, J. Xu, W. Pu, G.-C. Yuan^* and S. H. Orkin^*. Enhancer-dependence of cell-type-specific gene expression increases with developmental age. PNAS, 2020.
6. J. Huang#, K. Li#, W. Cai, X. Liu, Y. Zhang, S. H. Orkin, J. Xu^*, G.-C. Yuan^*. Dissecting super-enhancer hierarchy based on chromatin interactions. Nature Communications, 2018.
7. E. Marco#, W. Meuleman#, J. Huang#, K. Glass, L. Pinello, J. Wang, M. Kellis^*, G.-C. Yuan^*. Multi-scale chromatin state annotation using a hierarchical hidden Markov model. Nature Communications, 2017.
8. J. Huang#, X. Liu#, D. Li#, Z. Shao#, H. Cao, Y. Zhang, E. Trompouki, T. V. Bowman, L. I. Zon, G.-C. Yuan, S. H. Orkin^*, J. Xu^*. Dynamic control of enhancer repertoires drives lineage and stage-specific transcription during hematopoiesis. Developmental Cell, 2016.
9. J. Huang, E. Marco, L. Pinello, G.-C. Yuan^*. Predicting chromatin organization using histone marks. Genome Biology, 2015.
10. J. Huang, C. Niu, C. D.Green, L. Yang, H. Mei^*, J.-D. J. Han^*. Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Computational Biology, 2013.
11. J. Huang, Y. Liu, W. Zhang, H. Yu and J.-D. J. Han^*. eResponseNet: a package prioritizing candidate disease genes through cellular pathways. Bioinformatics, 2011.

荣誉、奖励及参加学术团体的情况: