Introduction

　　Yubo Zhang, Principle Investigator, Doctoral Advisor, the chief scientist of the Animal Functional Genomics Innovation Group. His main research fields are animal three-dimensional genomics, three-dimensional genomics technology development, construction of three-dimensional gene transcription regulatory network and research on the process of gene transcription regulation by non-coding DNA regulatory elements. His researches have been published on high profile journals including Nature and PNAS with more than 900 citations. In 2017, he was awardee of the national “The Thousand Talents Plan, Youth”. In 2015, he was awarded the "Young Talents" by Chinese Academy of Agricultural Sciences.

　　Research Interest

　　We focus our research on 3D genomics and precise animal breeding. The main research directions are as follows. Firstly, reliable and efficient 3D genomics technology is developed to investigate the biological effects of genes, regulatory elements and their long-distance interactions. Secondly, new algorithms are developed and applied for analyzing 3D genomics data. The goal is to optimize, standardize and program existing algorithms. Thirdly, 3D transcriptional regulation network is studied in early development of mice. By constructing a dynamic three-dimensional transcriptional regulatory network and integrating multiple sets of data, we hope to clarify the biological effects of key transcription factors or regulatory elements, thus revealing important scientific issues related to animal early development.

　　Major Achievements　　

　　1）Fast and efficient editing technology of enhancers provides a platform for the research of ESC enhancer functions.

　　Enhancer activity analysis of Sox2 gene: E1 and E3 showed obvious enhancer activity in mESCs cells, while the enhancer activity of E2 was weak . Three enhancer knockout cell lines were constructed using CRISPR/Cas9 system in mESCs cells. Compared with wild type, the expression level of Sox 2 gene in knockout cells decreased significantly .　　

　　2）eHi-C technologies have been improved and developed.

　　In view of the high false positive rate and high background noise of the original Hi-C technology, the team successfully introduced the "bar code" strategy in Hi-C technology, which significantly reduced the false positive rate in the original technology , providing technical support for the further development of three-dimensional/multi-dimensional genomics research.　　

　　3）Improvement and optimization of chromatin ring recognition technology

　　Based on the significant interaction region of statistical analysis, we introduced a local density clustering analysis method and developed the corresponding program. Compared with the existing methods, the new discriminating algorithm improves the computational efficiency and accurately identifies the chromatin loops based on Hi-C data which provides an efficient tool for 3D / multidimensional genomics information mining and transcriptional regulation studies in animals and pigs.

　　Selected Publication　　

　　1. Peng YL, Zhang YB. Enhancer and super-enhancer: Positive regulators in gene transcription. Animal Model Exp Med. 2018; https://doi.org/10.1002/ame2.12032 (通讯作者) 

　　2. Yubo Zhang. 三维基因组学与精准生物学[J]. 中国生物化学与分子生物学报, 2018, 34(4): 351-363. (通讯作者)

　　3. Kamada R, Yang W, Zhang Y, Patel MC, Yang Y, Ouda R, Dey A, Wakabayashi Y, Sakaguchi K, Fujita T, Tamura T, Zhu J, Ozato K., Interferon stimulation creates chromatin marks and establishes transcriptional memory. Proc Natl Acad Sci., 2018, doi: 10.1073/pnas.1720930115. (共同一作)

　　4. Yubo Zhang. 三维基因组学与精准生物学[J]. 中国生物化学与分子生物学报, 2018(4).

　　5. Kong S , Zhang Y . Deciphering Hi-C: from 3D genome to function[J]. Cell Biology and Toxicology, 2019, 35(1):15-32.

　　6. Yubo Zhang, Chee Hong Wong, Ramon Birnbaum, Guoliang Li, Rebecca Favaro, Chew Yee Nyan, Eunice Tai, Joanne Lim Hui Ping, Huay Mei Poh, Fabianus Hendriyan Mulawadi, Silvia Nicolis, Nadav Ahituv, Yijun Ruan and Chia-Lin Wei. Dynamic chromatin connectivity maps reveal lineage specific regulation. Nature. 2013, 504, 306-310.

　　7. Siyuan Kong, Li Li, Wenjuan Zhu, Leilei Xin, Jinxue Ruan, Yubo Zhang, Shulin Yang, and Kui Li. Genetic characteristics of polycistronic system-mediated randomly-inserted multi-transgenes in miniature pigs and mice:[J]. Molecular Medicine Reports, 2018, 17(1):37-50.

　　8. Kong, S., Ruan, J., Zhang, K., Hu, B., Cheng, Y., & Zhang, Y., et al. (2018). Kill two birds with one stone: making multi-transgenic pre-diabetes mouse models through insulin resistance and pancreatic apoptosis pathogenesis. Peerj, 6(5), e4542.

　　Patent

　　1.201610995880.X，一种高效的全基因组染色质构象技术eHi-C，Yubo Zhang，孔思远，黄其通，黄雷，王秋雁，白立景，张高林，钟学优；

　　2. 201710773996.3，一种基于少量细胞全基因组染色质高分辨率构象技术eHi-C 2.0，Yubo Zhang，孔思远，黄其通，李琳，黄雷，白立景，彭艳玲

　　3. 201810589919.7，一种消除三维基因组学技术噪音的方法及应用，Yubo Zhang，孔思远，张高林，范磊，黄其通，李清，黄雷，彭艳玲；.

　　4. 201810589904，利用外切酶组合消除三维基因组学技术噪音的方法及应用, Yubo Zhang，孔思远，张高林，黄其通，范磊，李清，黄雷，彭艳玲；

　　5. 201811546406.4，一种精准获得全基因组范围内结构变异方法，Yubo Zhang，付亚娟，徐波，王帅。