2021-至今 北京生命科學(xué)研究所 生物信息學(xué)中心主任
Director of Bioinformatics Center, National Institute of Biological Science, Beijing
2016～2021 中國科學(xué)技術(shù)大學(xué)，生命科學(xué)與醫(yī)學(xué)部 副研究員
Associate researcher, Department of life sciences and medicine, University of science and technology of China
2014～2016 美國哥倫比亞大學(xué) 博士后研究員
Postdoctoral researcher, Department of chemistry, Columbia University
2010～2014 美國加州大學(xué)伯克利分校 博士后
Post-doctoral, Department of chemistry, University of California, Berkeley
近年來多種測序技術(shù)被廣泛應(yīng)用于基礎(chǔ)生物學(xué)和臨床醫(yī)學(xué)研究，使我們可以從基因組層面理解細(xì)胞發(fā)育、分化、轉(zhuǎn)化、病變的內(nèi)在機(jī)制。然而，深入分析海量測序數(shù)據(jù)仍是難題，尤其是單細(xì)胞測序數(shù)據(jù)規(guī)模的指數(shù)級(jí)提高，亟需高效準(zhǔn)確的生物信息學(xué)算法和工具。NIBS生物信息學(xué)中心為各個(gè)實(shí)驗(yàn)室提供生物信息學(xué)分析服務(wù)，包括對(duì)單細(xì)胞基因組、空間轉(zhuǎn)錄組、表觀遺傳組、蛋白質(zhì)組等多種測序數(shù)據(jù)的深入解析和挖掘。同時(shí)，我們關(guān)注于開發(fā)新型機(jī)器學(xué)習(xí)和深度學(xué)習(xí)算法，結(jié)合大語言模型分析多種類型的大規(guī)模測序數(shù)據(jù)，探索各種重大疾病的發(fā)病機(jī)制，挖掘潛在的藥物靶點(diǎn)，預(yù)測用藥后的基因組變化，為患者的個(gè)性化精準(zhǔn)診療提供支持。
In recent years, various sequencing technologies have been widely applied in basic biology and clinical medical research, allowing us to understand the intrinsic mechanisms of cell development, differentiation, transformation, and pathological changes at the genomic level. However, in-depth analysis of vast sequencing data remains a challenge, especially with the exponential increase in the scale of single-cell sequencing data, which urgently requires efficient and accurate bioinformatics algorithms and tools. The NIBS Bioinformatics Center provides bioinformatics analysis services for various laboratories, including in-depth interpretation and exploration of sequencing data from single-cell genomics, spatial transcriptomics, epigenomics, proteomics, and more. We also focus on developing novel machine learning and deep learning algorithms, integrating large language models to analyze various types of large-scale sequencing data, exploring the pathogenesis of diseases, identifying potential drug targets, predicting genomic changes after drug administration, and supporting personalized precision diagnosis and treatment for patients.
發(fā)表文章
1. Yinlei Hu#, Siyuan Wan#, Yuanhanyu Luo#, Yuanzhe Li, Tong Wu, Wentao Deng, Chen Jiang, Shan Jiang, Yueping Zhang, Nianping Liu, Zongcheng Yang, Falai Chen*, Bin Li* & Kun Qu*, “Benchmarking algorithms for single-cell multi-omics prediction and integration”, Nature Methods, 21, 2181-2194 (2024).
2. Hao Xu, Shuyan Wang, Minghao Fang, Songwen Luo, Chunpeng Chen, Siyuan Wan, Rirui Wang, Meifang Tang, Tian Xue, Bin Li*, Jun Lin* & Kun Qu*, “SPACEL: deep learning-based characterization of spatial transcriptome architectures”, Nature Communications, 14, 7603 (2023).
3. Bin Li#, Wen Zhang#, Chuang Guo#, Hao Xu, Longfei Li, Minghao Fang, Yinlei Hu, Xinye Zhang, Xinfeng Yao, Meifang Tang, Ke Liu, Xuetong Zhao, Jun Lin, Linzhao Cheng, Falai Chen, Tian Xue & Kun Qu, “Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution”, Nature Methods, 19, 662–670 (2022).
4. Bin Li#, Young Li#, Kun Li, Lianbang Zhu, Qiaoni Yu, Jingwen Fang, Pengfei Cai, Chen Jiang, Kun Qu, “APEC: An accesson-based method for single-cell chromatin accessibility analysis”, Genome Biology, 21, 116 (2020).
5. Chuang Guo#, Bin Li#, Huan Ma, Xiaofang Wang, Lianxin Liu, Xiaoling Ma, Jianping Weng, Haiming Wei, Tengchuan Jin*, Jun Lin*, Kun Qu*, “Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm”, Nature Communications, 11, 3924 (2020).
地址：北京市中關(guān)村生命科學(xué)園科學(xué)園路七號(hào) 