Bin Li, Ph.D.

Director of Bioinformatics Center, NIBS, Beijing, China

Email: libin@nibs.ac.cn

2003～2010

Ph.D., Dalian Institute of Chemical Physics, Chinese Academy of Sciences

1999～2003

Bachelor of science, University of science and technology of China

2021-

Director of Bioinformatics Center, National Institute of Biological Science, Beijing

2016～2021

Associate researcher, Department of life sciences and medicine, University of science and technology of China

2014～2016

Postdoctoral researcher, Department of chemistry, Columbia University

2010～2014

Post-doctoral, Department of chemistry, University of California, Berkeley

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.

Publications

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).