• Postdoctoral Fellow, Department of Biostatistics and Computational Biology (now Department of Data Science), Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, 2012–2018
• PhD, Computer Science and Engineering, University of California, Riverside, 2008–2012
• MS, Computer Science, Tsinghua University, Beijing, China, 2006–2008
• BS, Computer Science, Tsinghua University, Beijing, China, 2002–2006

bioinformatics, computational biology, functional genomics, genome engineering, gene editing, CRISPR, CRISPR screens, single-cell omics, Perturb-seq, high-throughput sequencing, machine learning, artificial intelligence, deep learning, precision medicine

• Yuqing Zhu, Qingying Wang, Yanzi Cao, Linxiao Hou, Lin Yang, Chen Cheng, Shoudong Ye, Wei Li#, and Jin Zhang#. Single cell CRISPR screen identifies antagonism between Nsd1-H3K36me2 and Ezh2-H3K27me3 orchestrates pluripotency transition. Stem Cell Reports. 2026;21(8):103016.

• Qing Chen, Tianchang Yang, Yingping Hou, Kai Wang, Tingting Li, Longteng Wang, Cuiyun Dou, Panpan Cheng, Minglei Shi#, and Wei Li#. An integrated multi-omics and network analysis of neutrophil differentiation from initial- to late-stage. Genome Biology. 2026;27:63. doi:10.1186/s13059-025-03902-1.

• Bicna Song, Dingyu Liu, Weiwei Dai, Natalie McMyn, Qingyang Wang, et al., and Wei Li. Decoding Heterogeneous Single-cell Perturbation Responses. Nature Cell Biology. 2025;27:493–504. https://www.nature.com/articles/s41556-025-01626-9

• Zexu Li*, Zihan Li*, Xiaolong Cheng*, et al., Wei Li#, and Teng Fei#. Intrinsic targeting of host RNA by Cas13 constrains its utility. Nature Biomedical Engineering. 2023;8:177–192. https://doi.org/10.1038/s41551-023-01109-y

• Wei Li*, Han Xu*, Tengfei Xiao, Le Cong, Feng Zhang, Jun S. Liu, Myles Brown, and X. Shirley Liu. MAGeCK enables robust identification of essential genes from genome-scale CRISPR-Cas9 knockout screens. Genome Biology. 2014;15:554. https://mageck.sourceforge.net

• Bicna Song and Wei Li. Factoring Single Cell Perturbations. Nature Methods. 2023. https://doi.org/10.1038/s41592-023-02002-x
• Xiaolong Cheng, Zexu Li, Ruocheng Shan, et al., Teng Fei, and Wei Li. Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches. Nature Communications. 2023;14:752. http://deepcas13.weililab.org
• Weiwei Dai, Fengting Wu, Victoria E. Walker-Sperling, et al., Wei Li#, and Robert F. Siliciano#. Genome-wide CRISPR screens identify combinations of candidate latency reversing agents for targeting the latent HIV-1 reservoir. Science Translational Medicine. 2022;14(667):eabh3351.
• Christoph Bock, Paul Datlinger, Florence Chardon, et al., including Wei Li. High-content CRISPR screening. Nature Reviews Methods Primers. 2022;2(1):1–23.
• Zexu Li, Yingjia Yao, Xiaolong Cheng, et al., Wei Li#, and Teng Fei#. A Computational Framework of Host-Based Drug Repositioning for Broad-Spectrum Antivirals against RNA Viruses. iScience. 2021;24(3):102148.
• Yingbo Cui, Xiaolong Cheng, Qing Chen, et al., and Wei Li. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Research. 2021;49(D1):D848–D854. 
• Yinghua Li*, Bo Li*, Wei Li*, et al. Murine models of IDH-wild-type glioblastoma exhibit spatial segregation of tumor initiation and manifestation during evolution. Nature Communications. 2020;11:3669.
• Lin Yang, Yuqing Zhu, Hua Yu, et al., Jin Zhang#, and Wei Li#. scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens. Genome Biology. 2020;21:1–14. https://bitbucket.org/weililab/scmageck
• Binbin Wang, Mei Wang, Wubing Zhang, et al., Wei Li#, and X. Shirley Liu#. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nature Protocols. 2019;14:756–780.
• Teng Fei*, Wei Li*, Jingyu Amy Peng*, et al. Deciphering essential cistromes using genome-wide CRISPR screens. Proceedings of the National Academy of Sciences. 2019;116(50):25186–25195.
• Tengfei Xiao*, Wei Li*, Xiaoqing Wang, et al. Estrogen-regulated Feedback Loop Limits the Efficacy of Estrogen Receptor-targeted Breast Cancer Therapy. Proceedings of the National Academy of Sciences. 2018;115(31):7869–7878.
• Chen-Hao Chen, Tengfei Xiao, Han Xu, Peng Jiang, Cliff Meyer, Wei Li#, Myles Brown#, and X. Shirley Liu#. Improved design and analysis of CRISPR Knockout Screens. Bioinformatics. 2018;34(23):4095–4101.
• Qingyi Cao, Jian Ma, Chen-Hao Chen, Han Xu, Zhi Chen, Wei Li#, and X. Shirley Liu#. CRISPR-FOCUS: a web server for designing focused CRISPR screening experiments. PLoS ONE. 2017;12(9):e0184281.
• Shiyou Zhu*, Wei Li*, Jingze Liu, et al. CRISPR/Cas9-mediated genomic deletion screening for long non-coding RNAs using paired-gRNAs. Nature Biotechnology. 2016;34:1279–1286.
• Wei Li*, Johannes Köster*, Tengfei Xiao, et al. Quality control, modeling and visualization of genome-wide CRISPR screens using MAGeCK-VISPR. Genome Biology. 2015;16:281.
• Masruba Tasnim, Shining Ma, Ei-Wen Yang, Tao Jiang#, and Wei Li#. Accurate Inference of Isoforms from Multiple Sample RNA-Seq Data. BMC Genomics. 2015;16(S2):S15. APBC 2015 Best Paper Award.
• Wei Li and Tao Jiang. Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads. Bioinformatics. 2012;28(22):2914–2921.
• Wei Li, Jianxing Feng, and Tao Jiang. IsoLasso: A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly. Journal of Computational Biology. 2011;18(11):1693–1707.

The Li laboratory develops computational and experimental approaches for understanding how genomic variation and gene regulation shape cellular phenotypes in health and disease. The group integrates functional genomics, genome engineering, single-cell technologies, and artificial intelligence to connect perturbations with molecular and cellular outcomes.

A central focus is the development and analysis of CRISPR-based experiments, including Cas9 and Cas13 systems, base editing, pooled genetic screens, single-cell CRISPR screens, and Perturb-seq. The laboratory creates algorithms for experimental design, quality control, statistical modeling, visualization, and prediction. Its widely used MAGeCK and MAGeCK-VISPR frameworks helped establish robust analysis of genome-scale CRISPR knockout screens, while subsequent work has addressed multi-phenotype single-cell screens and heterogeneous perturbation responses.

The group also develops machine-learning and deep-learning models to predict on-target and off-target editing effects, interpret large-scale perturbation data, and model context-dependent cellular responses. These approaches support AI-guided target discovery and the systematic study of coding and noncoding functional elements.

Working with basic, translational, and clinical collaborators, the laboratory applies these technologies to cancer and cancer immunology, infectious diseases such as HIV and respiratory viral infection, and genetic disorders. The long-term goal is to turn information-rich genomic and perturbation datasets into mechanistic insight and actionable targets for precision medicine.

Awards and distinctions

• Mrs. Mary Elizabeth McGehee Joyce Chair in Genetic Research, Children’s National Hospital, 2024–2025
• Research Starter Award, Pharmaceutical Research and Manufacturers of America Foundation, 2019

Current support

• NIH/NHLBI R01 HL168174 — Integrative genomic and functional genomic studies to connect variants to function for CAD GWAS loci
• Gilbert Family Foundation Award — Strategic platform to interrogate multi-omics data from NF1-associated tumors
• UMB Research Infrastructure Investment Grant 
  
  

Past support

• NIH/NHGRI R01 HG010753 — Modeling Functional Elements Using CRISPR Screening
• District of Columbia Intellectual and Developmental Disabilities Research Center Pilot Award 
• District of Columbia Center for AIDS Research Transitional Investigator Award
• Virginia Tech–Children’s National Hospital AI Research Collaboration Pilot Award
• COVID-19 High Performance Computing Consortium Awards r
• PhRMA Research Starter Grant in Informatics 
  
  

• Founding Director, Gene Editing Core, Children’s National Hospital, 2023–2025
• Academic Editor and Editorial Board Member, PLOS Computational Biology, 2023–present
• Editorial Board Member, Computational and Structural Biotechnology Journal, 2023–present
• Editorial Board Member, Journal of Translational Genetics and Genomics, 2023–present
• Associate Editor, Frontiers in Oncology — Cancer Genetics; Genome Editing in Cancer and Immunology, 2022–present
  
  

• CRISPR-Cas9 and CRISPR-Cas13 genome engineering
• Base editing and gene knock-in/knockout
• Genome-scale pooled CRISPR screening
• Single-cell CRISPR screening and Perturb-seq
• Single-cell RNA sequencing and multi-omics
• High-throughput sequencing and functional genomics
• Computational biology, bioinformatics, and network analysis
• Machine learning, deep learning, and artificial intelligence for perturbation modeling
• Experimental design, quality control, statistical analysis, and visualization of genetic screens

• Wei Li lab website: https://weililab.org 
• Google Scholar