We use novel computational and genome engineering approaches to understand the function of human genome, especially in the context of human physiology and disease.

Join Us Research @davidliwei

The lab uses the latest gene editing technology (including CRISPR/Cas9 and CRISPR/Cas9 screening) and new computational algorithms to better understand how coding and non-coding elements function especially in human cancer, and to further identify novel molecular targets to inform precision medicine.

We are particularly interested in applying new computer science, artificial intelligence and machine learning methods to address challenges in biomedical and biological big data problems.

We are part of the Center for Genetic Medicine Research at Children’s National Hospital. We are also affiliated with Department of Genomics & Precision Medicine and Department of Pediatrics at The George Washington School of Medicine and Health Sciences.

To Infinity … and Beyond!

— Buzz Lightyear

Buzz Lightyear of Star Command: The Adventure Begins.



We are interested in developing computational technologies to understand the functions of coding and non-coding elements, especially in the context of human physiology and disease. We are focusing on the following areas:

Algorithm development for functional screening (esp. CRISPR/Cas9 knockout screening)

We developed a comprehensive computational solution for functional screens using CRISPR/Cas9, including guide-RNA design algorithms:

Algorithms for the modeling and processing of CRISPR screens:

Algorithms for modeling single-cell CRISPR screens:

Databases for large-scale genetic screens spanning multiple phenotypes:

And so on. These algorithms became popular in the field: the MAGeCK suite reach over 20k paper visits and over 90,000 software downloads. These softwares enabled researchers to identify interesting hits from screens, and to perform joint analysis from multiple screening experiments:


Figure 1: Analyzing gene functions using MAGeCK-VISPR in a single experiment (left) and two experiments (right)

Functional analysis of coding and non-coding elements from screening and genomics data

Using the computational frameworks we developed, we collaborated with experimental and clinical scientists around to world to study DNA functions and their associations with human diseases.

Example 1: understanding gene perturbations at a single cell level

CRISPR/Cas9 based functional screening coupled with single-cell RNA-seq (“single-cell CRISPR screening”) is an exciting new technology that combines genome engineering with single cell sequencing. It’s particularly helpful to understand gene regulatory networks and enhancer-gene regulations in a large scale. We propose scMAGeCK, a computational framework to systematically identify genes and non-coding elements associated with multiple expression-based phenotypes in single-cell CRISPR screening. Furthermore, we collaborated with various labs to answer key biological questions including embryonic stem cell differentiation. scMAGeCK is a novel and effective computational tool to study genotype-phenotype relationships at a single-cell level. scMAGeCK was published at Genome Biology.


Figure 2. Overview of the scMAGeCK algorithm to analyze single-cell CRISPR screens

Example 2: targeting endocrine resistant breast cancer

Over 70% of breast cancer patients are ER positive, and endocrine therapy has been a standard treatment for these patients for decades. However, most patients with advanced stage will eventually develop resistance to ER inhibition therapies with unknown mechanisms. We collaborated with Myles Brown lab (at Dana-Farber Cancer Institute/Harvard Medical School) to study the mechanism and potential treatment solutions of breast cancer endocrine resistance. By analyzing genome-wide CRISPR knockout screening data, we found an unusual tumor suppressor, c-src tyrosine kinase (CSK), whose loss accelerated cell growth without hormone, and is associated with high-grade tumors and worse survival rates in patients.

We also identified genes that are synthetic lethal in CSK loss from screens that can serve as drug targets. The top hit (PAK family kinase) is confirmed as a vulnerable target for endocrine resistant patients, and the small molecule PAK inhibitor suppresses tumor growth in various confirmation experiments. In other words, we not only found a biomarker that are responsible for breast cancer drug resistance, but also found a potential drug that can be repurposed to treat these patients.

The paper was published in PNAS 2018 and a corresponding patent application is submitted.


Figure 3. Analyzing critical genes in breast cancer using MAGeCK and MAGeCK-VISPR.

Example 3: Studying functional long non-coding RNAs in cancer

Long non-coding RNAs (lncRNAs) do not translate into protein but they are important in many aspects (including cancer). In collaboration with Wensheng Wei laboratory (Peking University), we developed a novel computational and experimental protocol to screen for lncRNAs using paired gRNAs (pgRNAs). This technology introduces pgRNAs simultaneously into one cell, and is able to efficiently knockout non-coding elements by introducing large genomic deletions. We demonstrated its ability to knockout lncRNAs in a fast and efficient manner.

The paper was published in Nature Biotechnology.


Figure 4. lncRNA screening: designing algorithm (left) and identifying top hits (right) using CRISPR screening.

Transcriptome dynamics from RNA-seq and scRNA-seq

RNA-Seq is an exciting technology to study transcriptome via the second generation sequencing. We studied the problem of de novo transcriptome assembly from RNA-Seq reads — reconstructing all possible message RNA compositions simultaneously, without using any information from current gene annotations. We developed a series of influential algorithms for RNA-seq transcriptome assembly and expression analysis: IsoInfer, IsoLasso, CEM and ISP. IsoInfer and IsoLasso were the first algorithms to use combinatorial methods and regularized least squares methods to study assembly problem in RNA-seq.

We are now working on single-cell RNA-seq (scRNA-seq), an exciting new technology to study transcriptome dynamics at the single-cell level.


Figure 5. The IsoLasso splicing model


Algorithms for the modeling and processing of CRISPR screens:

  • MAGeCK*: Model-based analysis of CRISPR/Cas9 knockout screens. [Genome Bio. 2014][code]
  • MAGeCK-VISPR*: Quality control, modeling and visualization of CRISPR screens. [Genome Bio. 2015][code]
  • MAGeCKFlute*: Integrative analysis of pooled CRISPR genetic screens. [Nature Protocols 2019][code]
  • CRISP-View*: A Comprehensive Database of CRISPR Screen Data. [website]

Guide-RNA design algorithms:

Algorithms for modeling single-cell CRISPR screens:

  • scMAGeCK*: Linking genotypes with multiple phenotypes in single-cell RNA-seq based CRISPR screens. [Genome Biology 2020][code]

Algorithms for RNA-seq:

  • IsoLasso*: A lasso regression approach to RNA-seq based transcriptome assembly. [RECOMB & JCB 2011][code]
  • CEM*: Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads. [Bioinformatics 2011][code]
  • ISP*: Accurate inference of isoforms from multiple sample RNA-Seq data. [APBC & BMC genomics 2015][code]
  • IsoInfer: Inference of isoforms from short sequence reads using combinatorial optimization. [RECOMB & JCB 2010]
  • RNAseqMut: A light-weight, fast mutation calling tool from RNA-seq (and other types of sequencing) data. [code]

*  Softwares where we are the major contributors.


For a full list of published papers, see the google scholar page.

Selected publications

*first/co-first author; #corresponding/co-corresponding author. Li lab members.

  • Zexu Li*, Zihan Li*, Xiaolong Cheng*, Shengnan Wang, Xiaofeng Wang, Shixin Ma, Zhiyan Lu, Han Zhang, Wenchang Zhao, Zhisong Chen, Yingjia Yao, Cheng Zhang, Lumen ChaoWei Li#, Teng Fei#. Intrinsic RNA targeting constrains the utility of CRISPR-Cas13 systems. In press, Nature Biomedical Engineering. Also in bioRxiv 2022. [Link]
  • Xiaolong Cheng*, Zexu Li*, Ruocheng Shan, Zihan Li, Shengnan Wang, Wenchang Zhou, Han Zhang, Lumen Chao, Jian Peng, Teng Fei#, Wei Li#. Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches. Nature Communications 2023, 14 (752). Also on bioRxiv 2021. [Link][website]
  • Weiwei Dai, Fengting Wu, Natalie McMyn, Bicna Song, Victoria E. Walker-Sperling, Joseph Varriale, Hao Zhang, Dan H. Barouch, Janet D. Siliciano, Wei Li#, Robert F. Siliciano# . Genome-wide CRISPR screens identify combinations of candidate latency reversal agents for targeting the latent HIV-1 reservoir. Science Translational Medicine 2022, 14 (667) abh3351. [Link]

  • Christoph Bock, Paul Datlinger, Florence Chardon, Matthew A. Coelho, Matthew B. Dong, Keith A. Lawson, Tian Lu, Laetitia Maroc, Thomas M. Norman, Bicna Song, Geoff Stanley, Sidi Chen, Mathew Garnett, Wei Li, Jason Moffat, Lei S. Qi, Rebecca S. Shapiro, Jay Shendure, Jonathan S. Weissman & Xiaowei Zhuang. High-content CRISPR screening. Nature Reviews Methods Primers, 2, Article number: 8 (2022). [Link]

  • Yingbo Cui*, Xiaolong Cheng*, Qing Chen, Bicna Song, Anthony Chiu, Yuan Gao, Tyson Dawson, Lumen Chao, Wubing Zhang, Dian Li, Zexiang Zeng, Jijun Yu,  Zexu Li, Teng Fei, Shaoliang Peng, Wei Li#. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Research 2021, 49 (D1) D848-854. [Link][Website]
  • Lin Yang*, Yuqing Zhu*Hua Yu*, Xiaolong Cheng, Sitong Chen, Yulan ChuHe HuangJin Zhang#Wei Li#Linking genotypes with multiple phenotypes in single-cell CRISPR screens. Genome Biology 2020, 21, 19. [Link][Software].
  • Teng Fei*, Wei Li*, Jingyu Peng, Tengfei Xiao, Chen-Hao Chen, Alexander Wu, Jialiang Huang, Chongzhi Zang, X. Shirley Liu#, and Myles Brown#. Deciphering essential cistromes using genome-wide CRISPR screens. PNAS 2019. [Link]
  • Tengfei Xiao*, Wei Li*, Xiaoqing Wang, Han Xu, Qiu Wu, Peng Jiang, Jixin Yang, Teng Fei, Chongzhi Zang, Qi Liao, Jonathan Rennhack, Eran Andrechek, Rinath M. Jeselsohn, X. Shirley Liu#, Myles Brown#. Estrogen-regulated Feedback Loop Limits the Efficacy of Estrogen Receptor-targeted Breast Cancer Therapy. PNAS 2018, 115 (31), 7869-7878. [Link]
  • Binbin Wang*, Mei Wang*, Wubing Zhang*, Tengfei Xiao, Chen-Hao Chen, Alexander Wu, Feizhen Wu, Nicole Traugh, Xiaoqing Wang, Ziyi Li, Shenglin Mei, Yingbo Cui, Sailing Shi, Jesse Jonathan Lipp, Matthias Hinterndorfer, Johannes Zuber, Myles Brown, Wei Li#, X. Shirley Liu#. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nature Protocols 2019, (14) 756-780.
  • Chen-Hao Chen*, Tengfei Xiao*, Han Xu, Peng Jiang, Cliff Meyer, Wei Li#, Myles Brown#, X. Shirley Liu#. Integrative design and analysis of CRISPR Knockout Screens. Bioinformatics 2018, 34 (23) 4095–4101. [Link]
  • Qingyi Cao, Jian Ma, Chen-Hao Chen, Han Xu, Zhi Chen#, Wei Li#, 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, Chen-Hao Chen, Qi Liao, Han Xu, Tengfei Xiao, Zhongzheng Cao, Jingyu Peng, Pengfei Yuan, Myles Brown, Xiaole Shirley Liu & Wensheng Wei. CRISPR/Cas9-mediated genomic deletion screening for long non-coding RNAs using paired-gRNAs. Nature Biotechnology 2016, 34:1279-1286. [Link]
  • Wei Li*, Han Xu*, Tengfei Xiao, Le Cong, Feng Zhang, Jun S. Liu, Myles Brown, X. Shirley Liu. MAGeCK enables robust identification of essential genes from genome-scale CRISPR-Cas9 knockout screens. Genome Biology 2014, 15:554. Citation: >300; >50k software downloads. [Link][Software]
  • Wei Li*, Johannes Koster*, Tengfei Xiao, Han Xu, Chen-Hao Chen, Jun S. Liu, Myles Brown, Xiaole S. Liu. Quality control, modeling and visualization of genome-wide CRISPR screens using MAGeCK-VISPR. Genome Biology 2015, 16:281. [Link]
  • 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. Also appear in 2015 Asian Pacific Bioinformatics Conference (APBC 2015). APBC 2015 Best Paper Award. [Link]


  • Alexander Wu, Tengfei Xiao, Teng Fei, X Shirley Liu, Wei Li. Reducing false positives in CRISPR/Cas9 screens from copy number variations. bioRxiv 2018. [Link]


Lab Alumni

Wei Li: Principal Investigator

[Google Scholar] [CV] [More]

As a computational biologist with a background in computer science, Wei is always fascinated by the exciting opportunities in both areas of computation and biology. His past research has focused on developing analysis algorithms on functional genomics, especially for RNA-seq and CRISPR-Cas9 screens.

Wei received his postdoc training in Dr. X. Shirley Liu lab at Dana-Farber Cancer Institute and Harvard School of Public Health. He obtained his Ph.D. in computer science at University of California, Riverside (mentor: Dr. Tao Jiang), followed by his bachelor and master degrees of computer science and technology at Tsinghua University, Beijing, China.

Xiaolong Cheng

Xiaolong Cheng is a postdoctoral research fellow at Children’s National Medical Center. He is interested in developing novel computational methods and software tools for understanding biological data.

Xiaolong obtained his Ph.D. degree at the Ocean University of China and majored in Intelligent Information and Communication Systems. In the Ph.D. study phase, his research was focused on signal processing and machine learning.

Bicna Song

Bicna Song is a postdoctoral research fellow at Children’s National Medical Center. She is interested in the development of computational algorithms for analyzing large-scale sequencing data to identify predictive biomarker or drug target in cancer. Bicna obtained her Ph.D. degree of bioinformatics from the University of Science and Technology in South Korea. She focused on analyzing cancer genomics dataset for identifying and understanding the molecular drivers of differential response to targeted therapy.

Lumen Chao

Lumen Chao is a Postdoctoral Research Fellow at Children’s National Hospital. Her research interest focus on utilizing genome-wide CRISPR screen and single-cell RNA-seq technology to systematically identify critical genes and potential drug targets in brain tumors. Having been trained as an experimental biologist for years, she hopes to integrate both computational biology and experimental biology to comprehensively analyze the critical genes (oncogenes/suppressers) and regulatory networks in pediatric brain tumor.

Lumen received her first postdoc training at Michigan State University. She obtained her Ph.D. degree in Genetics at the Chinese Academy of Sciences, Shanghai, China and bachelor’s degree in biology at Nanjing University, Nanjing, China. She focused on uncovering key transcription factors and regulatory networks of temperature response and immunity in plants.

Kai Wang

Kai Wang is a Postdoctoral Research Fellow at Children’s National Hospital. His research focus on learning, developing and utilizing genome-wide CRISPR screen and single-cell RNA-seq technology to identify critical genes and potential drug targets in human brain tumors. Kai received his first postdoc training in NIH. He obtained his Ph.D. in Chinese Academy of Sciences, Beijing, China, focusing on ion channels, electrophysiology and electrochemistry in neuroscience.

Pamela Chansky

Pamela Chansky is a PhD student in the Genomics and Bioinformatics program at George Washington University. Her research interests include applying machine learning to immunological problems and VST therapy. She obtained her B.S. in biomedical engineering from Johns Hopkins, where her undergraduate research focused on computational modeling of therapeutic targets for non-small cell lung cancer.

Lab alumni

Postdocs and technicians

  • Qing Chen — technician (2020). Now a GWU PhD student.

Master students

  • Sitong Chen — GWU Biochemistry and Molecular Medicine (2019-2020). Now biostatistician at University of Miami.
  • Lin Yang — GWU BMM (2019-2020). Now an associate computational biologist at Dana-Farber Cancer Institute.
  • Chia-Han Lee — GWU BMM (2019-2020). Now a staff scientist at NIH.
  • Yuan Gao — GWU BMM (2020-2021). Now a Ph.D. student at UMD.
  • Ruocheng Shan — GWU CS (2020-2021). Now Ph.D. student at GWU.
  • Wei Shao — GWU BMM (2020-2021). Now Ph.D. student at UC Irvine.

Rotating Students

  • Neerja Vashist — GWU IBS Ph.D. program (2020).
  • Tyson Dawson — GWU IBS program (2020).
  • Anthony Chiu — GWU first year medical student awarded with Gill Fellowship (2020).
  • Pamela Chansky — GWU IBS Ph.D. program (2021).

High school, undergraduate, and graduate interns

  • Abhinav Adhikari — high school student (2021). Now a freshman at the University of Washington, Seattle.
  • Julia Kao-Sowa — high school student (2020) at Thomas Jefferson High School for Science and Technology.
  • Mozhan Haghighatian — UMD undergraduate (2020).
  • Daniel Zheng — high school student (2022)
  • Isabelle Yang — high school student (2022)
  • Rod Rahmjoo — UMD undergraduate and graduate (2022).

Lab News

  • 2023.5 Our Cas13 intrinsic RNA targeting manuscript is accepted by Nature Biomedical Engineering. Congratulations to our wonderful lab members (Xiaolong, Lumen) and great collaborators (Teng Fei, Zexu Li, Zihan Li and other Fei lab members)!
  • 2023.4 Daniall Masood (IBS rotation student) and Li-Ting Wang (BMM master student) joined our lab. Welcome Daniall and Li-Ting!
  • 2023.4 The R01 proposal with Hanrui Zhang (Columbia University) and Edoardo Marcora (Mount Sinai) was awarded by NHLBL (R01HL168174). Look forward to having more exciting science!
  • 2023.2 Our DeepCas13 paper is published in Nature Communications online.
  • 2023.2 Congratulations to Pamela Chansky on receiving the Cosmos Club Foundation Scholars Awards!
  • 2022.12 Our collaboration paper on macrophage CRISPR screens is accepted by Nature Communications. Congratulations to our collaborators (Hanrui Zhang, Columbia University)!
  • 2022.11 Our DeepCas13 manuscript is accepted by Nature Communications! Congratulations to the team (esp. Xiaolong Cheng) and our great collaborators, Teng Fei (and Zexu Li)!
  • 2022.10 Our STM HIV CRISPR screening paper is online.
  • 2022.8 Junyan (Jeremy) Wu and Quan Yuan, two master students from GWU Biochemistry and Molecular Biology, joined us. Welcome Jeremy and Quan!
  • 2022.7 Our collaboration paper on latent HIV CRISPR screens is accepted by Science Translational Medicine. Congratulations to the team (esp. Bicna Song) and our great collaborators at Robert Siliciano lab (Johns Hopkins University)!
  • 2022.6 Pamela Chansky joined us as a Ph.D. student. Welcome (again) Pamela!
  • 2022.5 Our collaboration manuscript (with Teng Fei group, Northeastern University, China) on intrinsic Cas13 targets is online at bioRxiv. Great work to the team in our lab and the collaborator lab!
  • 2022.3 Our lab is moving to the new Children’s National Research and Innovation Campus (CNRIC)!
  • 2022.2 Our collaboration paper (with Dr. Yarui Diao lab at Duke University) is published in Molecular Cell. Congratulations to our lab alumni Ruocheng Shan!
  • 2022.2 The high-content CRISPR screening review paper is online at Nature Reviews Methods Primers. Congratulations to Bicna and our great collaborator team!
  • 2022.1 Daniel Zheng and Rod Rahmjoo joined as interns. Welcome!
  • 2022.1 The collaboration paper (with X. Shirley Liu, Myles Brown and Kexin Xu) on EZH2 inhibitor is published in PNAS.
  • 2021.12 The review manuscript of CRISPR screening (working with Christopher Bock and many other groups around the world) was accepted in Nature Reviews Methods Primers. Congratulations!
  • 2021.9 The collaboration paper (with X. Shirley Liu group) on in vivo CRISPR screens is published in Cell. Great work to the entire Liu lab team!
  • 2021.9 We enjoyed our happy hour in person, after 18 months!
  • 2021.9 Our DeepCas13 manuscript preprint is now online at bioRxiv with companion website DeepCas13. Great work to the team and the collaborators (Prof. Teng Fei)!
  • 2021.5 Congratulations to the three GWU master students who will go to the PhD programs this fall: Yuan Gao (U Maryland), Ruocheng Shan (GWU) and Wei Shao (UCI)!
  • 2021.4 Our lab received a second round of support from COVID-19 HPC Consortium to identify potential drug targets of SARS-COV-2.
  • 2021.2 We received a pilot grant from our center for Genetic Medicine Research to study Respiratory Syncytial Virus (RSV) using CRISPR screens! This is going to be a collaboration project with Dr. Nino Gustavo and Dr. Jyoti Jaiswal within Children’s National.
  • 2021.2 Our collaboration paper with Teng Fei lab on SARS-COV-2 drug repurposing is accepted by iScience. Congratulations to Xiaolong, Qing, Zexu and Teng!

Not-that-new News (before 2021)

Join us

Postdoc, graduate student, and visiting scholar positions are all open in the lab!

Postdoc positions

Computational biology and molecular biology postdoc positions are available in the laboratory of Wei Li at Children’s National Hospital and George Washington School of Medicine and Health Sciences at Washington, DC. In the past we have developed innovative bioinformatics algorithms to design, analyze and visualize genome-wide CRISPR/Cas9 knockout screening data, used CRISPR screening to identify genes responsible for drug resistance and synthetic lethality, and further understand the functions of non-coding elements.

We are devoted to using cutting-edge computational methods, in combination with new genome engineering and single cell approaches to understand how coding and non-coding elements function in cancer and childhood diseases.

We also accept graduate students and visiting scholars. Please contact the PI directly for opportunities.

Outstanding research and living community

Candidates will join the outstanding research community at Children’s National Hospital and George Washington University, and have the opportunity to interact with scientists at nearby institutions including National Institute of Health (NIH), John Hopkins University, University of Maryland, etc.

Children’s National Hospital, based in Washington, D.C., has been serving the nation’s children since 1870. Children’s National is #6 overall, #1 for babies and ranked in every specialty evaluated by U.S. News & World Report for children’s hospital in the U.S. Home to the Children’s Research Institute, Children’s National is one of the nation’s top NIH-funded pediatric institutions. Children’s National is recognized for its expertise and innovation in pediatric care and as a strong voice for children through advocacy at the local, regional and national levels.

As the capital of United States, Washington DC is the hub of American politics and history. Washington DC metropolitan area (including Virginia and Maryland) is the home of people from diverse backgrounds, and is considered one of the best places to live and work in US.

Unique career development opportunities

By joining a newly established lab, the candidate will have unique opportunities to set up a research laboratory and interact with the PI/collaborators. The candidate will also gain experiences and guidances in a variety of aspects including grant writing, presentation, career transition, networking, etc.

Computational biology postdocs

We will conduct research focusing on the following areas:
  • Develop algorithms to analyze large-scale screening and sequencing data;
  • Use the latest machine learning algorithms to study cancer genomics data and identify predictive biomarkers or drug targets;
  • Collaborate with experimental and clinical labs to study a variety of biological and biomedical problems, including (1) pediatric diseases especially glioma and Neurofibromatosis type 1 (NF1); (2) the functions of coding and non-coding elements using genetic screening and single-cell sequencing approaches; and (3) other exciting collaboration projects.
Responsibilities of the position will include but not limited to: methodology development, coding, statistical analysis of big biomedical data, writing manuscript, application to postdoctoral fellowship and communication with other researchers.
Ideal applicants are expected to have:
  • PhD degree in Bioinformatics/Genetics/Computer Science/Statistics or other quantitative science;
  • Solid programming skills, strong publication record and the ability to work independently;
  • Experienced in cancer genomics data analysis and computational methodology development;
  • Ability to communicate and collaborate with other team members;
  • Additional expertise in cancer biology, machine learning, single-cell genomics and childhood diseases would be a plus.

Postdoc fellow: genomics and genome engineering

The successful applicant will be responsible for (1) establishing the basic components of a molecular biology lab, (2) utilizing CRISPR-Cas9 based gene editing and single cell technologies to understand human cancer and rare disease, especially in pediatric brain tumor, (3) identifying potential interventions. Candidate will also have the opportunity to receive rigorous training in bioinformatics and computational biology, and be under the joint supervision of collaboration labs at Children’s National. Prior lab experience with molecular biology techniques (e.g., CRISPR/Cas9) is required.

Ideal applicants are expected to:

  • Hold Ph.D. degree in cellular and molecular biology, biochemistry, etc.
  • Be experienced in molecular biology (tissue culture, PCR, cloning, genotyping, RNA and DNA isolation, flow cytometry, Western blots, etc.)
  • Familiar with genome editing (CRISPR/Cas9)
  • Demonstrate independent critical thinking and rigorous work.
  • Have strong record of productivity and good publication record in peer-reviewed journals.
  • Have strong personal skills, and excellent verbal and written communication skills.
  • Work effectively both independently and collaboratively
  • Prior knowledge of pediatric diseases not required.

How do I apply?

Interested candidates should submit a CV, a cover letter of research background and future research goals, and the contact information of three references letters by email to Wei Li (li.david.wei AT gmail). More information can be found from our website (https://weililab.org).


We are recruiting!

Wei Li

Center for Genetic Medicine Research | Center for Cancer and Immunology Research

Children’s National Research and Innovation Campus (CNRIC), Children’s National Hospital

7144 13th Pl NW, Washington DC 20012

Department of Genomics and Precision Medicine | Department of Pediatrics

George Washington School of Medicine and Health Sciences

Email: li.david.wei AT gmail.com or wli2 AT childrensnational.org