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 Medical Center. We are also affiliated with Department of Genomics and Precision Medicine, 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:

And so on. These algorithms became popular in the field: the MAGeCK suite reach over 20k paper visits and over 50,000 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.


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


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



  • Lin YangYuqing ZhuHua YuSitong ChenYulan ChuHe HuangJin ZhangWei Li. Linking genotypes with multiple phenotypes in single-cell CRISPR screens. bioRxiv, 2019. [Link][Software].


Selected publications

*first/co-first author; #corresponding/co-corresponding author

  • 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]




Sitong Chen

Sitong Chen is a master student at GWU and majors in Bioinformatics. She is very excited to learn about computational biology and use different software and algorithms to solve biological problems.

Sitong obtained her bachelor degree in China. In her undergraduate, she focused on the antibacterial activity of Quantum Dots and salt-tolerant microorganism.

Lin Yang

Lin is a GWU master student in bioinformatics track under the Department of Biochemistry & Molecular Medicine. He is always ready for absorbing and integrating cutting-edge knowledge both in technology and research.

Lin obtained his bachelor degree of biological science at China Agricultural University. During college, he focused on analyzing the function of genes associated with Nitrogen-fixation.

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.

Lab News

  • 2019.9 Our recent R01 award was highlighted in Children’S National Innovation District website (also in EurekAlert).
  • 2019.8 Wei received a 5-year R01 support from NIH/NHGRI for the algorithm development of CRISPR screens! Cheers!
  • 2019.8 Wei gave a talk at 2019 Young Bioinformatics PI forum (YBP 2019) about our recent work.
  • 2019.7 Welcome our lab’s first postdoc, Xiaolong Cheng!
  • 2019.6 Our “First Cut” essay on the recent Sanger DepMap paper was published on CRISPR Journal. Great work from Sitong and Lin!
  • 2019.6 Our scMAGeCK open source tool to model CRISPR screening + single-cell RNA-seq data is released in bitbucket. The corresponding manuscript is also posted in biorxiv. Great collaboration with Jin Zhang lab at Zhejiang University!
  • 2019.3 Wei got the Board of Visitors (BoV) award from Children’s National Medical Center to identify drug targets in brain tumor. Thanks to the support from Dr. Yuan Zhu, as well as the Board of Visitors at Children’s!
  • 2019.2 Our MAGeCKFlute paper was online at Nature Protocols. Great work, Tongji team!
  • 2019.2 Our collaboration paper with Myles Brown lab (ARv7) was online at Cancer Cell (Wei is a co-author). Thanks Laura and the Brown lab!
  • 2019.1 Wei was awarded the Research Starter Grant in Informatics from the Pharmaceutical Research and Manufacturers of American Foundation (PhRMA)! Thanks Shirley and Myles for the support!
  • 2019.1 Our collaboration paper (iBAR) with Wensheng Wei lab (Peking University) was published at Genome Biology (Wei is a co-author). Thanks to the teams of Wei lab!
  • 2019.1 Welcome GWU master students Lin Yang and Sitong Chen!
  • 2018.10 Our collaboration paper with Myles Brown lab was accepted by Cancer Cell (Wei is a co-author).
  • 2018.8 Wei gave a talk at Cold Spring Harbor Laboratory (CSHL) Genome Engineering: The CRISPR/Cas9 Evolution meeting.
  • 2018.6 Our CSK paper was accepted by PNAS (Wei is a co-first author). That’s one of the fruitful outcomes from our collaboration with Myles Brown lab. Thanks Tengfei (Ted) Xiao for his great work!
  • 2018.6 The MAGeCKFlute paper was accepted by Nature Protocol (Wei is a co-corresponding author). Thanks Binbin Wang, Wubing Zhang, and Feizhen Wu for the hard work!
  • 2018.5 Our MAGeCK-NEST paper was accepted by Bioinformatics (Wei is a co-corresponding author). Thanks Chen-Hao for the hard work!

Join us

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

Postdoc positions

Computational biology postdoc positions are available in the laboratory of Wei Li, Center for Genetic Medicine Research, Children’s National Medical Center, and Department of Genomics and Precision Medicine, The George Washington School of Medicine and Health Sciences at Washington, DC.

What will you get from our lab?

Exciting research projects

We are devoted to developing cutting-edge computational methods for biology and medicine, with a focus on understanding how coding and non-coding elements function in cancer and childhood diseases. In the past we have developed innovative bioinformatics algorithms to 1) design, analyze and visualize genome-wide CRISPR/Cas9 knockout screening data (MAGeCK/MAGeCK-VISPR); 2) identify genes responsible for cancer drug resistance and synthetic lethal targets (Xiao, Li, et al.) and 3) understand how non-coding elements, especially long non-coding RNAs and enhancers, play roles in cancer (Zhu, Li, et al; Fei, Li, Peng, et al.).
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.

Outstanding research and living community

Candidates will join the outstanding research community at Children’s National Medical Center, 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 Health System, based in Washington, D.C., has been serving the nation’s children since 1870. Children’s National is #5 overall, #1 for babies and ranked in every specialty evaluated by U.S. News & World Report for children’s hospital in the U.S. It has been designated two times as a Magnet® hospital, a designation given to hospitals that demonstrate the highest standards of nursing and patient care delivery. This pediatric academic health system offers expert care through a convenient, community-based primary care network and specialty outpatient centers in the D.C. Metropolitan area including the Maryland suburbs and Northern Virginia. Home to the Children’s Research Institute and the Sheikh Zayed Institute for Pediatric Surgical Innovation, 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.

What do we expect from you?

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.

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).

Graduate students and visiting scholars

Please contact Wei Li (li.david.wei AT gmail) for opportunities regarding graduate students and visiting scholars.


We are recruiting!


Wei Li

Center for Genetic Medicine Research | Center for Cancer Immunology Research

Children’s National Medical Center

Department of Genomics and Precision Medicine | Department of Pediatrics

George Washington School of Medicine and Health Sciences

111 Michigan Avenue NW, Washington, DC 20010

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