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

Join Us Research

The lab uses the latest genome 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 (SSC and CRISPR-DO), algorithms for the modeling and processing of CRISPR screens (MAGeCK/MAGeCK-VISPR), and for the interpretation of CRISPR screens using network or pathway information (NEST, MAGeCK-NEST). These algorithms became popular in the field: MAGeCK/MAGeCK-VISPR reaches over 20k paper visits and over 24k software downloads.



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: 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.


Analyzing critical genes in breast cancer

Example 2: 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.


lncRNA screening: designing algorithm (left) and identifying top hits (right)


Transcriptome dynamics from RNA-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.


The IsoLasso splicing model


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

Selected publications

  • 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: 82; >24k 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]
  • Wei Li, Jianxing Feng, Tao Jiang. IsoLasso: A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly. Journal of Computational Biology 2011, 18(11):1693-1707. Also appear in the Research in Computational Molecular Biology (RECOMB 2011). Citation: 142. [Link][Software]
  • Wei Li and Tao Jiang. Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads. Bioinformatics 2012, 28(22):2914-2921. Citation: 63. [Link]
  • Jianxing Feng, Wei Li, Tao Jiang. Inference of isoforms from short sequence reads. Journal of Computational Biology 2011, 18(3):305-321. Also appear in the Research in Computational Molecular Biology (RECOMB 2010). Citation: 85. [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]


Wei Li: Principal Investigator

[Google Scholar] [CV]

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. Wei obtained his Ph.D. in computer science at University of California, Riverside, followed by his bachelor and master degrees of computer science at Tsinghua University, Beijing, China.


We are recruiting!

Wei Li laboratory

Children’s National Medical Center

111 Michigan Avenue, NW Washington, DC 20010

Email: li.david.wei AT gmail.com


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 #1 for babies and ranked in every specialty evaluated byU.S. News & World Report and 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. For example, Washington DC is ranked 4th by Business Insider as “Best Places To Live” in US, and many cities in the DC metro area are ranked as “Best Places To Raise A Family” (e.g., here).

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.