Evidence map›Paper›PMID 32796939›Full record

ArticleNature protocols2020

GOTI, a method to identify genome-wide off-target effects of genome editing in mouse embryos.

Erwei Zuo, Yidi Sun, Wu Wei, Tanglong Yuan, Wenqin Ying, Hao Sun, Liyun Yuan, Lars M Steinmetz, Yixue Li, Hui Yang

Open access · greenAbstract read
In one paragraph

Article in Nature protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 40 citations in OpenAlex.

  1. Review
  2. Engineered Transformer Base Editor with Enhanced Editing Efficiency.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Molecular therapy. Nucleic acids · 2025
    Review
  8. Review
  9. CRISPR and gene editing technologies for bleeding disorders.Therapeutic advances in hematology · 2025
    Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. CRISPR/Cas9 systems: Delivery technologies and biomedical applications.Asian journal of pharmaceutical sciences · 2023
    Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Computational Tools and Resources for CRISPR/Cas Genome Editing.Genomics, proteomics & bioinformatics · 2023
    Review
  20. Mitochondrial genome engineering coming-of-age.Trends in genetics : TIG · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 4 countries.

Erwei Zuo *Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID http://orcid.org/0000-0001-8259-0275
Yidi Sun *Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID http://orcid.org/0000-0002-4191-2917
Wu Wei *Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences Shanghai, Shanghai, China.ORCID http://orcid.org/0000-0001-5643-8739
Tanglong Yuan *Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.ORCID http://orcid.org/0000-0003-4670-2625
Wenqin YingInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Hao SunUniversity of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0002-4790-8847
Liyun YuanBio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences Shanghai, Shanghai, China.
Lars M SteinmetzStanford Genome Technology Center, Stanford University, Palo Alto, CA, USA. lars.steinmetz@stanford.edu.ORCID http://orcid.org/0000-0002-3962-2865
Yixue LiBio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences Shanghai, Shanghai, China. yxli@sibs.ac.cn.
Hui YangInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. huiyang@ion.ac.cn.ORCID http://orcid.org/0000-0003-3590-722X
Chinese Academy of Sciences · CNShanghai Jiao Tong University · CNAgricultural Genomics Institute at Shenzhen · CNEuropean Bioinformatics Institute · GBUniversity of California San Diego · US

Funding

WHOLE YEAST GENOME TRANSCRIPTIONAL AND FUNCTIONAL GENE MAPS--A FEASIBILITY STUDYP01HG000205 · NHGRI · STANFORD UNIVERSITY · PI DAVIS, RONALD WAYNE, HOWE, ROGER THOMAS · 1990 to 2016
$120.9M
NHGRI NIH HHS P01 HG000205
6 · The paper itself

Abstract

Genome editing holds great potential for correcting pathogenic mutations. We developed a method called GOTI (genome-wide off-target analysis by two-cell embryo injection) to detect off-target mutations by editing one blastomere of two-cell mouse embryos using either CRISPR-Cas9 or base editors. GOTI directly compares edited and non-edited cells without the interference of genetic background and thus could detect potential off-target variants with high sensitivity. Notably, the GOTI method was designed to detect potential off-target variants of any genome editing tools by the combination of experimental and computational approaches, which is critical for accurate evaluation of the safety of genome editing tools. Here we provide a detailed protocol for GOTI, including mice mating, two-cell embryo injection, embryonic day 14.5 embryo digestion, fluorescence-activated cell sorting, whole-genome sequencing and data analysis. To enhance the utility of GOTI, we also include a computational workflow called GOTI-seq (https://github.com/sydaileen/GOTI-seq) for the sequencing data analysis, which can generate the final genome-wide off-target variants from raw sequencing data directly. The protocol typically takes 20 d from the mice mating to sequencing and 7 d for sequencing data analysis.

Indexed as

AnimalsEmbryo, MammalianFemaleGene EditingInjectionsMaleMiceMice, Inbred C57BLMutation

Identifiers

PMID32796939
PMCPMC8190672
OpenAlexW3049215545

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.