Evidence map›Paper›PMID 29036676›Full record

ArticleNucleic acids research2018

Anti-CRISPRdb: a comprehensive online resource for anti-CRISPR proteins.

Chuan Dong, Ge-Fei Hao, Hong-Li Hua, Shuo Liu, Abraham Alemayehu Labena, Guoshi Chai, Jian Huang, Nini Rao, Feng-Biao Guo

Abstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. CRISPR-Cas9 and Its Bioinformatics Tools: A Systematic Review.Current issues in molecular biology · 2025
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. The trade-off ofFrontiers in microbiology · 2024
    Article
  17. Article
  18. CRISPR/Cas9 Landscape: Current State and Future Perspectives.International journal of molecular sciences · 2023
    Review
  19. Article
  20. AcrNET: predicting anti-CRISPR with deep learning.Bioinformatics (Oxford, England) · 2023
    Article
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

9 authors.

Chuan DongSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Ge-Fei HaoKey Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, PR China.
Hong-Li HuaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Shuo LiuSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Abraham Alemayehu LabenaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Guoshi ChaiSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Jian HuangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Nini RaoSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Feng-Biao GuoSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR-Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.

Indexed as

CRISPR-Cas SystemsDatabases, ProteinBacteriophagesViral ProteinsViral Proteins

Identifiers

PMID29036676
PMCPMC5753274

What OpenQuestion holds

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LicenceCC BY-NC
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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.