Evidence map›Paper›PMID 33137193›Full record

ArticleNucleic acids research2021

AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins.

Jiawei Wang, Wei Dai, Jiahui Li, Qi Li, Ruopeng Xie, Yanju Zhang, Christopher Stubenrauch, Trevor Lithgow

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 12% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. The defensome of prokaryotes in aquifers.Nature communications · 2025
    Article
  10. AcrDB update: Predicted 3D structures of anti-CRISPRs in human gut viromes.Protein science : a publication of the Protein Society · 2025
    Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. AcrNET: predicting anti-CRISPR with deep learning.Bioinformatics (Oxford, England) · 2023
    Article
  18. Review
  19. Mechanisms regulating the CRISPR-Cas systems.Frontiers in microbiology · 2023
    Review
  20. 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

8 authors at 2 institutions in 2 countries.

Jiawei WangInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Wei DaiInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Jiahui LiSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Qi LiSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Ruopeng XieSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Yanju ZhangSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Christopher StubenrauchInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Trevor LithgowInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Australian Regenerative Medicine Institute · AUGuilin University of Electronic Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anti-CRISPR (Acr) proteins naturally inhibit CRISPR-Cas adaptive immune systems across bacterial and archaeal domains of life. This emerging field has caused a paradigm shift in the way we think about the CRISPR-Cas system, and promises a number of useful applications from gene editing to phage therapy. As the number of verified and predicted Acrs rapidly expands, few online resources have been developed to deal with this wealth of information. To overcome this shortcoming, we developed AcrHub, an integrative database to provide an all-in-one solution for investigating, predicting and mapping Acr proteins. AcrHub catalogs 339 non-redundant experimentally validated Acrs and over 70 000 predicted Acrs extracted from genome sequence data from a diverse range of prokaryotic organisms and their viruses. It integrates state-of-the-art predictors to predict potential Acrs, and incorporates three analytical modules: similarity analysis, phylogenetic analysis and homology network analysis, to analyze their relationships with known Acrs. By interconnecting all modules as a platform, AcrHub presents enriched and in-depth analysis of known and potential Acrs and therefore provides new and exciting insights into the future of Acr discovery and validation. AcrHub is freely available at http://pacrispr.erc.monash.edu/AcrHub/.

Indexed as

Databases, ProteinCRISPR-Cas SystemsData AnalysisInternet

Identifiers

PMID33137193
PMCPMC7779044
OpenAlexW3097237310

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.