Evidence map›Paper›PMID 32459325›Full record

ArticleNucleic acids research2020

PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins.

Jiawei Wang, Wei Dai, Jiahui Li, Ruopeng Xie, Rhys A Dunstan, Christopher Stubenrauch, Yanju Zhang, Trevor Lithgow

Abstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 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

40 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  11. AcrDB update: Predicted 3D structures of anti-CRISPRs in human gut viromes.Protein science : a publication of the Protein Society · 2025
    Article
  12. CombattingAntibiotics (Basel, Switzerland) · 2025
    Review
  13. Article
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  18. Comparative Analyses of Bacteriophage Genomes.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
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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.

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.
Ruopeng XieSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Rhys A DunstanInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Christopher StubenrauchInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.
Yanju ZhangSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Trevor LithgowInfection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR-Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR-Cas machineries for the development of new CRISPR-Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.

Indexed as

BacteriophagesCRISPR-Cas SystemsSoftwareComputer GraphicsMachine LearningSequence Analysis, ProteinViral ProteinsViral Proteins

Identifiers

PMID32459325
PMCPMC7319593

What OpenQuestion holds

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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.