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Anti-CRISPRs: Protein Inhibitors of CRISPR-Cas Systems

2020· review· en· W3011435240 on OpenAlexafffund
Alan R. Davidson, Wangting Lu, Sabrina Y. Stanley, Jingrui Wang, Marios Mejdani, Chantel N. Trost, Brian T. Hicks, Jooyoung Lee, Erik J. Sontheimer

Bibliographic record

VenueAnnual Review of Biochemistry · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsCRISPRBiologyComputational biologyGenomeGenome editingPalindromeMobile genetic elementsArchaeaGeneticsCRISPR interferenceTrans-activating crRNAGene

Abstract

fetched live from OpenAlex

(CRISPR-associated) genes are found frequently in bacteria and archaea, serving to defend against invading foreign DNA, such as viral genomes. CRISPR-Cas systems provide a uniquely powerful defense because they can adapt to newly encountered genomes. The adaptive ability of these systems has been exploited, leading to their development as highly effective tools for genome editing. The widespread use of CRISPR-Cas systems has driven a need for methods to control their activity. This review focuses on anti-CRISPRs (Acrs), proteins produced by viruses and other mobile genetic elements that can potently inhibit CRISPR-Cas systems. Discovered in 2013, there are now 54 distinct families of these proteins described, and the functional mechanisms of more than a dozen have been characterized in molecular detail. The investigation of Acrs is leading to a variety of practical applications and is providing exciting new insight into the biology of CRISPR-Cas systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.342
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations165
Published2020
Admission routes2
Has abstractyes

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