Evidence map›Paper›PMID 29267886›Full record

ArticleNucleic acids research2018

Refined sgRNA efficacy prediction improves large- and small-scale CRISPR-Cas9 applications.

Maurice Labuhn, Felix F Adams, Michelle Ng, Sabine Knoess, Axel Schambach, Emmanuelle M Charpentier, Adrian Schwarzer, Juan L Mateo, Jan-Henning Klusmann, Dirk Heckl

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 165 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
165citing 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

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

  1. Pooled it
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  18. Multi-step implementation of meiotic crossover patterning.bioRxiv : the preprint server for biology · 2025
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105 more citing papers are in PubMed but not listed here.

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.

Maurice LabuhnPediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.
Felix F AdamsInstitute of Experimental Hematology, Hannover Medical School, Hannover, Germany.
Michelle NgPediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.
Sabine KnoessPediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.
Axel SchambachInstitute of Experimental Hematology, Hannover Medical School, Hannover, Germany.
Emmanuelle M CharpentierDepartment of Regulation in Infection Biology, Max Planck Institute for Infection Biology, Berlin, Germany.
Adrian SchwarzerInstitute of Experimental Hematology, Hannover Medical School, Hannover, Germany.
Juan L MateoCentre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany.
Jan-Henning KlusmannPediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.
Dirk HecklPediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome editing with the CRISPR-Cas9 system has enabled unprecedented efficacy for reverse genetics and gene correction approaches. While off-target effects have been successfully tackled, the effort to eliminate variability in sgRNA efficacies-which affect experimental sensitivity-is in its infancy. To address this issue, studies have analyzed the molecular features of highly active sgRNAs, but independent cross-validation is lacking. Utilizing fluorescent reporter knock-out assays with verification at selected endogenous loci, we experimentally quantified the target efficacies of 430 sgRNAs. Based on this dataset we tested the predictive value of five recently-established prediction algorithms. Our analysis revealed a moderate correlation (r = 0.04 to r = 0.20) between the predicted and measured activity of the sgRNAs, and modest concordance between the different algorithms. We uncovered a strong PAM-distal GC-content-dependent activity, which enabled the exclusion of inactive sgRNAs. By deriving nine additional predictive features we generated a linear model-based discrete system for the efficient selection (r = 0.4) of effective sgRNAs (CRISPRater). We proved our algorithms' efficacy on small and large external datasets, and provide a versatile combined on- and off-target sgRNA scanning platform. Altogether, our study highlights current issues and efforts in sgRNA efficacy prediction, and provides an easily-applicable discrete system for selecting efficient sgRNAs.

Indexed as

AlgorithmsCRISPR-Cas SystemsBase CompositionBase SequenceCell Line, TumorClustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Associated Protein 9Gene EditingGene TargetingHEK293 CellsHumansLeukocytesNucleic Acid ConformationRNA, Guide, CRISPR-Cas SystemsCas9 endonuclease Streptococcus pyogenesCRISPR-Associated Protein 9RNA, Guide, CRISPR-Cas Systems

Identifiers

PMID29267886
PMCPMC5814880

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.