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Nucleic Acids Res. 2018 Feb 16;46(3):1375-1385. doi: 10.1093/nar/gkx1268.

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

Author information

1
Pediatric Hematology & Oncology, Hannover Medical School, Hannover, Germany.
2
Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany.
3
REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany.
4
Department of Regulation in Infection Biology, Max Planck Institute for Infection Biology, Berlin, Germany.
5
The Laboratory for Molecular Infection Medicine Sweden, Umeå University, Umeå, Sweden.
6
Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.
7
Centre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany.
8
Department of Information Technology, University of Oviedo, Oviedo, Asturias, Spain.
9
Department of Pediatrics I, Pediatric Hematology and Oncology, University of Halle, Halle, Germany.

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.

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