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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.

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


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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