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A novel, high-fidelity Cas9 improves CRISPR editing accuracy without sacrificing performance

The introduction of the CRISPR-Cas9 system for genome editing has been an incredible boon for biological research. As our understanding of CRISPR technology increases, so does our awareness of the off-target editing events carried out by Cas9 nuclease. Existing CRISPR guide RNA design tools, such as CRISPR Design (MIT [1]) and CCTop (CRISPR-Cas9 Target online predictor, Heidelberg University [2]), attempt to predict off-target editing of the guide RNAs. However, these tools are limited by an incomplete understanding of the factors that cause Cas9 to cleave at incorrect sites.

The introduction of the CRISPR-Cas9 system for genome editing has been an incredible boon for biological research. As our understanding of CRISPR technology increases, so does our awareness of the off-target editing events carried out by Cas9 nuclease. Existing CRISPR guide RNA design tools, such as CRISPR Design (MIT [1]) and CCTop (CRISPR-Cas9 Target online predictor, Heidelberg University [2]), attempt to predict off-target editing of the guide RNAs. However, these tools are limited by an incomplete understanding of the factors that cause Cas9 to cleave at incorrect sites.

Attempts have also been made to improve the specificity of the Cas9 nuclease, but these rationally designed variants have come at some cost to the on-target performance of Cas9 [3–4]. Dr Christopher Vakulskas, a research scientist at IDT, has taken a different approach to evolve the Cas9 nuclease into a more precise genome editing tool. In the recorded webinar, Reducing off-target events in CRISPR genome editing applications with a novel, high-fidelity Cas9 nuclease, Dr Vakulskas gives an overview of how off-target editing is predicted and identified, and provides approaches you can use to improve targeting accuracy. The presentation sets the stage for a discussion about the development and use of a new high-fidelity Cas9 nuclease, Alt-R® S.p. HiFi Cas9 Nuclease 3NLS, that significantly reduces off-target editing, without sacrificing on-target performance. If you are performing genome editing experiments and you are concerned about specificity, watch the recording below to learn more.

Watch the presentation