Study Identifies Drivers of Efficient, Precise Genome Editing and Hidden DNA Repair

Experts at the Max Planck Institute for Evolutionary Anthropology provided an answer to a prevailing question about CRISPR genome editing: How to accurately predict guide RNA (gRNA) activity?

Compared to previous tools that were trained to use transcribed gRNAs and prone to transcription biases, the new approach focuses on chemically synthesized gRNAs, which are often used in research. The researchers developed a simple linear model, which they called the EVA score. It can robustly predict gRNA activity across cell types and datasets.

“Chemical synthesis avoids some of the sequence-related pitfalls of transcribed gRNAs,” says Stephan Riesenberg, who led the research. “This difference, coupled with a more accurate quantification of cellular CRISPR cleavage outcomes, enabled us to build a simple prediction model that generalizes across cell types “. The authors have pre-calculated the EVA scores for all gRNAs in the human and mouse genomes, which are accessible using online genome browser tracks.

Aside from gRNA activity prediction, the research also provides a new model for homology-directed repair (HDR) efficiency, which is vital in precise genome editing tasks, including correcting point mutations. The researchers are able to identify sequence features that lead to HDR success, such as donor misfolding and the type of nucleotide change, enabling HDR efficiencies of up to 78% under optimal conditions.

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