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Multigene editing: current approaches and beyond

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 5, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa396

Keywords

CRISPR/Cas9; multigene editing; off-target editing; preference cutting score

Funding

  1. Australian Research Council [ARC DP180100120]

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CRISPR/Cas9 multigene editing is a widely studied topic in biomedicine and biology, requiring optimization of sgRNA selection to minimize off-target editing effects. While off-target editing is unavoidable, some sgRNAs may turn into on-target editing sites for other genes of interest, demonstrating the potential benefits of preference cutting score evaluation.
CRISPR/Cas9 multigene editing is an active and widely studied topic in the fields of biomedicine and biology. It involves a simultaneous participation of multiple single-guide RNAs (sgRNAs) to edit multiple target genes in a way that each gene is edited by one of these sgRNAs. There are possibly numerous sgRNA candidates capable of on-target editing on each of these genes with various efficiencies. Meanwhile, each of these sgRNA candidates may cause unwanted off-target editing at many other genes. Therefore, selection optimization of these multiple sgRNAs is demanded so as to minimize the number of sgRNAs and thus reduce the collective negative effects caused by the off-target editing. This survey reviews wet-laboratory approaches to the implementation of multigene editing and their needs of computational tools for better design. We found that though off-target editing is unavoidable during the gene editing, those disfavored cuttings by some target genes' sgRNAs can potentially become on-target editing sites for some other genes of interests. This off-to-on role conversion is beneficial to optimize the sgRNA selection in multigene editing. We present a preference cutting score to assess those beneficial off-target cutting sites, which have a few mismatches with their host genes' on-target editing sites. These potential sgRNAs can be prioritized for recommendation via ranking their on-target average cutting efficiency, the total off-target site number and their average preference cutting score. We also present case studies on cancer-associated genes to demonstrate tremendous usefulness of the new method.

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