Maximizing CRISPR/Cas9 phenotype penetrance applying predictive modeling of editing outcomes in Xenopus and zebrafish embryos.

CRISPR/Cas9 genome editing has revolutionized functional genomics in vertebrates. However, CRISPR/Cas9 edited F animals too often demonstrate variable phenotypic penetrance due to the mosaic nature of editing outcomes after double strand break (DSB) repair. Even with high efficiency levels of genome editing, phenotypes may be obscured by proportional presence of in-frame mutations that still produce functional protein. Recently, studies in cell culture systems have shown that the nature of CRISPR/Cas9-mediated mutations can be dependent on local sequence context and can be predicted by computational methods. Here, we demonstrate that similar approaches can be used to forecast CRISPR/Cas9 gene editing outcomes in Xenopus tropicalis, Xenopus laevis, and zebrafish. We show that a publicly available neural network previously trained in mouse embryonic stem cell cultures (InDelphi-mESC) is able to accurately predict CRISPR/Cas9 gene editing outcomes in early vertebrate embryos. Our observations can have direct implications for experiment design, allowing the selection of guide RNAs with predicted repair outcome signatures enriched towards frameshift mutations, allowing maximization of CRISPR/Cas9 phenotype penetrance in the F generation.