Spotting off-targets from gene editing: Unintended genomic modifications limit the potential therapeutic use of gene-editing tools. Available methods to find off-targets generally do not work in vivo or detect single-nucleotide changes. Three papers in this issue report new methods for monitoring gene-editing tools in vivo (see the Perspective by Kempton and Qi). Wienert et al. followed the recruitment of a DNA repair protein to DNA breaks induced by CRISPR-Cas9, enabling unbiased detection of off-target editing in cellular and animal models. Zuo et al. identified off-targets without the interference of natural genetic heterogeneity by injecting base editors into one blastomere of a two-cell mouse embryo and leaving the other genetically identical blastomere unedited. Jin et al. performed whole-genome sequencing on individual, genome-edited rice plants to identify unintended mutations. Cytosine, but not adenine, base editors induced numerous single-nucleotide variants in both mouse and rice. Science , this issue p. 286 , p. 289 , p. 292 ; see also p. 234

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites.