Base editors, including adenine base editors (ABEs)¹ and cytosine base editors (CBEs)^2,3, are widely used to induce point mutations. However, determining whether a specific nucleotide in its genomic context can be edited requires time-consuming experiments. Furthermore, when the editable window contains multiple target nucleotides, various genotypic products can be generated. To develop computational tools to predict base-editing efficiency and outcome product frequencies, we first evaluated the efficiencies of an ABE and a CBE and the outcome product frequencies at 13,504 and 14,157 target sequences, respectively, in human cells. We found that there were only modest asymmetric correlations between the activities of the base editors and Cas9 at the same targets. Using deep-learning-based computational modeling, we built tools to predict the efficiencies and outcome frequencies of ABE- and CBE-directed editing at any target sequence, with Pearson correlations ranging from 0.50 to 0.95. These tools and results will facilitate modeling and therapeutic correction of genetic diseases by base editing.

碱基编辑器，包括腺嘌呤碱基编辑器(ABE) ¹和胞嘧啶碱基编辑器(CBE) ^2,3 ，广泛用于诱导点突变。然而，确定基因组背景中的特定核苷酸是否可以被编辑需要耗时的实验。此外，当可编辑窗口包含多个靶核苷酸时，可以生成各种基因型产物。为了开发计算工具来预测碱基编辑效率和结果产物频率，我们首先评估了人类细胞中 ABE 和 CBE 的效率以及分别在 13,504 和 14,157 个靶序列上的结果产物频率。我们发现碱基编辑器和 Cas9 在相同靶标上的活动之间仅存在适度的不对称相关性。使用基于深度学习的计算模型，我们构建了工具来预测任何目标序列上 ABE 和 CBE 定向编辑的效率和结果频率，皮尔逊相关性范围为 0.50 到 0.95。这些工具和结果将有助于通过碱基编辑对遗传疾病进行建模和治疗校正。