Off-target effects of the CRISPR–Cas9 system can lead to suboptimal gene-editing outcomes and are a bottleneck in its development. Here, we introduce two interdependent machine-learning models for the prediction of off-target effects of CRISPR–Cas9. The approach, which we named Elevation, scores individual guide–target pairs, and also aggregates them into a single, overall summary guide score. We demonstrate that Elevation consistently outperforms competing approaches on both tasks. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases. Because of the large-scale and computational demands of the prediction of off-target activities, we have developed a fast cloud-based service (https://crispr.ml) for end-to-end guide-RNA design. The service makes use of pre-computed on-target and off-target activity prediction for every genic region in the human genome.

CRISPR–Cas9系统的脱靶效应可能导致基因编辑结果欠佳，并且是其发展的瓶颈。在这里，我们介绍了两个相互依赖的机器学习模型，用于预测CRISPR–Cas9的脱靶效应。我们将这种方法称为“高程”，它对单个指导目标对进行评分，并将它们汇总为一个整体的总体指导分数。我们证明，在两项任务中，海拔高度始终优于竞争方法。我们还介绍了一种评估方法，该方法可以平衡有效指南和无效指南之间的错误，从而封装了一系列实际用例。由于预测脱靶活动的大规模和计算需求，我们为端到端指南RNA设计开发了基于云的快速服务（https://crispr.ml）。