A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org.

CRISPR-Cas13d系统应用中的一个主要挑战是准确预测其依赖向导的靶向和脱靶效应。在这里，我们进行了 CRISPR-Cas13d 增殖筛选，并设计了一个名为 DeepCas13 的深度学习模型，以根据指导序列和二级结构预测靶向活性。 DeepCas13 在预测针对蛋白质编码和非编码 RNA 的导向效率方面优于现有方法。针对非必需基因的指南表现出脱靶活力效应，这与其靶向效率密切相关。在标准化过程中选择适当的阴性对照指南可以减少增殖筛选中相关的假阳性。我们将 DeepCas13 应用到针对 lncRNA 的指南中，并识别出影响多个细胞系中细胞活力和增殖的 lncRNA。通过二次 CRISPR-Cas13d 筛选和定量 RT-PCR 实验广泛证实 DeepCas13 比现有方法具有更高的预测准确性。 DeepCas13 可通过 http://deepcas13.weililab.org 免费访问。