Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
复制标题
通过深度学习优化两种高保真 Cas9 变体的 CRISPR 引导 RNA 设计
DOI：
10.1038/s41467-019-12281-8
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发表时间：
2019-09-19
影响因子：
16.6
通讯作者：
Wang, Yongming
中科院分区：
文献类型：
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作者：
Wang, Daqi;Zhang, Chengdong;Wang, Yongming
从SpCas9衍生的高度特异的Cas9核酸酶是基因组编辑的宝贵工具，但由于缺乏管理引导RNA(GRNA)活性的知识，它们的广泛应用受到阻碍。在这里，我们进行了基因组规模的筛选，以测量两个高度特异的SpCas9变异体(eSpCas9(1.1)和SpCas9-HF1)和野生型SpCas9(WT-SpCas9)在人类细胞中的gRNA活性，并获得了每个核酸酶超过50,000 gRNA的插入速率，覆盖了大约20,000个基因。我们评估了1,031个特征对gRNA活性的贡献，并开发了活性预测模型。我们的数据显示，RNN与重要的生物学特征相结合，在活性预测方面的表现优于其他模型。我们进一步证明了我们的模型比其他流行的gRNA设计工具性能更好。最后，我们开发了一个针对这三个Cas9核酸酶的在线设计工具DeepHF。数据库和设计器工具都可以通过Web服务器http://www.DeepHF.com/.免费访问
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering similar to 20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/.