Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E & 27 Neapoleos Str, 15341 Athens, Greece.
School of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
Nucleic Acids Res. 2022 Apr 22;50(7):3616-3637. doi: 10.1093/nar/gkac192.
The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR-Cas9 guide design.
簇状规律间隔短回文重复（CRISPR）/CRISPR 相关蛋白 9（Cas9）系统已成为基因编辑的一种成功且有前途的技术。为了促进其有效应用，已经开发了各种计算工具。这些工具可以通过预测切割效率和特异性并排除不理想的靶标来协助研究人员进行向导 RNA（gRNA）设计过程。然而，虽然有许多工具可用，但对其应用场景和性能基准的评估是有限的。此外，最近还探索了一些新的深度学习工具来预测 gRNA 的效率，但尚未对其进行系统评估。在这里，我们讨论与靶标活性问题相关的方法，主要集中在它们利用的特征和计算方法上。此外，我们还在独立数据集上评估了这些工具，并为它们的使用提供了一些建议。最后，我们总结了关于 CRISPR-Cas9 指导设计未来方向的一些挑战和观点。
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