Genetic Perturbation Platform, Broad Institute of MIT and Harvard, 75 Ames St, Cambridge, MA, USA.
Arbor Biotechnologies, Cambridge, MA, USA.
Nat Commun. 2022 Sep 6;13(1):5255. doi: 10.1038/s41467-022-33024-2.
CRISPR technology is a powerful tool for studying genome function. To aid in picking sgRNAs that have maximal efficacy against a target of interest from many possible options, several groups have developed models that predict sgRNA on-target activity. Although multiple tracrRNA variants are commonly used for screening, no existing models account for this feature when nominating sgRNAs. Here we develop an on-target model, Rule Set 3, that makes optimal predictions for multiple tracrRNA variants. We validate Rule Set 3 on a new dataset of sgRNAs tiling essential and non-essential genes, demonstrating substantial improvement over prior prediction models. By analyzing the differences in sgRNA activity between tracrRNA variants, we show that Pol III transcription termination is a strong determinant of sgRNA activity. We expect these results to improve the performance of CRISPR screening and inform future research on tracrRNA engineering and sgRNA modeling.
CRISPR 技术是研究基因组功能的强大工具。为了帮助从众多可能的选择中挑选出针对目标具有最大功效的 sgRNA，有几个小组开发了预测 sgRNA 靶活性的模型。尽管通常使用多种 tracrRNA 变体进行筛选，但在提名 sgRNA 时，没有现有的模型考虑到这一特性。在这里，我们开发了一种针对靶的模型，规则集 3，该模型可以对多种 tracrRNA 变体进行最佳预测。我们在一个新的 sgRNA 靶向必需和非必需基因的数据集上验证了 Rule Set 3，该模型在之前的预测模型基础上有了显著的改进。通过分析 tracrRNA 变体之间 sgRNA 活性的差异，我们表明 Pol III 转录终止是 sgRNA 活性的一个重要决定因素。我们希望这些结果能够提高 CRISPR 筛选的性能，并为未来的 tracrRNA 工程和 sgRNA 建模研究提供信息。
