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可编程核酸酶脱靶编辑的实验和计算分析工具。

Tools for experimental and computational analyses of off-target editing by programmable nucleases.

机构信息

ILISATech, Houston, TX, USA.

Arsenal Biosciences, South San Francisco, CA, USA.

出版信息

Nat Protoc. 2021 Jan;16(1):10-26. doi: 10.1038/s41596-020-00431-y. Epub 2020 Dec 7.

Abstract

Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.

摘要

基因组编辑技术利用可编程核酸酶正在彻底改变生命科学和医学领域。这些核酸酶的脱靶编辑仍然是一个相当大的关注点,尤其是在治疗应用中。本文综述了用于鉴定潜在脱靶编辑位点的工具,并比较了这些工具正确分析脱靶效应的能力。最近在脱靶分析的计算和实验工具方面的进展,对于具有高脱靶编辑活性的位点产生了非常一致的结果。然而,没有单一的工具能够准确预测低频脱靶编辑,这是治疗性基因组编辑的一个瓶颈,因为即使只有少数细胞发生脱靶编辑,也可能造成损害。因此,我们建议至少应同时使用一种计算工具和一种实验工具来鉴定潜在的脱靶位点,并应使用基于扩增子的下一代测序(NGS)作为评估候选位点真实脱靶效应的金标准检测方法。未来改进脱靶分析的工作包括扩展真实脱靶编辑数据集,以评估新的实验技术和训练机器学习算法;使用所研究细胞的特定基因组而不是参考基因组进行分析;以及应用新型 NGS 技术来提高基于扩增子的脱靶编辑定量的灵敏度。

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