Arazi School of Computer Science, Interdisciplinary Center, Herzliya, Israel.
The Institute for Advanced Materials and Nanotechnology, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
Nat Commun. 2021 May 24;12(1):3042. doi: 10.1038/s41467-021-22417-4.
Controlling off-target editing activity is one of the central challenges in making CRISPR technology accurate and applicable in medical practice. Current algorithms for analyzing off-target activity do not provide statistical quantification, are not sufficiently sensitive in separating signal from noise in experiments with low editing rates, and do not address the detection of translocations. Here we present CRISPECTOR, a software tool that supports the detection and quantification of on- and off-target genome-editing activity from NGS data using paired treatment/control CRISPR experiments. In particular, CRISPECTOR facilitates the statistical analysis of NGS data from multiplex-PCR comparative experiments to detect and quantify adverse translocation events. We validate the observed results and show independent evidence of the occurrence of translocations in human cell lines, after genome editing. Our methodology is based on a statistical model comparison approach leading to better false-negative rates in sites with weak yet significant off-target activity.
控制脱靶编辑活性是使 CRISPR 技术在医学实践中准确和适用的核心挑战之一。目前用于分析脱靶活性的算法没有提供统计量化，在编辑率低的实验中不足以从信号中分离噪声，并且不能解决易位的检测问题。在这里，我们介绍了 CRISPECTOR，这是一种软件工具，可使用配对的处理/对照 CRISPR 实验从 NGS 数据中支持检测和定量的靶和脱靶基因组编辑活性。特别是，CRISPECTOR 便于对多重 PCR 比较实验的 NGS 数据进行统计分析，以检测和定量不良易位事件。我们验证了观察到的结果，并在基因组编辑后显示了人细胞系中转位发生的独立证据。我们的方法基于统计模型比较方法，导致在弱但具有显著脱靶活性的位点具有更好的假阴性率。
