School of Engineering Science, Kochi University of Technology, Kami 782-8502, Japan.
Cells. 2024 Jan 30;13(3):261. doi: 10.3390/cells13030261.
Successful genome editing depends on the cleavage efficiency of programmable nucleases (PNs) such as the CRISPR-Cas system. Various methods have been developed to assess the efficiency of PNs, most of which estimate the occurrence of indels caused by PN-induced double-strand breaks. In these methods, PN genomic target sites are amplified through PCR, and the resulting PCR products are subsequently analyzed using Sanger sequencing, high-throughput sequencing, or mismatch detection assays. Among these methods, Sanger sequencing of PCR products followed by indel analysis using online web tools has gained popularity due to its user-friendly nature. This approach estimates indel frequencies by computationally analyzing sequencing trace data. However, the accuracy of these computational tools remains uncertain. In this study, we compared the performance of four web tools, TIDE, ICE, DECODR, and SeqScreener, using artificial sequencing templates with predetermined indels. Our results demonstrated that these tools were able to estimate indel frequency with acceptable accuracy when the indels were simple and contained only a few base changes. However, the estimated values became more variable among the tools when the sequencing templates contained more complex indels or knock-in sequences. Moreover, although these tools effectively estimated the net indel sizes, their capability to deconvolute indel sequences exhibited variability with certain limitations. These findings underscore the importance of judiciously selecting and using an appropriate tool with caution, depending on the type of genome editing being performed.
成功的基因组编辑依赖于可编程核酸酶(PNs)的切割效率,如 CRISPR-Cas 系统。已经开发了各种方法来评估 PNs 的效率,其中大多数方法估计 PN 诱导的双链断裂引起的插入缺失的发生。在这些方法中,通过 PCR 扩增 PN 基因组靶位点,然后使用 Sanger 测序、高通量测序或错配检测分析来分析所得的 PCR 产物。在这些方法中,由于其用户友好的性质,使用在线网络工具对 PCR 产物进行 Sanger 测序并进行插入缺失分析的方法已经流行起来。该方法通过计算分析测序跟踪数据来估计插入缺失频率。然而,这些计算工具的准确性仍然不确定。在这项研究中,我们使用具有预定插入缺失的人工测序模板比较了四个网络工具(TIDE、ICE、DECODR 和 SeqScreener)的性能。我们的结果表明,当插入缺失简单且仅包含少数碱基变化时,这些工具能够以可接受的准确性估计插入缺失频率。然而,当测序模板包含更复杂的插入缺失或敲入序列时,估计值在工具之间变得更加多变。此外,尽管这些工具有效地估计了净插入缺失大小,但它们对插入缺失序列的去卷积能力存在一定的局限性,表现出变异性。这些发现强调了根据所进行的基因组编辑类型,谨慎选择和使用适当工具的重要性。