Detailed profiling with MaChIAto reveals various genomic and epigenomic features affecting the efficacy of knock-out, short homology-based knock-in and Prime Editing
Detailed profiling with MaChIAto reveals various genomic and epigenomic features affecting the efficacy of knock-out, short homology-based knock-in and Prime Editing
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DOI：
10.1101/2022.06.27.496697
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Kazuki Nakamae;Mitsumasa Takenaga;Shota Nakade;A. Awazu;N. Sakamoto;Takashi Yamamoto;Tetsushi Sakuma
中科院分区：
文献类型：
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作者：
Kazuki Nakamae;Mitsumasa Takenaga;Shota Nakade;A. Awazu;N. Sakamoto;Takashi Yamamoto;Tetsushi Sakuma
通过利用CRISPR-Cas9及其先进技术（如Prime Editor）实现了高效的基因敲除和敲入。此外，各种生物信息学资源已可用于量化和定性CRISPR编辑的效率和准确性，这显著提高了基因组编辑背景下通用下一代测序（NGS）分析的用户友好性。然而，目前还没有专门的集成软件来研究使用CRISPR-Cas9及其他技术进行基因组编辑的效率和准确性所涉及的基因组背景中的偏好。在此，我们通过建立NGS数据的新型分析平台来解决该问题，该平台用于分析无模板敲除和基于短同源性的编辑的结果，称为MaChIAto（微同源性相关染色体整合/编辑分析工具）（https：//github.com/KazukiNakamae/MaChIAto）。MaChIAto适应NGS读段的分类和分析，以揭示相应的基因组编辑方法的趋势。在剖析功能中，MaChIAto可以沿着编辑效率总结突变模式，以及> 70种特征分析，例如，与热力学和二级结构参数的相关性分析。此外，MaChIAto的分类功能是基于，但比现有的工具，这是通过实现一种新的方法，利用贝叶斯优化参数自适应更严格。为了证明MaChIAto的功能，我们分析了敲除、基于短同源性的敲入和Prime编辑的NGS数据。我们证实，（表）基因组背景的一些功能影响的效率和准确性。这些结果表明，MaChIAto是理解CRISPR编辑的最佳设计的有用工具。更重要的是，它是发现基于短同源性的敲入结果中的特征的第一个工具。MaChIAto将帮助研究人员分析编辑数据，并为CRISPR编辑生成预测模型，进一步有助于揭示产生各种CRISPR和Prime Editing结果的“黑匣子”过程。
Highly efficient gene knock-out and knock-in have been achieved by harnessing CRISPR-Cas9 and its advanced technologies such as Prime Editor. In addition, various bioinformatics resources have become available to quantify and qualify the efficiency and accuracy of CRISPR edits, which significantly increased the user-friendliness of the general next-generation sequencing (NGS) analysis in the context of genome editing. However, there is no specialized and integrated software for investigating the preference in the genomic context involved in the efficiency and accuracy of genome editing using CRISPR-Cas9 and beyond. Here, we address this issue by establishing a novel analysis platform of NGS data for profiling the outcome of template-free knock- out and short homology-based editing, named MaChIAto (Microhomology- associated Chromosomal Integration/editing Analysis tools) (https://github.com/KazukiNakamae/MaChIAto). MaChIAto accommodates the classification and profiling of the NGS reads to uncover the tendency of the corresponding method of genome editing. In the profiling function, MaChIAto can summarize the mutation patterns along with the editing efficiency, and > 70 kinds of feature analysis, e.g., correlation analysis with thermodynamics and secondary structure parameters, are available. Additionally, the classifying function of MaChIAto is based on, but much stricter than, that of the existing tool, which is achieved by implementing a novel method of parameter adaptation utilizing Bayesian optimization. To demonstrate the functionality of MaChIAto, we analyzed the NGS data of knock- out, short homology-based knock-in, and Prime Editing. We confirmed that some features of (epi-)genomic context affected the efficiency and accuracy. These results show that MaChIAto is a helpful tool for understanding the best design for CRISPR edits. More importantly, it is the first tool for discovering features in the short homology-based knock-in outcomes. MaChIAto would help researchers profile editing data and generate prediction models for CRISPR edits, further contributing to revealing a “black box” process to produce a variety of CRISPR and Prime Editing outcomes.