Through fusing CRISPR-Cas9 nickases with cytidine or adenine deaminases, a new paradigm-shifting class of genome-editing technology, termed "base editors," has recently been developed. Base editors mediate highly efficient, targeted single-base conversion without introducing double-stranded breaks. Analysis of base editing outcomes typically relies on imprecise enzymatic mismatch cleavage assays, time-consuming single-colony sequencing, or expensive next-generation deep sequencing. To overcome these limitations, several groups have recently developed computer programs to measure base-editing efficiency from fluorescence-based Sanger sequencing data such as Edit deconvolution by inference of traces in R (EditR), TIDER, and ICE. These approaches have greatly simplified the quantitation of base-editing experiments. However, the current Sanger sequencing tools lack the capability of batch analysis and producing high-quality images for publication. Here, we provide a base editing analysis tool (BEAT) written in Python to analyze and quantify the base-editing events from Sanger sequencing data in a batch manner, which can also produce intuitive, publication-ready base-editing images.

中文翻译：

---

BEAT：一个Python程序，用于从Sanger测序中量化基础编辑。

通过将CRISPR-Cas9切口酶与胞苷或腺嘌呤脱氨酶融合，最近开发了一种新的范式转移类基因组编辑技术，称为“基础编辑器”。基本编辑器可以在不引入双链中断的情况下，以高效，有针对性的单基转换为中介。基本编辑结果的分析通常依赖于不精确的酶促错配切割测定，费时的单菌落测序或昂贵的下一代深度测序。为了克服这些限制，最近有几个小组开发了计算机程序，用于通过基于R（EditR），TIDER和ICE的迹线推断，从基于荧光的Sanger测序数据（例如编辑反卷积）中测量碱基编辑效率。这些方法大大简化了基础编辑实验的定量。然而，当前的Sanger测序工具缺乏批量分析和生成高质量图像以供发布的功能。在这里，我们提供了一个用Python编写的基础编辑分析工具（BEAT），用于以批处理方式分析和量化Sanger测序数据中的基础编辑事件，它还可以生成直观的，可发布的基础编辑图像。