BEAT: A Python Program to Quantify Base Editing from Sanger Sequencing

Abstract

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.