EpiMapper: A new tool for analyzing high-throughput sequencing from CUT&Tag

Since the invention of next-generation sequencing, new methods have been developed to understand the regulation of gene expression through epigenetic markers. Among these, CUT&Tag (Cleavage Under Targets and Tagmentation) analysis has emerged as an efficient epigenomic profiling technique with low input requirements, high sensitivity, and low background signals. Although wet-lab techniques are available, data analysis remains challenging for scientists without expert-level computational skills. Therefore, we developed EpiMapper, a new Python package that simplifies the data analysis of CUT&Tag sequencing and similar techniques, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) or ChIP-seq (chromatin immunoprecipitation [ChIP] assays with sequencing), and allows biomedical scientists to easily interpret the results. This new package includes every necessary step, from quality control to annotation and differential peak analysis. In particular, EpiMapper has improved functionality (e.g., reproducibility assessment) compared to previous analysis protocols and visualization plots and provides new features, such as genome annotation and differential peak analysis. Using three case studies, two on CUT&Tag and one on ATAC-seq data, the EpiMapper Python package successfully reproduced previous results. • We propose EpiMapper – a Python tool for analyzing high-throughput sequencing data. • It is suitable for analyzing sequencing data from CUT&Tag, ATAC-seq, or ChIP-seq. • It includes both low- and high-level sequencing data analysis. • It predicts differential peaks and annotates them to respective genomic regions. • It exports summary reports and visualization figures for each analysis step.