Preprocessing and computational analysis of single-cell epigenomic datasets

Abstract

Recent technological developments have enabled the characterization of the epigenetic landscape of single cells across a range of tissues in normal and diseased states and under various biological and chemical perturbations. While analysis of these profiles resembles methods from single-cell transcriptomic studies, unique challenges are associated with bioinformatics processing of single-cell epigenetic data, including a much larger (10–1,000×) feature set and significantly greater sparsity, requiring customized solutions. Here, we discuss the essentials of the computational methodology required for analyzing common single-cell epigenomic measurements for DNA methylation using bisulfite sequencing and open chromatin using ATAC-Seq.