Improved sensitivity and resolution of ATAC-seq differential DNA accessibility analysis

Eukaryotic genomes are packaged into chromatin, and the extent of its compaction must be modulated to allow several biological processes such as gene transcription. The regulatory elements of expressed genes are typically in relatively accessible chromatin, and several studies have revealed a reliable correlation between the abundance of mRNA transcripts and the degree of DNA accessibility at the regulatory elements of their coding genes. In consequence, the genome-wide profiling of DNA accessibility by methods such as ATAC-seq can help in the study of gene regulatory networks by serving as a proxy for gene expression and by helping identify important gene cis-regulatory elements and the trans-acting factors that bind them. The predominant approach used to identify differentially accessible genomic loci from ATAC-seq data obtained in two conditions of interest is comparable to that employed in RNA-seq gene expression profiling studies: accessible regions are identified through peak calling and treated like “genes”, then sequenced DNA fragments (originating from two neighboring transposase insertion events) that overlap them are counted and subjected to abundance modeling, which then allows to identify those that have a significant difference between the two conditions. We reasoned that this approach could be improved in terms of sensitivity and resolution by introducing two changes: bypassing peak calling, using instead a genome-wide sliding window quantification approach, and counting transposase insertion sites, instead of fragments originating from two neighboring insertion sites. We present the development of this approach, which we term “widaR”, for Window- and Insertion-based Differential Accessibility in R, using a murine skeletal myoblast differentiation dataset. Reproducible R code is provided.