ATACgraph: Profiling Genome-Wide Chromatin Accessibility From ATAC-seq

Abstract

Assay for transposase-accessible chromatin using sequencing data (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. Most of the current ATAC-seq tools follow chromatin immunoprecipitation sequencing (ChIP-seq) strategies that do not consider ATAC-seq-specific properties. To incorporate specific ATAC-seq quality control and the underlying biology of chromatin accessibility, we developed a bioinformatics software named ATACgraph for analyzing and visualizing ATAC-seq data. ATACgraph profiles accessible chromatin regions and provides ATAC-seq-specific information including definitions of nucleosome-free regions (NFRs) and nucleosome-occupied regions. ATACgraph also allows identification of differentially accessible regions between two ATAC-seq datasets. ATACgraph incorporates the docker image with the Galaxy platform to provide an intuitive user experience via the graphical interface. Without tedious installation processes on a local machine or cloud, users can analyze data through activated websites using pre-designed workflows or customized pipelines composed of ATACgraph modules. Overall, ATACgraph is an effective tool designed for ATAC-seq for biologists with minimal bioinformatics knowledge to analyze chromatin accessibility. ATACgraph can be run on any ATAC-seq data with no limit to specific genomes. As validation, we demonstrated ATACgraph on human genome to showcase its functions for ATAC-seq interpretation. This software is publicly accessible and can be downloaded at https://github.com/RitataLU/ATACgraph.

Genetic Variation in Chromatin State Across Multiple Tissues inDrosophila melanogaster

“…Figure 1C shows fold-enrichment as a function of distance from the TSS for the female brain (S3B Fig, S4B Fig, S5B Fig for the other tissues). The patterns we see largely mirror other studies [52,54,55]. We finally examined average fold enrichment as a function of HOMER annotation type (Figure 1D depicts female brain).…”

“…We observed the same trends for the four other tissues (see S3A Fig,S4A Fig,S5A Fig). This observation is consistent with results from DNase1-HS-seq experiments [50,51] and other ATAC-seq datasets [52,53]. We further examined the distribution of fold enrichment as a function Overall, properties of the ATAC-seq peaks observed for our four target tissues are comparable to those observed in the Drosophila literature [56], giving us confidence that the peaks of this study are robustly inferred.…”

“…We observed the same trends for the four other tissues (see S3A Fig,S4A Fig,S5A Fig). This observation is consistent with results from DNase1-HS-seq experiments [50,51] and other ATAC-seq datasets [52,53]. We further examined the distribution of fold enrichment as a function of distance from transcription start sites (TSSs) for brain peaks, as TSSs often exhibit strong enrichment patterns [54,55].…”

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

“…However, longer the fragment, less likely the region away from the insertion sites is open. This is especially acute when there are long fragments with insertions completely outside the peak/bin of interest 11,12 (cell 1 in Figure 1a ). The two counting strategies can result in discrepancies in downstream analysis.…”

ATAC‐seq exposes differences in chromatin accessibility leading to distinct leaf shapes in mulberry

“…Compared with earlier ChIP‐seq or DNase‐seq methods for detecting chromatin accessibility, ATAC‐seq is faster and easier to perform, does not require cross‐linking, has a higher signal‐to‐noise ratio, and can be performed on a smaller number of cells. ATAC‐seq takes into account transposase‐digested DNA fragments that contain other nucleosome localization information (Lu et al, 2021 ). ATAC‐seq seq is widely used to measure chromatin accessibility and to identify open chromatin regions (OCRs).…”