ENCODE ATAC-seq bigWig Coverage (149 Samples) for sample-to-sample correlation and heatmap visualization

Public ENCODE ATAC-seq datasets were processed to generate bigWig (bw) files for downstream correlation and heatmap analyses (see https://github.com/CarlosRangel23/ATAC-toolkit). The generation of coverage tracks was specifically performed to enable comparison and correlation between ENCODE cell types and cell types of interest, providing a reference framework that may be useful for downstream analyses and interpretation. To ensure consistency and comparability, all datasets were processed using the same standardized pipeline. All analyses were performed using the UoM_ATAC_processor pipeline, which includes read alignment, quality control, filtering, and signal track generation. The pipeline is available at: https://github.com/jakebutler98/UoM_ATAC_processor Additionally, metadata associated with each analyzed sample has been included to provide contextual information and facilitate interpretation and reuse of the dataset. To ensure traceability and reproducibility, a standardized naming convention was applied to all samples. Sample names were programmatically generated from ENCODE metadata fields and encode key experimental attributes. Each sample name follows the structure: {ExperimentAccession}_{CellTypeCode}_BioRep{BiologicalReplicate}_TechRep{TechnicalReplicate}_{PairedID} Where: ExperimentAccession: ENCODE experiment accession identifier (e.g., ENCSRXXXXXX). CellTypeCode: a 4-digit code extracted from the ENCODE Biosample term ID, representing the cell type. BioRep: biological replicate number. TechRep: technical replicate number. PairedID: identifier linking paired-end reads. For paired-end read 1, this corresponds to the file accession; for read 2, it is derived from the matching paired file. This naming scheme ensures that each file and sample can be uniquely identified while preserving essential experimental metadata, facilitating downstream processing, grouping of replicates, and reproducibility of analyses. All code is in the R script.