{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Veerappa AM"],"funding":["NIGMS NIH HHS","NOSI supplement to the parent IDeA Networks of Biomedical Research Excellence (INBRE) Program"],"pagination":["bbae090"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11264300"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(Supplement_1)"],"pubmed_abstract":["Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore"],"journal":["Briefings in bioinformatics"],"pubmed_title":["CloudATAC: a cloud-based framework for ATAC-Seq data analysis."],"pmcid":["PMC11264300"],"funding_grant_id":["P20 GM103427","NIH/NIGMS P20 GM103427"],"pubmed_authors":["Rowley MJ","Kim A","Sethi S","Veerappa AM","Beaudry L","Guda C","Maggio A","Hawkins D","Sorgen PL"],"additional_accession":[]},"is_claimable":false,"name":"CloudATAC: a cloud-based framework for ATAC-Seq data analysis.","description":"Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-06-01T01:15:29.654Z","creation":"2025-06-01T01:15:29.654Z"},"accession":"S-EPMC11264300","cross_references":{"pubmed":["39041910"],"doi":["10.1093/bib/bbae090"]}}