{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Han SK"],"funding":["HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases","NIDDK NIH HHS","NHLBI NIH HHS","HHS | NIH | National Heart, Lung, and Blood Institute"],"pagination":["e2212810119"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9907136"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["119(51)"],"pubmed_abstract":["Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify \"high-quality\" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data."],"journal":["Proceedings of the National Academy of Sciences of the United States of America"],"pubmed_title":["Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model."],"pmcid":["PMC9907136"],"funding_grant_id":["R01 HL141980","HL086694","R01 DK119380","RC2 DK122397","HL141980","RC2DK122397","K08 DK126847","R01 HL086694","RO1DK119380"],"pubmed_authors":["Muto Y","Wilson PC","Chakravarti A","Humphreys BD","Han SK","Sampson MG","Lee D"],"additional_accession":[]},"is_claimable":false,"name":"Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model.","description":"Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify \"high-quality\" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-06-25T03:12:27.273Z","creation":"2025-04-05T19:39:29.937Z"},"accession":"S-EPMC9907136","cross_references":{"pubmed":["36508674"],"doi":["10.1073/pnas.2212810119"]}}