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ATAC-seq technology effectively identifies active\n                    <jats:italic>cis<\/jats:italic>\n                    -REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of\n                    <jats:italic>cis<\/jats:italic>\n                    -REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples\/replicates) and accurately predicted known\n                    <jats:italic>cis<\/jats:italic>\n                    -RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain\/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred\n                    <jats:italic>cis<\/jats:italic>\n                    -RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study\n                    <jats:italic>cis<\/jats:italic>\n                    -RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in\n                    <jats:italic>cis<\/jats:italic>\n                    -RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009670","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T13:35:25Z","timestamp":1639402525000},"page":"e1009670","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq 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