{"doi":"10.1101/2025.05.06.650874","title":"Genome structure mapping with high-resolution 3D genomics and deep learning","abstract":"Gene expression is often regulated by distal enhancers through cell-type-specific 3D looping interactions, but comprehensive mapping of these interactions across cell types is experimentally intractable. To address this gap, we introduce an integrated approach where we generate ultra-deep Region Capture Micro-C (RCMC) and Micro-C data specifically designed for state-of-the-art deep learning architectures. We developed Cleopatra, an attention-based deep learning model that takes epigenomic inputs and is pre-trained on genome-wide Micro-C data followed by fine-tuning with high-resolution RCMC data. Cleopatra accurately predicts 3D maps at sub-kilobase bin sizes and unprecedented resolution, enabling us to generate ultra-high-resolution, genome-wide 3D contact maps across four human cell types. These maps revealed cell-type-specific microcompartments and over 900,000 loops across the cell types, about half of which are cell-type-specific. Using Cleopatra maps, we observe that promoters form about a dozen loops on average, and that expression increases monotonically with the number of loops, indicating that looping is associated with higher gene expression. We further show the enhancer-promoter loops are often anchored by CTCF, and nominate new transcription factors that may regulate cell-type-specific enhancer-promoter interactions. Overall, we establish a framework for ultra-high-resolution 3D genome mapping, providing a broadly applicable resource for gaining new insights into cell-type-specific gene regulation.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":554734,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5117,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":565648,"name":"Fan Feng","orcid":"0000-0002-5990-312X","position":1,"is_corresponding":false},{"id":1131808,"name":"V. D. Ramanathan","orcid":"0009-0001-6914-6484","position":2,"is_corresponding":false},{"id":277585,"name":"Jie Liu","orcid":"0000-0002-9504-0587","position":3,"is_corresponding":false},{"id":109374,"name":"Anders S. Hansen","orcid":"0000-0001-7540-7858","position":4,"is_corresponding":false},{"id":287718,"name":"Clarice KY Hong","orcid":"0000-0002-9485-1425","position":0,"is_corresponding":true}],"reference_count":81,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:54:54.542303Z","pmid":"40654659","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}