{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T20:40:50Z","timestamp":1789332050501,"version":"build-2803163510"},"reference-count":87,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFA1100501"],"award-info":[{"award-number":["2021YFA1100501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Project of Guangzhou National Laboratory","award":["GZNL2024A01003"],"award-info":[{"award-number":["GZNL2024A01003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32370690"],"award-info":[{"award-number":["32370690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Enhancers play an important role in transcriptional regulation by modulating gene expression from distal genomic locations. Although single-cell ATAC and RNA sequencing (scATAC\/RNA-seq) data have been leveraged to infer enhancer\u2013gene links, establishing regulatory links between enhancers and their target genes remains a challenge due to the absence of chromatin conformation information. Here, we present SCEG-HiC, a machine learning method based on weighted graphical lasso, which decodes enhancer\u2013gene links from single-cell multi-omics data by integrating bulk average Hi-C as prior knowledge. SCEG-HiC supports both paired scATAC\/RNA-seq and scATAC-only inputs, improving prediction accuracy while retaining context-specific correlations and enabling the discovery of biologically relevant links. Comprehensive evaluation across 10 human and mouse single-cell multi-omics datasets shows that SCEG-HiC outperforms existing single-cell models. Application of SCEG-HiC to COVID-19 datasets illustrates its capacity to more reliably reconstruct gene regulatory networks underlying disease severity, and elucidate functional associations between noncoding variants and their putative target genes. SCEG-HiC is freely available as an open-source and user-friendly R package, facilitating broad applications in regulatory genomics research.<\/jats:p>","DOI":"10.1093\/nar\/gkag437","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T11:41:56Z","timestamp":1776944516000},"source":"Crossref","is-referenced-by-count":1,"title":["Predicting enhancer\u2013gene links from single-cell multi-omics data by integrating prior Hi-C information"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6717-5039","authenticated-orcid":false,"given":"Xuan","family":"Liang","sequence":"first","affiliation":[{"name":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Miao","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1115-5219","authenticated-orcid":false,"given":"Dongmei","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou 310024 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yurun","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8108-627X","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou 310024 ,","place":["China"]},{"name":"Yuhang Institute of Medical Science Innovation and Transformation , Hangzhou 311100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"2026060301113059300_B1","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.3390\/ijms20061281","article-title":"Transcriptional regulation: molecules, involved mechanisms, and 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