{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T08:26:21Z","timestamp":1774427181326,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Genome Quebec"},{"name":"Canada and Genome Quebec"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/BlanchetteLab\/RefHiC.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad266","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T08:14:34Z","timestamp":1688112874000},"page":"i386-i393","source":"Crossref","is-referenced-by-count":4,"title":["Reference panel-guided super-resolution inference of Hi-C data"],"prefix":"10.1093","volume":"39","author":[{"given":"Yanlin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, McGill University, Montr\u00e9al , Qu\u00e9bec H3A 0E9, Canada"}]},{"given":"Mathieu","family":"Blanchette","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University, Montr\u00e9al , Qu\u00e9bec H3A 0E9, Canada"}]}],"member":"286","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"2023063008142075200_btad266-B1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1093\/bioinformatics\/btz540","article-title":"Cooler: scalable storage for Hi-C data and other genomically labeled arrays","volume":"36","author":"Abdennur","year":"2020","journal-title":"Bioinformatics"},{"key":"2023063008142075200_btad266-B2","author":"Borgeaud"},{"key":"2023063008142075200_btad266-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1913-y","article-title":"HIFI: estimating DNA-DNA interaction frequency from Hi-c data at restriction-fragment resolution","volume":"21","author":"Cameron","year":"2020","journal-title":"Genome Biol"},{"key":"2023063008142075200_btad266-B4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","author":"ENCODE Project Consortium","year":"2012","journal-title":"Nature"},{"key":"2023063008142075200_btad266-B5","author":"Dali","year":"2018"},{"key":"2023063008142075200_btad266-B6","author":"Gao"},{"key":"2023063008142075200_btad266-B7","doi-asserted-by":"crossref","first-page":"e1007287","DOI":"10.1371\/journal.pcbi.1007287","article-title":"DeepHiC: A generative adversarial network for enhancing hi-C data resolution","volume":"16","author":"Hong","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2023063008142075200_btad266-B8","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/nmeth.2148","article-title":"Iterative correction of Hi-C data reveals hallmarks of chromosome organization","volume":"9","author":"Imakaev","year":"2012","journal-title":"Nat Methods"},{"key":"2023063008142075200_btad266-B9","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.molcel.2020.03.003","article-title":"Ultrastructural 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