{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T08:28:26Z","timestamp":1775377706161,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2050919"],"award-info":[{"award-number":["2050919"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Committee on Research and Creative Works (CRCW) Seed Grant funding from the University of Colorado"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but provide greater insight into the intricate details of 3D chromatin structures such as enhancer\u2013promoter interactions and sub-domains. To provide a cost-effective solution to high-resolution Hi-C data collection, deep learning models are used to predict high-resolution Hi-C matrices from existing low-resolution matrices across multiple cell types.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we present two Cascading Residual Networks called HiCARN-1 and HiCARN-2, a convolutional neural network and a generative adversarial network, that use a novel framework of cascading connections throughout the network for Hi-C contact matrix prediction from low-resolution data. Shown by image evaluation and Hi-C reproducibility metrics, both HiCARN models, overall, outperform state-of-the-art Hi-C resolution enhancement algorithms in predictive accuracy for both human and mouse 1\/16, 1\/32, 1\/64 and 1\/100 downsampled high-resolution Hi-C data. Also, validation by extracting topologically associating domains, chromosome 3D structure and chromatin loop predictions from the enhanced data shows that HiCARN can proficiently reconstruct biologically significant regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>HiCARN can be accessed and utilized as an open-sourced software at: https:\/\/github.com\/OluwadareLab\/HiCARN and is also available as a containerized application that can be run on any platform.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac156","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:11:29Z","timestamp":1646943089000},"page":"2414-2421","source":"Crossref","is-referenced-by-count":26,"title":["HiCARN: resolution enhancement of Hi-C data using cascading residual networks"],"prefix":"10.1093","volume":"38","author":[{"given":"Parker","family":"Hicks","sequence":"first","affiliation":[{"name":"Department of Biology, Concordia University Irvine , Irvine, CA 92612, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-2342","authenticated-orcid":false,"given":"Oluwatosin","family":"Oluwadare","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Colorado , Colorado Springs, CO 80918, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"2023041402564359300_","author":"Ahn","year":"2018"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41588-019-0561-1","article-title":"On the existence and functionality of topologically associating domains","volume":"52","author":"Beagan","year":"2020","journal-title":"Nat. Genet"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1093\/bioinformatics\/bty1059","article-title":"Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps","volume":"35","author":"Carron","year":"2019","journal-title":"Bioinformatics"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1038\/nature12753","article-title":"Topology of mammalian developmental enhancers and their regulatory landscapes","volume":"502","author":"De Laat","year":"2013","journal-title":"Nature"},{"key":"2023041402564359300_","author":"Dimmick","year":"2020"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.molcel.2016.05.018","article-title":"Chromatin domains: the unit of chromosome organization","volume":"62","author":"Dixon","year":"2016","journal-title":"Mol. Cell"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-88115-9","article-title":"VEHiCLE: a variationally encoded Hi-C loss enhancement algorithm for improving and generating Hi-C data","volume":"11","author":"Highsmith","year":"2021","journal-title":"Sci. Rep"},{"key":"2023041402564359300_","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":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3389\/fgene.2020.00353","article-title":"SRHiC: a deep learning model to enhance the resolution of Hi-C data","volume":"11","author":"Li","year":"2020","journal-title":"Front. Genet"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1126\/science.1181369","article-title":"Comprehensive mapping of long-range interactions reveals folding principles of the human genome","volume":"326","author":"Lieberman-Aiden","year":"2009","journal-title":"Science"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"4222","DOI":"10.1093\/bioinformatics\/btz251","article-title":"HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data","volume":"35","author":"Liu","year":"2019","journal-title":"Bioinformatics"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"862","DOI":"10.3390\/genes10110862","article-title":"HiCNN2: enhancing the resolution of Hi-C data using an ensemble of convolutional neural networks","volume":"10","author":"Liu","year":"2019","journal-title":"Genes"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"i99","DOI":"10.1093\/bioinformatics\/btz317","article-title":"hicGAN infers super resolution Hi-C data with generative adversarial networks","volume":"35","author":"Liu","year":"2019","journal-title":"Bioinformatics"},{"key":"2023041402564359300_","author":"Liu","year":"2021"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1146\/annurev-genom-083115-022339","article-title":"CTCF and cohesin in genome folding and transcriptional gene regulation","volume":"17","author":"Merkenschlager","year":"2016","journal-title":"Annu. Rev. Genomics Hum. Genet"},{"key":"2023041402564359300_"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12575-019-0094-0","article-title":"An overview of methods for reconstructing 3-D chromosome and genome structures from Hi-C data","volume":"21","author":"Oluwadare","year":"2019","journal-title":"Biol. Procedures Online"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.cell.2014.11.021","article-title":"A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping","volume":"159","author":"Rao","year":"2014","journal-title":"Cell"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.cell.2017.09.026","article-title":"Cohesin loss eliminates all loop domains","volume":"171","author":"Rao","year":"2017","journal-title":"Cell"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"e70","DOI":"10.1093\/nar\/gkv1505","article-title":"TopDom: an efficient and deterministic method for identifying topological domains in genomes","volume":"44","author":"Shin","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1093\/bioinformatics\/bty164","article-title":"GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs","volume":"34","author":"Ursu","year":"2018","journal-title":"Bioinformatics"},{"key":"2023041402564359300_"},{"key":"2023041402564359300_","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1101\/gr.220640.117","article-title":"HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient","volume":"27","author":"Yang","year":"2017","journal-title":"Genome Res"},{"key":"2023041402564359300_","first-page":"1","article-title":"Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus","volume":"9","author":"Zhang","year":"2018","journal-title":"Nat. Commun"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac156\/43222765\/btac156.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/9\/2414\/49874316\/btac156.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/9\/2414\/49874316\/btac156.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T17:58:40Z","timestamp":1700330320000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/9\/2414\/6547054"}},"subtitle":[],"editor":[{"given":"Peter","family":"Robinson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":24,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,4,28]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac156","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,5,1]]},"published":{"date-parts":[[2022,3,11]]}}}