{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:40:38Z","timestamp":1775115638072,"version":"3.50.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T00:00:00Z","timestamp":1554854400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R15GM120650"],"award-info":[{"award-number":["R15GM120650"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006686","name":"University of Miami","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5\u2009kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a new computational method named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions of Hi-C data. The network contains both global and local residual learning with multiple speedup techniques included resulting in fast convergence. We used mean squared errors and Pearson\u2019s correlation coefficients between real high-resolution and computationally predicted high-resolution Hi-C data to evaluate the method. The evaluation results show that HiCNN consistently outperforms HiCPlus, the only existing tool in the literature, when training and testing data are extracted from the same cell type (i.e. GM12878) and from two different cell types in the same or different species (i.e. GM12878 as training with K562 as testing, and GM12878 as training with CH12-LX as testing). We further found that the HiCNN-enhanced high-resolution Hi-C data are more consistent with real experimental high-resolution Hi-C data than HiCPlus-enhanced data in terms of indicating statistically significant interactions. Moreover, HiCNN can efficiently enhance low-resolution Hi-C data, which eventually helps recover two chromatin loops that were confirmed by 3D-FISH.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>HiCNN is freely available at http:\/\/dna.cs.miami.edu\/HiCNN\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz251","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T21:11:36Z","timestamp":1554498696000},"page":"4222-4228","source":"Crossref","is-referenced-by-count":73,"title":["HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data"],"prefix":"10.1093","volume":"35","author":[{"given":"Tong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Miami , Coral Gables, FL, USA"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Miami , Coral Gables, FL, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,4,9]]},"reference":[{"key":"2023062712460730100_btz251-B1","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1101\/gr.160374.113","article-title":"Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts","volume":"24","author":"Ay","year":"2014","journal-title":"Genome Res"},{"key":"2023062712460730100_btz251-B2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.cell.2017.09.043","article-title":"Multiscale 3D genome rewiring during mouse neural development","volume":"171","author":"Bonev","year":"2017","journal-title":"Cell"},{"key":"2023062712460730100_btz251-B3","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1126\/science.1067799","article-title":"Capturing chromosome conformation","volume":"295","author":"Dekker","year":"2002","journal-title":"Science"},{"key":"2023062712460730100_btz251-B4","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/nature11082","article-title":"Topological domains in mammalian genomes identified by analysis of chromatin interactions","volume":"485","author":"Dixon","year":"2012","journal-title":"Nature"},{"key":"2023062712460730100_btz251-B5","first-page":"184","article-title":"Learning a deep convolutional network for image super-resolution","volume-title":"European Conference on Computer Vision","author":"Dong","year":"2014"},{"key":"2023062712460730100_btz251-B6","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1101\/gr.5571506","article-title":"Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements","volume":"16","author":"Dostie","year":"2006","journal-title":"Genome Res"},{"key":"2023062712460730100_btz251-B7","doi-asserted-by":"crossref","first-page":"1237973","DOI":"10.1126\/science.1237973","article-title":"The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome","volume":"341","author":"Engreitz","year":"2013","journal-title":"Science"},{"key":"2023062712460730100_btz251-B8","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1038\/nmeth.1906","article-title":"ChromHMM: automating chromatin-state discovery and characterization","volume":"9","author":"Ernst","year":"2012","journal-title":"Nat. Methods"},{"key":"2023062712460730100_btz251-B9","first-page":"1026","author":"He","year":"2015"},{"key":"2023062712460730100_btz251-B10","doi-asserted-by":"crossref","first-page":"e1002893","DOI":"10.1371\/journal.pcbi.1002893","article-title":"Bayesian inference of spatial organizations of chromosomes","volume":"9","author":"Hu","year":"2013","journal-title":"PLoS Comput. Biol"},{"key":"2023062712460730100_btz251-B11","author":"Hu","year":"2018"},{"key":"2023062712460730100_btz251-B12","first-page":"1646","author":"Kim","year":"2016"},{"key":"2023062712460730100_btz251-B13","first-page":"1637","author":"Kim","year":"2016"},{"key":"2023062712460730100_btz251-B14","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/bioinformatics\/btp698","article-title":"Fast and accurate long-read alignment with Burrows\u2013Wheeler transform","volume":"26","author":"Li","year":"2010","journal-title":"Bioinformatics"},{"key":"2023062712460730100_btz251-B15","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":"2023062712460730100_btz251-B16","first-page":"807","author":"Nair","year":"2010"},{"key":"2023062712460730100_btz251-B17","author":"Paszke","year":"2017"},{"key":"2023062712460730100_btz251-B18","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":"2023062712460730100_btz251-B19","first-page":"5","author":"Tai","year":"2017"},{"key":"2023062712460730100_btz251-B20","first-page":"4539","author":"Tai","year":"2017"},{"key":"2023062712460730100_btz251-B21","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1016\/j.cell.2015.11.024","article-title":"CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription","volume":"163","author":"Tang","year":"2015","journal-title":"Cell"},{"key":"2023062712460730100_btz251-B22","doi-asserted-by":"crossref","first-page":"i26","DOI":"10.1093\/bioinformatics\/btu268","article-title":"A statistical approach for inferring the 3D structure of the genome","volume":"30","author":"Varoquaux","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062712460730100_btz251-B23","doi-asserted-by":"crossref","first-page":"19598","DOI":"10.1038\/srep19598","article-title":"Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks","volume":"6","author":"Wang","year":"2016","journal-title":"Sci. Rep"},{"key":"2023062712460730100_btz251-B24","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1038\/s41467-018-03113-2","article-title":"Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus","volume":"9","author":"Zhang","year":"2018","journal-title":"Nat. Commun"},{"key":"2023062712460730100_btz251-B25","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1038\/ng1891","article-title":"Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra-and interchromosomal interactions","volume":"38","author":"Zhao","year":"2006","journal-title":"Nat. Genet"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz251\/28560784\/btz251.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/21\/4222\/50722011\/bioinformatics_35_21_4222.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/21\/4222\/50722011\/bioinformatics_35_21_4222.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T12:47:22Z","timestamp":1687870042000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/21\/4222\/5436129"}},"subtitle":[],"editor":[{"given":"John","family":"Hancock","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,4,9]]},"references-count":25,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2019,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz251","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,11,1]]},"published":{"date-parts":[[2019,4,9]]}}}