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Most of the existing methods do not account for dependency in Hi-C data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on the detection of enriched contacts. ZipHiC implements a Bayesian method based on a hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC) to detect interactions in two-dimensional space based on a Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to the contact frequency matrix, allows borrowing information from neighbours using the Potts model and improves computation speed using the ABC model. In addition to outperforming existing tools on both simulated and real data, our model also provides insights into different sources of biases that affects Hi-C data. We show that some datasets display higher biases from DNA accessibility or Transposable Elements content. Furthermore, our analysis in Drosophila melanogaster showed that approximately half of the detected significant interactions connect promoters with other parts of the genome indicating a functional biological role. Finally, we found that the micro-C datasets display higher biases from DNA accessibility compared to a similar Hi-C experiment, but this can be corrected by ZipHiC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R scripts are available at https:\/\/github.com\/igosungithub\/HMRFHiC.git.<\/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\/btac387","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T08:31:00Z","timestamp":1654763460000},"page":"3523-3531","source":"Crossref","is-referenced-by-count":6,"title":["ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data"],"prefix":"10.1093","volume":"38","author":[{"given":"Itunu G","family":"Osuntoki","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, University of Essex , Colchester CO4 3SQ, UK"},{"name":"Statistics, Modelling and Economics Department, UK Health Security Agency , London NW9 5EQ, UK"}]},{"given":"Andrew","family":"Harrison","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, University of Essex , Colchester CO4 3SQ, UK"}]},{"given":"Hongsheng","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, University of Essex , Colchester CO4 3SQ, UK"}]},{"given":"Yanchun","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, University of Essex , Colchester CO4 3SQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9964-6271","authenticated-orcid":false,"given":"Nicolae Radu","family":"Zabet","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Essex , Colchester CO4 3SQ, UK"},{"name":"Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London E1 2AT, UK"}]}],"member":"286","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"2023041405365114700_","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1126\/science.1232542","article-title":"Genome-wide quantitative enhancer activity maps identified by STARR-seq","volume":"339","author":"Arnold","year":"2013","journal-title":"Science"},{"key":"2023041405365114700_","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":"2023041405365114700_","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1093\/genetics\/162.4.2025","article-title":"Approximate Bayesian computation in population genetics","volume":"162","author":"Beaumont","year":"2002","journal-title":"Genetics"},{"key":"2023041405365114700_","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1038\/nrg.2016.112","article-title":"Organization and function of the 3D genome","volume":"17","author":"Bonev","year":"2016","journal-title":"Nat. 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