{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T09:36:04Z","timestamp":1775554564699,"version":"3.50.1"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T00:00:00Z","timestamp":1613088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54 DK107979"],"award-info":[{"award-number":["U54 DK107979"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UM1 HG011531"],"award-info":[{"award-number":["UM1 HG011531"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to assess experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high-resolution Hi-C data or on large single-cell Hi-C datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a Python implementation of HiCRep and demonstrate that it is much faster and consumes much less memory than the existing R implementation. Furthermore, we give examples of HiCRep\u2019s ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>HiCRep.py and its documentation are available with a GPL license at https:\/\/github.com\/Noble-Lab\/hicrep. The software may be installed automatically using the pip package installer.<\/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\/btab097","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T10:50:50Z","timestamp":1612867850000},"page":"2996-2997","source":"Crossref","is-referenced-by-count":36,"title":["HiCRep.py: fast comparison of Hi-C contact matrices in Python"],"prefix":"10.1093","volume":"37","author":[{"given":"Dejun","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Genome Sciences, University of Washington , Seattle, WA 98040, USA"}]},{"given":"Justin","family":"Sanders","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brown University , Providence, RI 02912, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-4715","authenticated-orcid":false,"given":"William Stafford","family":"Noble","sequence":"additional","affiliation":[{"name":"Department of Genome Sciences, University of Washington , Seattle, WA 98040, USA"},{"name":"Paul G. Allen School of Computer Science and Engineering, University of Washington , Seattle, WA 98040, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,2,12]]},"reference":[{"key":"2023061310470923300_btab097-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":"2019","journal-title":"Bioinformatics"},{"key":"2023061310470923300_btab097-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-015-0745-7","article-title":"Analysis methods for studying the 3D architecture of the genome","volume":"16","author":"Ay","year":"2015","journal-title":"Genome Biol"},{"key":"2023061310470923300_btab097-B3","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/s41592-019-0547-z","article-title":"Simultaneous profiling of 3D genome structure and DNA methylation in single human cells","volume":"16","author":"Lee","year":"2019","journal-title":"Nat. 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