{"doi":"10.1101/2020.10.27.357756","title":"HiCRep.py: Fast comparison of Hi-C contact matrices in Python","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  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 asses 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. We introduce a Python implementation of HiCRep and demonstrate that it is much faster than the existing R implementation. Furthermore, we give examples of HiCRep’s ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data. HiCRep.py and its documentation are available with a GPL license at\n                  <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/Noble-Lab/hicrep\">https://github.com/Noble-Lab/hicrep</jats:ext-link>\n                  . The software may be installed automatically using the pip package installer.\n                </jats:p>","journal":null,"year":null,"id":613837,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1159656,"name":"Justin Sanders","orcid":null,"position":1,"is_corresponding":false},{"id":4958,"name":"William Stafford Noble","orcid":"0000-0001-7283-4715","position":2,"is_corresponding":false},{"id":566538,"name":"Dejun Lin","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"HiCRep.py: Fast comparison of Hi-C contact matrices in Python","abstract":"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 asses 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. We introduce a Python implementation of HiCRep and demonstrate that it is much faster than the existing R implementation. Furthermore, we give examples of HiCRep’s ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data. 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.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33576390","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"5U54DK107979-02","title":"University of Washington Center for Nuclear Organization and Function"}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"CC BY","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2020/10/28/2020.10.27.357756.full.pdf","host_type":"Unpaywall"},{"url":"https://doi.org/10.1093/bioinformatics/btab097","host_type":""},{"url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab097/36331420/btab097.pdf","host_type":""},{"url":"https://doi.org/10.1101/2020.10.27.357756","host_type":""},{"url":"https://europepmc.org/articles/pmc8479650?pdf=render","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/33576390","host_type":""},{"url":"http://dx.doi.org/10.1093/bioinformatics/btab097","host_type":""},{"url":"https://dx.doi.org/10.1101/2020.10.27.357756","host_type":""},{"url":"https://dx.doi.org/10.1093/bioinformatics/btab097","host_type":""}],"fields_of_study":["0301 basic medicine","03 medical and health sciences","0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Genome","Reproducibility of Results","Applications Notes","Chromatin","Chromosomes","Software"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T09:22:40.294263Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}