{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Freire-Pritchett P"],"funding":["Medical Research Council","Wellcome Trust","Biotechnology and Biological Sciences Research Council"],"pagination":["4144-4176"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7612634"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(9)"],"pubmed_abstract":["Capture Hi-C is widely used to obtain high-resolution profiles of chromosomal interactions involving, at least on one end, regions of interest such as gene promoters. Signal detection in Capture Hi-C data is challenging and cannot be adequately accomplished with tools developed for other chromosome conformation capture methods, including standard Hi-C. Capture Hi-C Analysis of Genomic Organization (CHiCAGO) is a computational pipeline developed specifically for Capture Hi-C analysis. It implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures for this data type. Here we provide a step-by-step guide to the CHiCAGO workflow that is aimed at users with basic experience of the command line and"],"journal":["Nature protocols"],"pubmed_title":["Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools."],"pmcid":["PMC7612634"],"funding_grant_id":["107881","107881/Z/15/Z","215097","215097/Z/18/Z","MC_UP_1605/3","WT107881","MC_UU_00002/4","MC-A652-5QA20"],"pubmed_authors":["Wingett SW","Spivakov M","Cairns J","Della Rosa M","Malysheva V","Freire-Pritchett P","Wallace C","Orchard WR","Ray-Jones H","Eijsbouts CQ"],"additional_accession":[]},"is_claimable":false,"name":"Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools.","description":"Capture Hi-C is widely used to obtain high-resolution profiles of chromosomal interactions involving, at least on one end, regions of interest such as gene promoters. Signal detection in Capture Hi-C data is challenging and cannot be adequately accomplished with tools developed for other chromosome conformation capture methods, including standard Hi-C. Capture Hi-C Analysis of Genomic Organization (CHiCAGO) is a computational pipeline developed specifically for Capture Hi-C analysis. It implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures for this data type. Here we provide a step-by-step guide to the CHiCAGO workflow that is aimed at users with basic experience of the command line and","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2025-04-04T08:52:46.468Z","creation":"2025-04-04T08:52:46.468Z"},"accession":"S-EPMC7612634","cross_references":{"pubmed":["34373652"],"doi":["10.1038/s41596-021-00567-5"]}}