<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-18T01:12:14Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10230/52841" metadataPrefix="oai_dc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10230/52841</identifier><datestamp>2025-12-12T02:02:20Z</datestamp><setSpec>com_2072_6</setSpec><setSpec>col_2072_452952</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
   <dc:title>Identification of chromatin loops from Hi-C interaction matrices by CTCF-CTCF topology classification</dc:title>
   <dc:creator>Galan Martínez, Silvia, 1992-</dc:creator>
   <dc:creator>Serra, François</dc:creator>
   <dc:creator>Marti-Renom, Marc A.</dc:creator>
   <dc:subject>Genòmica</dc:subject>
   <dc:subject>Cromatina</dc:subject>
   <dc:subject>Epigenètica</dc:subject>
   <dc:description>Genome-wide profiling of long-range interactions has revealed that the CCCTC-Binding factor (CTCF) often anchors chromatin loops and is enriched at boundaries of the so-called Topologically Associating Domains, which suggests that CTCF is essential in the 3D organization of chromatin. However, the systematic topological classification of pairwise CTCF-CTCF interactions has not been yet explored. Here, we developed a computational pipeline able to classify all CTCF-CTCF pairs according to their chromatin interactions from Hi-C experiments. The interaction profiles of all CTCF-CTCF pairs were further structurally clustered using self-organizing feature maps and their functionality characterized by their epigenetic states. The resulting clusters were then input to a convolutional neural network aiming at the de novo detecting chromatin loops from Hi-C interaction matrices. Our new method, called LOOPbit, is able to automatically detect significant interactions with a higher proportion of enhancer-promoter loops compared to other callers. Our highly specific loop caller adds a new layer of detail to the link between chromatin structure and function.</dc:description>
   <dc:description>Funding: European Research Council under the 7th Framework Program FP7/2007–2013 [ERC grant agreement 609989]; European Union&amp;apos;s Horizon 2020 research and innovation programme [676556]; Spanish Ministerio de Ciencia e Innovación [BFU2017-85926-P, PID2020-115696RB-I00]; CRG acknowledges support from ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012-0208 and the CERCA Programme/Generalitat de Catalunya as well as support of the Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III and the EMBL partnership; Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement; European Regional Development Fund (ERDF) by the Spanish Ministry of Science and Innovation corresponding to the Programa Opertaivo FEDER Plurirregional de España (POPE) 2014–2020; Secretaria d’Universitats i Recerca, Departament d’Empresa i Coneixement of the Generalitat de Catalunya corresponding to the programa Operatiu FEDER Catalunya 2014–2020. Funding for open access charge: Spanish Ministerio de Ciencia, Innovación y Universidades [BFU2017-85926-P]</dc:description>
   <dc:date>2022-04-07T06:10:29Z</dc:date>
   <dc:date>2022-04-07T06:10:29Z</dc:date>
   <dc:date>2022</dc:date>
   <dc:type>info:eu-repo/semantics/article</dc:type>
   <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
   <dc:identifier>Galan S, Serra F, Marti-Renom MA. Identification of chromatin loops from Hi-C interaction matrices by CTCF-CTCF topology classification. NAR Genom Bioinform. 2022 Mar 8;4(1):lqac021. DOI: 10.1093/nargab/lqac021</dc:identifier>
   <dc:identifier>2631-9268</dc:identifier>
   <dc:identifier>http://hdl.handle.net/10230/52841</dc:identifier>
   <dc:identifier>http://dx.doi.org/10.1093/nargab/lqac021</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:relation>info:eu-repo/grantAgreement/EC/H2020/676556</dc:relation>
   <dc:relation>info:eu-repo/grantAgreement/EC/FP7/609989</dc:relation>
   <dc:relation>info:eu-repo/grantAgreement/ES/2PE/BFU2017-85926-P</dc:relation>
   <dc:rights>© Silvia Galan, François Serra, Marc A. Marti-Renom 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.&#xd;
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
   <dc:format>application/pdf</dc:format>
   <dc:format>application/pdf</dc:format>
   <dc:publisher>Oxford University Press</dc:publisher>
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