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SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data

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dc.contributor.author Klonizakis, Antonios
dc.contributor.author Nikolaou, Christoforos
dc.contributor.author Graf, T. (Thomas)
dc.date.accessioned 2023-03-09T07:22:12Z
dc.date.available 2023-03-09T07:22:12Z
dc.date.issued 2023
dc.identifier.citation Klonizakis A, Nikolaou C, Graf T. SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data. Bioinformatics. 2023 Jan;39(1):btac742. DOI: 10.1093/bioinformatics/btac742
dc.identifier.issn 1367-4803
dc.identifier.uri http://hdl.handle.net/10230/56115
dc.description.abstract Motivation: The compartmentalization of biochemical reactions, involved in the activation of gene expression in the eukaryotic nucleus, leads to the formation of membraneless bodies through liquid-liquid phase separation. These formations, called transcriptional condensates, appear to play important roles in gene regulation as they are assembled through the association of multiple enhancer regions in 3D genomic space. To date, we are still lacking efficient computational methodologies to identify the regions responsible for the formation of such condensates, based on genomic and conformational data. Results: In this work, we present SEGCOND, a computational framework aiming to highlight genomic regions involved in the formation of transcriptional condensates. SEGCOND is flexible in combining multiple genomic datasets related to enhancer activity and chromatin accessibility, to perform a genome segmentation. It then uses this segmentation for the detection of highly transcriptionally active regions of the genome. At a final step, and through the integration of Hi-C data, it identifies regions of putative transcriptional condensates (PTCs) as genomic domains where multiple enhancer elements coalesce in 3D space. SEGCOND identifies a subset of enhancer segments with increased transcriptional activity. PTCs are also found to significantly overlap highly interconnected enhancer elements and super enhancers obtained through two independent approaches. Application of SEGCOND on data from a well-defined system of B-cell to macrophage transdifferentiation leads to the identification of previously unreported genes with a likely role in the process. Availability and implementation: Source code and details for the implementation of SEGCOND is available at https://github.com/AntonisK95/SEGCOND. Supplementary information: Supplementary data are available at Bioinformatics online.
dc.description.sponsorship This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC). The project reference number of the Plan Estatal 2019 funding is PID2019-109354GB-I00.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Oxford University Press
dc.relation.ispartof Bioinformatics. 2023 Jan;39(1):btac742
dc.rights © The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://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.uri https://creativecommons.org/licenses/by/4.0/
dc.title SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btac742
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-109354GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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