A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters

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• dc.contributor.author González Ramírez, Mar, 1991-
• dc.contributor.author Blanco, Enrique
• dc.contributor.author Di Croce, Luciano
• dc.date.accessioned 2023-03-09T07:22:09Z
• dc.date.available 2023-03-09T07:22:09Z
• dc.date.issued 2023
• dc.description.abstract Here, we present a computational pipeline to obtain quantitative models that characterize the relationship of gene expression with the epigenetic marking at enhancers or promoters in mouse embryonic stem cells. Our protocol consists of (i) generating predictive models of gene expression from epigenetic information (such as histone modification ChIP-seq) at enhancers or promoters and (ii) assessing the performance of these predictive models. This protocol could be applied to other biological scenarios or other types of epigenetic data. For complete details on the use and execution of this protocol, please refer to Gonzalez-Ramirez et al. (2021).1.
• dc.description.sponsorship The work in the Di Croce laboratory is supported by grants from “la Caixa” Foundation (HR20-00411), the Spanish Ministry of Science and Innovation (PID2019-108322GB-100), “Fundación Vencer El Cancer” (VEC), and the European Regional Development Fund (FEDER) and from AGAUR. We acknowledge support from the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme/Generalitat de Catalunya.
• dc.format.mimetype application/pdf
• dc.identifier.citation González-Ramírez M, Blanco E, Di Croce L. A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters. STAR Protocols. 2023 Mar 17;4(1):101948. DOI: 10.1016/j.xpro.2022.101948
• dc.identifier.doi http://dx.doi.org/10.1016/j.xpro.2022.101948
• dc.identifier.issn 2666-1667
• dc.identifier.uri http://hdl.handle.net/10230/56114
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof STAR Protocols. 2023 Mar 17;4(1):101948
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-108322GB-100
• dc.rights © 2022 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Bioinformatics
• dc.subject.keyword Cell differentiation
• dc.subject.keyword Genomics
• dc.subject.keyword Stem cells
• dc.title A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion