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

González Ramírez, Mar, 1991-; Blanco, Enrique; Di Croce, Luciano

doi:http://dx.doi.org/10.1016/j.xpro.2022.101948

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

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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

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.