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

• Published in: STAR protocols 2023-03, Vol.4 (1), p.101948, Article 101948
• Main Authors: González-Ramírez, Mar, Blanco, Enrique, Di Croce, Luciano
• Format: Article
• Language: English
• Subjects: Animals; Bioinformatics; Cell Differentiation; Epigenesis, Genetic; Gene Expression; Genomics; Histones - metabolism; Mice; Promoter Regions, Genetic - genetics; Protocol; Regulatory Sequences, Nucleic Acid; Stem Cells
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2022.101948

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 [Display omitted] •Protocol to study the relationship between epigenetic marking and gene expression•Steps to calculate ChIP-seq signal strength from mapped reads over regulatory regions•Pipeline written in R to learn predictive models of gene expression from epigenetic data•Steps for ChIP-seq and RNA-seq raw data analysis in case-mapped reads are not available Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. 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.