Modeling the relationship of diverse genomic signatures to gene expression levels with the regulation of long-range enhancer-promoter interactions

Enhancer–promoter (E–P) interaction is an essential component of cis-regulatory regulation for gene expression. However, to comprehensively study the gene expression with the regulation of long-range E–P interactions is a major challenge in the regulatory networks. As these types of gene expression are regulated by diverse genomic signatures, we presented a computational method to study the relationships between gene expression levels and diverse genomic signatures. In this paper, based on the datasets of long-range E–P interactions, we extracted feature parameters from multiple signatures ( e.g. , epigenetic marks, transcription factors) and used regression models to predict the gene expression levels. In our results, we found that the predicted expression values correlated well with the measured expression values in both the interacting and non-interacting sets, and the correlation values of the interacting set were higher than that of the corresponding non-interacting set in each cell line, which indicated that the distal enhancers would cooperate with diverse genomic signatures to facilitate the expression level of target genes. By comparing the important signature features for the gene expression levels between the interacting and non-interacting sets in the same cell line, we found that the important specific signatures affect the gene expression regulated by distal enhancers. Our research provided additional insights about the roles of diverse signatures in gene expression with the regulation of distal enhancers.