DegCre: Probabilistic association of differential gene expression with regulatory regions

Abstract

Differential gene expression in response to perturbations is mediated at least in part by changes in binding of transcription factors (TFs) and other proteins at specific genomic regions. Association of these cis-regulatory elements (CREs) with their target genes is a challenging task that is essential to address many biological and mechanistic questions. Many current approaches rely on chromatin conformation capture techniques that identify spatial proximity between genomic sites to establish CRE-to-gene associations. These methods can be effective but have limitations, including resolution, minimal detectable interaction distance, and cost. As an alternative, we have developed DegCre, a non-parametric method that evaluates correlations between measurements of perturbation-induced differential gene expression and differential regulatory signal at CREs to score possible CRE-to-gene associations. It has several unique features, including the ability to: use any type of CRE activity measurement; yield probabilistic scores for CRE-to-gene pairs; and assess CRE-to-gene pairings across a wide range of sequence distances. We apply DegCre to three data sets, each employing different perturbations and containing a variety of regulatory signal measurements, including chromatin openness, histone modifications, and TF occupancy. To test their efficacy, we compare DegCre associations to HiC loop calls and to CRISPR validated interactions, with both yielding good agreement. We demonstrate the identification of perturbation direct target genes with DegCre confirm the results with previous reports. DegCre is a novel approach to the association of CREs to genes from a perturbation-differential perspective, with strengths that are complementary to existing approaches and allow for new insights into gene regulation.