Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Abstract

Background Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1860 human genome-wide distal enhancer-to-target gene definitions (EnTDefs). To evaluate performance, we used gene set enrichment (GSE) testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology annotations, and other benchmarks. Results The top ranked 741 (40%) EnTDefs significantly outperform the common, naïve approach of linking distal regions to the nearest genes, and the top 10 EnTDefs perform well when applied to ChIP-seq data of other cell types. The GSE-based ranking of EnTDefs is highly concordant with ranking based on overlap with curated benchmarks of enhancer-gene interactions. Both our top general EnTDef and cell-type-specific EnTDefs significantly outperform seven independent computational and experiment-based enhancer-gene pair datasets. We show that using our top EnTDefs for GSE with either genome-wide DNA methylation or ATAC-seq data is able to better recapitulate the biological processes changed in gene expression data performed in parallel for the same experiment than our lower-ranked EnTDefs. Conclusions Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

Refined read‐out: The hUHRF1 Tandem‐Tudor domain prefers binding to histone H3 tails containing K4me1 in the context of H3K9me2/3

“…The resulting network was visualized using Cytoscape ( cytoscape.org ; Shannon et al, 2003 ). For enhancer‐to‐gene assignment and GO:BP analysis, we used the hybrid method of ChIP‐Enrich (Qin et al, 2022 ) with the settings nearest gene and adjusted for mappability—true. The resulting GO assignments were filtered for FDR ≤ 0.05, hybrid p value ≤ 0.05, and status enriched ( Supplemental File 4 ).…”

Parkinson’s disease risk enhancers in microglia

“…However, enhancers are known to control multiple genes in cis and trans . 19 To profile close-range interactions of the risk enhancer, we used published PLAC-seq (proximity ligation-assisted ChIP-seq) data from human microglia, neurons, and oligodendrocytes 20 ( Figure 2 A). Compared to neurons and oligodendrocytes, microglia have a unique interaction profile characterized by frequent contact with MMRN1 and GPRIN3 (denoted by red ovals in Figure 2 A).…”

CanMethdb: a database for genome-wide DNA methylation annotation in cancers

“…Typical enhancers in CanMethdb were downloaded from Fantom5 Transcribed Enhancer Atlas ( Andersson et al , 2014 ), a database describing enhancer regions defined using Cap Analysis of Gene Expression tags, encompassing 808 human samples, from which we manually annotated and screened 208 cancer cell line samples ( Supplementary Table S1 ). SEs in CanMethdb were obtained from SEdb ( Jiang et al , 2019 ; Qian et al , 2019 ) developed by our group using ROSE ( Loven et al , 2013 ) from 542 manually collected human public H3K27ac samples, from which we manually annotated and screened 315 cancer samples ( Supplementary Table S2 ). The merged cancer type-specific ATAC regions were downloaded from a paper generating high-quality ATAC-seq data in 410 TCGA tumor samples across 23 cancer types ( Corces et al , 2018 ).…”