Genome Biology (Apr 2022)

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

  • Tingting Qin,
  • Christopher Lee,
  • Shiting Li,
  • Raymond G. Cavalcante,
  • Peter Orchard,
  • Heming Yao,
  • Hanrui Zhang,
  • Shuze Wang,
  • Snehal Patil,
  • Alan P. Boyle,
  • Maureen A. Sartor

DOI
https://doi.org/10.1186/s13059-022-02668-0
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 30

Abstract

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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.

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