Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data
dc.contributor.author | Qin, Tingting | |
dc.contributor.author | Lee, Christopher | |
dc.contributor.author | Li, Shiting | |
dc.contributor.author | Cavalcante, Raymond G. | |
dc.contributor.author | Orchard, Peter | |
dc.contributor.author | Yao, Heming | |
dc.contributor.author | Zhang, Hanrui | |
dc.contributor.author | Wang, Shuze | |
dc.contributor.author | Patil, Snehal | |
dc.contributor.author | Boyle, Alan P. | |
dc.contributor.author | Sartor, Maureen A. | |
dc.date.accessioned | 2022-08-10T18:37:49Z | |
dc.date.available | 2022-08-10T18:37:49Z | |
dc.date.issued | 2022-04-26 | |
dc.identifier.citation | Genome Biology. 2022 Apr 26;23(1):105 | |
dc.identifier.uri | https://doi.org/10.1186/s13059-022-02668-0 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/173874 | en |
dc.description.abstract | 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. | |
dc.title | Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data | |
dc.type | Journal Article | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/173874/1/13059_2022_Article_2668.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/5605 | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s) | |
dc.date.updated | 2022-08-10T18:37:47Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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