| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Genome Biology, Vol 23, Iss 1, Pp 1-30 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1474-760X |
| DOI: |
10.1186/s13059-022-02668-0 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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