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

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.

Testing Proximity of Genomic Regions to Transcription Start Sites and Enhancers Complements Gene Set Enrichment Testing.

Large sets of genomic regions are generated by the initial analysis of various genome-wide sequencing data, such as ChIP-seq and ATAC-seq experiments. Gene set enrichment (GSE) methods are commonly employed to determine the pathways associated with them. Given the pathways and other gene sets (e.g., GO terms) of significance, it is of great interest to know the extent to which each is driven by binding near transcription start sites (TSS) or near enhancers. Currently, no tool performs such an analysis. Here, we present a method that addresses this question to complement GSE methods for genomic regions. Specifically, the new method tests whether the genomic regions in a gene set are significantly closer to a TSS (or to an enhancer) than expected by chance given the total list of genomic regions, using a non-parametric test. Combining the results from a GSE test with our novel method provides additional information regarding the mode of regulation of each pathway, and additional evidence that the pathway is truly enriched. We illustrate our new method with a large set of ENCODE ChIP-seq data, using the chipenrich Bioconductor package. The results show that our method is a powerful complementary approach to help researchers interpret large sets of genomic regions.

GS2PATH: a web-based integrated analysis tool for finding functional relationships using gene ontology and biochemical pathway data.

UNLABELLED: GS2PATH is a Web-based pipeline tool to permit functional enrichment of a given gene set from prior knowledge databases, including gene ontology (GO) database and biological pathway databases. The tool also provides an estimation of gene set enrichment, in GO terms, from the databases of the KEGG and BioCarta pathways, which may allow users to compute and compare functional over-representations. This is especially useful in the perspective of biological pathways such as metabolic, signal transduction, genetic information processing, environmental information processing, cellular process, disease, and drug development. It provides relevant images of biochemical pathways with highlighting of the gene set by customized colors, which can directly assist in the visualization of functional alteration. AVAILABILITY: The GS2PATH system is freely available at http://array.kobic.re.kr:8080/arrayport/gs2path/.