GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets

Abstract

Motivation: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. Results: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature-and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. Availability and Implementation: GenomeRunner web server is freely available at http://www.inte grativegenomics.org/. Contact:

Novel transcriptional activity and extensive allelic imbalance in the human MHC region

“…Transcription factor binding site enrichment analysis was performed for each of the fourteen co-expression clusters using Genome Runner Web 41 , which compares the genomic coordinates of each transcript to the genomic positions of known transcription factor binding sites, using a database that includes the non-cell specific binding patterns of 161 transcription factors, measured via transcription factor ChIP-seq distributed by ENCODE. The coordinates for the promoter region of each gene in each coexpression cluster was used as imput, defined as the 1500bp preceding transcriptional start sites.…”

TADCompare: An R Package for Differential and Temporal Analysis of Topologically Associated Domains

“…It outputs genomic coordinates of differential boundaries, type of the differences, and the associated boundary score measures. The downstream analysis options may be gene enrichment analysis in the proximity of (different types of) differential boundaries using rGREAT, epigenomic enrichment analysis [GenomeRunner (Dozmorov et al, 2012(Dozmorov et al, , 2016, LOLA (Sheffield and Bock, 2016)], and visual exploration of differential boundaries. Although TADCompare provides simultaneous visualization of two Hi-C matrices and the associated boundary differences and boundary scores, external tools for visualizing multiple datasets may be explored (reviewed in Yardimci et al, 2019).…”

R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi‐C Datasets

“…The coordinates of differentially interacting regions may be saved as .bed files for downstream analysis in tools such as GenomeRunner (Dozmorov, Cara, Giles, & Wren, 2016) or LOLA (Sheffield & Bock, 2016) or for visualization in, e.g., the UCSC Genome Browser (Current Protocols article: Karolchik, Hinrichs, & Kent, 2009…”