Annotating and prioritizing human non-coding variants with RegulomeDB

Abstract

Nearly 90% of the disease risk-associated variants identified from genome-wide association studies (GWAS) are in non-coding regions of the genome. The annotations obtained from analyzing functional genomics assays can provide additional information to pinpoint causal variants, which are often not the lead variants identified from association studies. However, the lack of available annotation tools limits the use of such data. To address the challenge, we have previously built the RegulomeDB database for prioritizing and annotating variants in non-coding regions1, which has been a highly utilized resource for the research community (Supplementary Fig. 1). RegulomeDB annotates a variant by intersecting its position with genomic intervals identified from functional genomic assays and computational approaches. It also incorporates those hits of a variant into a heuristic ranking score, representing its potential to be functional in regulatory elements. Here we present a newer version of the RegulomeDB web server, RegulomeDB v2.1 (http://regulomedb.org). We improve and boost annotation power by incorporating thousands of newly processed data from functional genomic assays in GRCh38 assembly, and now include probabilistic scores from the SURF algorithm that was the top performing non-coding variant predictor in CAGI 52. We also provide interactive charts and genome browser views to allow users an easy way to perform exploratory analyses in different tissue contexts.

Organ-specific prioritization and annotation of non-coding regulatory variants in the human genome

“…The continuously growing corpus of genomic data captures an increasingly complete view of human regulatory variation. RegulomeDB recently upgraded to version 2, expanding to >650 million and >1.5 billion genomic intervals in hg19 and GRCh38, respectively (11). The large discrepancy in the data availability between the two assemblies, for example in ChIP-seq and open chromatin data (RegulomeDB TF ChIP-seq availability in Fig 1a, open chromatin in Supplementary Fig 3, histone ChIP-seq in Supplementary Fig 4), results from better representation of complex variation and correction of sequencing artifacts in the GRCh38 assembly (12).…”

“…RegulomeDB intersects query variants with regulatory regions predicted by functional genomics assays and, by utilizing ranking heuristics, informs users about putative functional consequences to prioritize variants. Recently, RegulomeDB has been upgraded to version 2 (11), improving its annotation power by incorporating thousands of new functional genomics datasets from the ENCODE project (12), Roadmap Epigenomics Consortium (13), and the Genomics of Gene Regulation Consortium. A suite of models, namely SURF and TURF, was developed and integrated in this version to provide accurate probabilistic scores for general and cell-type specific regulatory activities (14, 15).…”

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

“…As most variants associated with complex traits are in the non-coding genome, discovering functional variants is constrained by methodological limitations. Computational approaches have been developed that utilise variable genomic features to aid variant prioritisation , including variable use of functional annotations, conservation scores, and TF motifs (Wang et al, 2022, Dong et al, 2023. However, for non-coding variants these methods tend to have low precision for identifying functional variants (Wang et al, 2022).…”

Causal Genetic Loci for a Motivated Behavior Spectrum Harbor Psychiatric Risk Genes

“…Using the RegulomeDB v2.2 44 , we found that most elements have predicted regulatory function, either assuming multiple functions, or as enhancers only or repressors only. About one third of the elements are predicted to be quiescent or transcribed (Figure 5D).…”

“…We downloaded multiple alignment sequence files across eight species (including zebrafish, medaka, stickleback, tetraodon, fugu, frog, mouse, and human) from the UCSC Genome Browser and analyzed the alignments with Biopython to discover conserved elements that contain our GWAS significant SNP variants. Regulatory function of the elements conserved between zebrafish and human were further explored using RegulomeDB 44 .…”