Annotation of functional variation in personal genomes using RegulomeDB

By Alan P. Boyle, Eurie L. Hong, M. Hariharan, Yong Cheng, M. Schaub, Maya M Kasowski, Konrad J. Karczewski, Julie Park, B. Hitz, S. Weng and 2 more. Genome Research, 2012

2,733 citations recorded by the source corpus.

As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified. Moreover, we demonstrate a GWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/de30519eadcb082c58a10a8767037e88c3863e46

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites (2015, background): “RegulomeDB, HaploReg and FunSeq each enable assessment of the effects of single nucleotide variants on predicted binding sites (Boyle et al. , 2012; Khurana et al. , 2013; Ward and Kellis, 2012).”
Genome-wide analysis identifies 12 loci influencing human reproductive behavior (2016, positive): “An analysis using RegulomeDB identified 50 SNPs in five loci that most likely have regulatory consequences ( Supplementary Table 6 ).”
An Integrated Encyclopedia of DNA Elements in the Human Genome (2012, methodology): “A more detailed analysis of regulatory variant annotation is described in ref 74 .”
Linking disease associations with regulatory information in the human genome (2012, methodology): “(Boyle et al. 2012), a database designed for fast annotation of SNPs that combines ENCODE data sets (ChIP-seq peaks, DNase I hy-”
Approximation to the Distribution of Fitness Effects across Functional Categories in Human Segregating Polymorphisms (2014, positive): “First, we partitioned the genome by regulatory 219 regions identified by Regulome DB [39], which predicts regulatory activity in noncoding regions based 220 on different types of experimental evidence (Figure S14, Tables 1, S1).”
Identification of type 2 diabetes subgroups through topological analysis of patient similarity (2015, background): “…of DNA Elements (ENCODE) project has shown that ~95% of known variants within sequenced genomes and 88% of those variants from GWAS fall outside of coding regions ( 71 ), and a functional SNP most strongly supported by experimental evidence is an SNP in the linkage disequilibrium region ( 72 ).”
FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer (2014, methodology): “A number of tools have been developed using these data to annotate potential regulatory variants or to suggest the most likely causal variants in linkage disequilibrium with GWAS SNPs, such as Haploreg [18], RegulomeDB [19], ANNOVAR [20], GEMINI [21], FunciSNP [22], and VEP [23].”