期刊
BIOINFORMATICS
卷 32, 期 15, 页码 2256-2263出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw169
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资金
- Virginia Commonwealth University start-up fund
- National Institute of Arthritis and Musculoskeletal and Skin Diseases [P30 AR053483]
- Institutional Development Award (IDeA) from the National Institute of General Medical Sciences [P30 GM103510]
- National Science Foundation [ACI-1345426]
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.integrativegenomics.org/.
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