LAUSR

MOTIVATION In the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL), and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170,000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment, and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic as-sociations. RESULTS We designed the Reducing Associations by Linking Genes And omics Results (REALGAR) web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic, and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies. AVAILABILITY The REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.