REALGAR is a tissue-specific, disease-focused resource that integrates results from omics studies to facilitate the design of experiments to validate genome-wide association study (GWAS) findings and generate novel hypotheses. Omics results are currently available for gene expression microarrays, RNA-Seq, and ChIP-Seq datasets. To use REALGAR, input an official gene symbol or SNP ID, and select tissues, conditions, treatments, and GWAS of interest. The 'Omics Results’ tab allows you to visualize and download results for the studies matching your selection criteria. The ‘Datasets loaded’ tab provides more information about the datasets selected. The ‘About’ tab provides additional information about the app.
Once you have made above selection, click Go. Data downloads and figure displays rely on the parameters specified before the most recent time Go was clicked. The intial figure display and data downloads correspond to the initial parameters shown.
Differential expression results for individual microarray and RNA-Seq studies were obtained using RAVED . Meta-analysis results displayed were obtained using three summary statistics-based approaches: (1) an effect size-based method that applies a random-effects model, (2) a p-value-based method that applies Fisher's sum-of-logs method, and (3) a rank-based method that adopts the Rank Product. Note that p-values from the p-value-based and effect size-based methods are not adjusted for multiple comparisons corrections. We suggest that users apply a stringent multiple comparisons corrections threshold to assign significance when using those methods (e.g., correct for tests in 25,000 genes, or a threshold of 2x10-6. For the rank-based method, an analytic rank product is provided instead of a permutated empirical p-value, so we suggest that users refer to the rank score when prioritizing the genes for functional validation. More information can be found in the 'Datasets loaded' and 'About' tabs.
Reference transcript for the selected gene is displayed along with any SNPs and/or transcription factor binding sites that fall within 20kb of the gene boundaries. Transcription factor binding site tracks contain ChIP-Seq results obtained with brocade for the Glucocorticoid Receptor (GR) and RNA Polymerase 2 (RNAP2) under dexamethasone (dex) or vehicle control (EtOH) exposure conditions. SNPs from GWAS studies meeting the selected p-value threshold are colored by p-value reported by the original study, with the lowest p-values corresponding to the brightest colors. cis-eQTL results for whole blood, lung, and skeletal muscle tissues were obtained from the GTEx V8 release . Glucocorticoid response element (GRE) motifs within Glucocorticoid Receptor (GR) binding sites were identified with the FIMO tool . More information can be found in the 'Datasets loaded' and 'About' tabs.
The following information is provided in each column:
Dataset: GEO accession numbers that link to corresponding GEO entries
PMID: PubMed IDs for papers, when available, that link to corresponding PubMed entries
Report: Quality Control report generated by RAVED
Description: brief overview of study design
The following information is provided in each column:
Dataset: GEO accession numbers that link to corresponding GEO entries
PMID: PubMed IDs for papers, when available, that link to corresponding PubMed entries
Report: Quality Control report generated by brocade
Description: brief overview of study design
The following information is provided in each column:
Dataset: name of the study, which links to the study website or publication
Description: brief overview of GWAS study design
This Rstudio Shiny app was created by members of the Himes Lab . Full code is available in GitHub .
Coding and design by Maya Shumyatcher, Mengyuan Kan, Avantika Diwadkar and Blanca Himes
Analysis of individual datasets by Avantika Diwadkar, Jaehyun Joo, Mengyuan Kan, Nisha Narayanan, Supriya Saxena, Haoyue Shuai, Maya Shumyatcher, Gabriel Soliman
If you use REALGAR in your research, please cite the following papers:
Kan M, Diwadkar AR, Saxena S, Shuai H, Joo J, Himes BE. REALGAR: a web app of integrated respiratory omics data. Bioinformatics. 2022 Sep 15;38(18):4442-4445.(PMID: 35863045 ).
Shumyatcher M, Hong R, Levin J, Himes BE. Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations. AMIA Annu Symp Proc. 2018;2017:1589–1596. Published 2018 Apr 16. (PMID: 29854229).
Analysis of gene expression microarray and RNA-Seq data was performed as described in this paper:
Kan M, Shumyatcher M, Diwadkar A, Soliman G, Himes BE. Integration of Transcriptomic Data Identifies Global and Cell-Specific Asthma-Related Gene Expression Signatures. AMIA Annu Symp Proc. 2018;2018:1338–1347. Published 2018 Dec 5. (PMID: 30815178) . You can refer to the code here.
Analysis of ChIP-Seq data was performed as described in this paper:
Diwadkar AR, Kan M, Himes BE. Facilitating Analysis of Publicly Available ChIP-Seq Data for Integrative Studies. AMIA Annu Symp Proc. 2019;2019:371-379. Published 2020 Mar 4. (PMID: 32308830) . You can refer to the code here.
2022-01 Added Gene-expression levels tab
2021-12 Redesigned integrative gene tracks
2021-08 Added initial PAH and PM transcriptomic datasets
2020-09 Incorporated UKBB asthma and COPD GWAS results
2020-08 Added initial cigarette and e-cig transcriptomic datasets
2019-08 Added initial PDE inhibitor transcriptomic datasets
2019-01 Incorporated glucocorticoid receptor ChIP-Seq datasets
2018-08 Added initial Beta2-agonist transcriptomic datasets
2018-02 Incorporated TAGC and Ferreira asthma GWAS results
2018-01 Added reactive meta-analysis scores for transcriptomic datasets
2017-08 Published initial version of REALGAR