GCgx
GCgx facilitates exploration of transcriptomic responses to glucocorticoids across cell types and species to support analysis of cell type–dependent immunoregulation and toxicity.
Key Features:
- High-quality transcriptomic datasets: Contains curated datasets of transcript abundance changes in response to glucocorticoids across multiple cell types and species.
- Cell type–specific response analysis: Enables analysis of cell type–dependent transcriptional responses to glucocorticoid exposure.
- Cross-species comparison: Supports comparison of glucocorticoid-induced transcriptomic changes across species to inform translation from model systems to humans.
- Visualization of transcriptomic changes: Provides visualization of glucocorticoid-induced changes in gene expression across datasets.
- Dataset integration and scalability: Supports incorporation of additional datasets to expand comparative and integrative analyses.
Scientific Applications:
- Mechanistic studies of glucocorticoid action: Analyze gene expression changes relevant to immunosuppression and anti-inflammatory effects of glucocorticoids.
- Investigation of toxicity and side effects: Examine transcriptomic signatures associated with glucocorticoid-related toxicity.
- Cell type–specific immunology: Identify differential transcriptional responses among cell types to elucidate immunoregulatory mechanisms.
- Translational research: Facilitate translation of findings from experimental models to human biological contexts.
Methodology:
Integrates and visualizes transcript abundance datasets measuring responses to glucocorticoids across diverse cell types and species and supports addition of new datasets.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R
- Added:
- 5/8/2022
- Last Updated:
- 5/8/2022
Operations
Publications
Cao Q, Boo Irizarry Y, Yazhuk S, Tran T, Gadkari M, Franco LM. GCgx: transcriptome-wide exploration of the response to glucocorticoids. Journal of Molecular Endocrinology. 2022;68(2):B1-B4. doi:10.1530/jme-21-0107. PMID:34787097. PMCID:PMC8691098.