GCgx: transcriptome-wide exploration of the response to glucocorticoids

in Journal of Molecular Endocrinology
Authors:
Qilin Cao Functional Immunogenomics Unit, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA

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Yamil Boo Irizarry Bioinformatics and Computational Biosciences Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, USA

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Svetlana Yazhuk Operations and Engineering Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, USA

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Thai Tran Functional Immunogenomics Unit, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA

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Manasi Gadkari Functional Immunogenomics Unit, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA

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Luis Miguel Franco Functional Immunogenomics Unit, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA

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https://orcid.org/0000-0002-0540-5942

Correspondence should be addressed to L M Franco: luis.franco@nih.gov
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Glucocorticoids are the cornerstone of immunosuppressive and anti-inflammatory therapy in humans, yet the mechanisms of glucocorticoid immunoregulation and toxicity remain unclear. The response to glucocorticoids is highly cell type-dependent, so translating results from different experimental systems into a better understanding of glucocorticoid effects in humans would benefit from rapid access to high-quality data on the response to glucocorticoids by different cell types. We introduce GCgx, a web application that allows investigators to quickly visualize changes in transcript abundance in response to glucocorticoids in a variety of cells and species. The tool is designed to grow by the addition of datasets based on input from the user community. GCgx is implemented in R and HTML and packaged as a Docker image. The tool and its source code are publicly available.

 

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