ShinyGO

ShinyGO performs enrichment analysis of gene lists to link genes to molecular pathways and functional categories (e.g., Gene Ontology terms) using annotation data from Ensembl and STRING-db across 59 plant species, 256 animal species, 115 archaeal species, and 1678 bacterial species.


Key Features:

  • Enrichment analysis: Performs statistical enrichment linking input gene lists to Gene Ontology (GO) terms and molecular pathways.
  • Annotation database: Uses annotation data sourced from Ensembl and STRING-db covering 59 plant, 256 animal, 115 archaeal, and 1678 bacterial species.
  • Graphical visualization: Produces graphical displays of enrichment results and gene characteristics to support interpretation of analyses.
  • API access to external resources: Provides an application programming interface to retrieve KEGG pathway diagrams and STRING protein-protein interaction networks.

Scientific Applications:

  • Omic gene-list interpretation: Interprets gene lists from various omic studies for animal and plant research by connecting genes to functions and pathways.
  • Functional annotation: Associates genes with biological processes, cellular components, and molecular functions via Gene Ontology terms.
  • Pathway and interaction exploration: Retrieves KEGG pathway diagrams and explores STRING protein-protein interaction networks to investigate pathway membership and protein interactions.
  • Cross-species analysis: Enables analyses across a broad range of species using the aggregated Ensembl and STRING-db annotations.

Methodology:

Performs enrichment analysis linking input gene lists to GO terms and pathways using annotation data from Ensembl and STRING-db; accesses KEGG pathways and STRING protein-protein interaction networks via an API; generates graphical visualizations of enrichment results and gene characteristics.

Topics

Details

Added:
1/14/2020
Last Updated:
11/24/2024

Operations

Publications

Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2019;36(8):2628-2629. doi:10.1093/bioinformatics/btz931. PMID:31882993. PMCID:PMC7178415.

PMID: 31882993
PMCID: PMC7178415
Funding: - National Institutes of Health: GM083226 - National Science Foundation/EPSCoR: IIA-1355423