ShinyGO is a graphical web application designed for gene-set enrichment analysis in animals and plants. It leverages a large annotation database from Ensembl and STRING-db, covering 59 plant species, 256 animal species, 115 archaeal species, and 1678 bacterial species. Key features of ShinyGO include: 1. **Graphical Visualization**: It provides visualizations of enrichment results, gene characteristics, and protein–protein interaction (PPI) networks. 2. **API Access**: It integrates with KEGG and STRING to retrieve pathway diagrams and PPI networks. 3. **Statistical Analysis**: It performs statistical tests to identify significant differences in gene type, length, GC content, and chromosomal distribution. 4. **Comprehensive Databases**: It includes a wide range of gene sets, such as transcription factor (TF) and microRNA (miRNA) target genes. ShinyGO is based on the Shiny framework and R/Bioconductor packages, making it user-friendly and accessible. The tool is available at http://ge-lab.org/go/ and can be used to gain actionable insights from gene lists.ShinyGO is a graphical web application designed for gene-set enrichment analysis in animals and plants. It leverages a large annotation database from Ensembl and STRING-db, covering 59 plant species, 256 animal species, 115 archaeal species, and 1678 bacterial species. Key features of ShinyGO include: 1. **Graphical Visualization**: It provides visualizations of enrichment results, gene characteristics, and protein–protein interaction (PPI) networks. 2. **API Access**: It integrates with KEGG and STRING to retrieve pathway diagrams and PPI networks. 3. **Statistical Analysis**: It performs statistical tests to identify significant differences in gene type, length, GC content, and chromosomal distribution. 4. **Comprehensive Databases**: It includes a wide range of gene sets, such as transcription factor (TF) and microRNA (miRNA) target genes. ShinyGO is based on the Shiny framework and R/Bioconductor packages, making it user-friendly and accessible. The tool is available at http://ge-lab.org/go/ and can be used to gain actionable insights from gene lists.