The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI's user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.

The Cancer Genome Atlas (TCGA) and other projects provide informative tumor-associated genomic data for the broad research community. Hence, several useful web-based tools have been generated to ease non-expert users with the analysis and characterization of a specific gene behavior in selected tumors. However, none of the existing tools offer the user the means to evaluate the expression profile of a given gene in the context of the whole transcriptome. Currently, such analyses require prior bioinformatic knowledge and expertise. Therefore, we developed GENI (Gene ENrichment Identifier) as a fast, user-friendly tool to analyze the TCGA expression data for gene set enrichments. GENI analyzes large-scale tumor-associated gene expression datasets and evaluates biological relevance, thus offering researchers a simplified means to analyze cancer patient-derived data.