Computational and Structural Biotechnology Journal (Jan 2023)

GENI: A web server to identify gene set enrichments in tumor samples

  • Arata Hayashi,
  • Shmuel Ruppo,
  • Elisheva E. Heilbrun,
  • Chiara Mazzoni,
  • Sheera Adar,
  • Moran Yassour,
  • Areej Abu Rmaileh,
  • Yoav D. Shaul

Journal volume & issue
Vol. 21
pp. 5531 – 5537

Abstract

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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.

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