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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)5531-5537
Number of pages7
JournalComputational and Structural Biotechnology Journal
Volume21
DOIs
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Bioinformatics
  • Cancer biology
  • Cancer-associated molecular mechanisms
  • Clinical data
  • Gene Set Enrichment Analysis
  • Multi-Gene Analysis
  • TCGA
  • Tumor samples
  • Web-based tools

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