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

doi:10.1016/j.csbj.2023.10.053

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

Comput Struct Biotechnol J. 2023 Oct 31:21:5531-5537. doi: 10.1016/j.csbj.2023.10.053. eCollection 2023.

Authors

Arata Hayashi¹, Shmuel Ruppo², Elisheva E Heilbrun³, Chiara Mazzoni³, Sheera Adar³, Moran Yassour^{3

4}, Areej Abu Rmaileh¹, Yoav D Shaul¹

Affiliations

¹ Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
² Info-CORE, Bioinformatics Unit of the I-CORE at the Hebrew University of Jerusalem, Jerusalem, Israel.
³ Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
⁴ School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

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.

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