Your browser doesn't support javascript.
loading
GENI: A web server to identify gene set enrichments in tumor samples.
Hayashi, Arata; Ruppo, Shmuel; Heilbrun, Elisheva E; Mazzoni, Chiara; Adar, Sheera; Yassour, Moran; Rmaileh, Areej Abu; Shaul, Yoav D.
Affiliation
  • Hayashi A; Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
  • Ruppo S; Info-CORE, Bioinformatics Unit of the I-CORE at the Hebrew University of Jerusalem, Jerusalem, Israel.
  • Heilbrun EE; Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
  • Mazzoni C; Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
  • Adar S; Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
  • Yassour M; Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
  • Rmaileh AA; School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Shaul YD; Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
Comput Struct Biotechnol J ; 21: 5531-5537, 2023.
Article in En | MEDLINE | ID: mdl-38034403
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Struct Biotechnol J Year: 2023 Type: Article Affiliation country: Israel

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Struct Biotechnol J Year: 2023 Type: Article Affiliation country: Israel