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GENI: A web server to identify gene set enrichments in tumor samples
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 (G...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681878/ https://www.ncbi.nlm.nih.gov/pubmed/38034403 http://dx.doi.org/10.1016/j.csbj.2023.10.053 |
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author | Hayashi, Arata Ruppo, Shmuel Heilbrun, Elisheva E. Mazzoni, Chiara Adar, Sheera Yassour, Moran Rmaileh, Areej Abu Shaul, Yoav D. |
author_facet | Hayashi, Arata Ruppo, Shmuel Heilbrun, Elisheva E. Mazzoni, Chiara Adar, Sheera Yassour, Moran Rmaileh, Areej Abu Shaul, Yoav D. |
author_sort | Hayashi, Arata |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10681878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-106818782023-11-30 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. Comput Struct Biotechnol J Software/Web Server Article 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. Research Network of Computational and Structural Biotechnology 2023-10-31 /pmc/articles/PMC10681878/ /pubmed/38034403 http://dx.doi.org/10.1016/j.csbj.2023.10.053 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Software/Web Server Article Hayashi, Arata Ruppo, Shmuel Heilbrun, Elisheva E. Mazzoni, Chiara Adar, Sheera Yassour, Moran Rmaileh, Areej Abu Shaul, Yoav D. GENI: A web server to identify gene set enrichments in tumor samples |
title | GENI: A web server to identify gene set enrichments in tumor samples |
title_full | GENI: A web server to identify gene set enrichments in tumor samples |
title_fullStr | GENI: A web server to identify gene set enrichments in tumor samples |
title_full_unstemmed | GENI: A web server to identify gene set enrichments in tumor samples |
title_short | GENI: A web server to identify gene set enrichments in tumor samples |
title_sort | geni: a web server to identify gene set enrichments in tumor samples |
topic | Software/Web Server Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681878/ https://www.ncbi.nlm.nih.gov/pubmed/38034403 http://dx.doi.org/10.1016/j.csbj.2023.10.053 |
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