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

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

The Hebrew University of Jerusalem

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	5531-5537
Number of pages	7
Journal	Computational and Structural Biotechnology Journal
Volume	21
DOIs	https://doi.org/10.1016/j.csbj.2023.10.053
State	Published - Jan 2023

Keywords

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

All Science Journal Classification (ASJC) codes

Genetics
Biophysics
Structural Biology
Biochemistry
Biotechnology
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.csbj.2023.10.053

Cite this

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title = "GENI: A web server to identify gene set enrichments in tumor samples",

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",

author = "Arata Hayashi and Shmuel Ruppo and Heilbrun, {Elisheva E.} and Chiara Mazzoni and Sheera Adar and Moran Yassour and Rmaileh, {Areej Abu} and Shaul, {Yoav D.}",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = jan,

doi = "10.1016/j.csbj.2023.10.053",

language = "الإنجليزيّة",

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T2 - A web server to identify gene set enrichments in tumor samples

AU - Hayashi, Arata

AU - Ruppo, Shmuel

AU - Heilbrun, Elisheva E.

AU - Mazzoni, Chiara

AU - Adar, Sheera

AU - Yassour, Moran

AU - Rmaileh, Areej Abu

AU - Shaul, Yoav D.

PY - 2023/1

Y1 - 2023/1

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Cancer biology

KW - Cancer-associated molecular mechanisms

KW - Clinical data

KW - Gene Set Enrichment Analysis

KW - Multi-Gene Analysis

KW - TCGA

KW - Tumor samples

KW - Web-based tools

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U2 - 10.1016/j.csbj.2023.10.053

DO - 10.1016/j.csbj.2023.10.053

M3 - مقالة

C2 - 38034403

SN - 2001-0370

VL - 21

SP - 5531

EP - 5537

JO - Computational and Structural Biotechnology Journal

JF - Computational and Structural Biotechnology Journal

ER -

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

Abstract

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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