RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets

Changes in biological pathways provide essential clues about metabolism. Genome-scale metabolic models (GEM) are network-based templates that computationally describe all stoichiometric associations and gene-protein reaction (GPR) relations found in an organism for all its metabolic genes and metabolites. Using reaction stoichiometry as input, GEMs mathematically simulate metabolic reaction fluxes occurring in an organism and predict changes in the metabolic system under the relevant condition. Multiple tools and approaches in the literature can capture fluxes sensitive to a given condition by using GEMs. However, functional enrichment analysis of these reaction lists in a systems biology perspective is not straightforward. Here, we introduce RSEA to annotate given reaction sets to significantly related metabolic pathways: Reaction Set Enrichment Analysis web server tool. RSEA converts given reaction list derived from GEMs into proper reaction identifiers and statistically analyze its enrichment in metabolic pathways. RSEA is designed to provide researchers with a practical and user-friendly platform to explore and interpret sets of reactions in biological pathways and freely available online (https://rseatool.com/).

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Publication Details

Journal
Biotechnology and Bioengineering
Published
2025-05-09
DOI
https://doi.org/10.1002/bit.29020
Citations
2
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
1.61
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article

RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets

Muhammed Erkan Karabekmez, Furkan Cantürk, Merve Yarıcı, Hatice Nur Aydın et al.
2 citations
Biotechnology and Bioengineering
Microbial Metabolic Engineering and Bioproduction
1.61
article

RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets

Muhammed Erkan Karabekmez, Furkan Cantürk, Merve Yarıcı, Hatice Nur Aydın, Serdar Dursun
article en
2 citations

Abstract

Changes in biological pathways provide essential clues about metabolism. Genome-scale metabolic models (GEM) are network-based templates that computationally describe all stoichiometric associations and gene-protein reaction (GPR) relations found in an organism for all its metabolic genes and metabolites. Using reaction stoichiometry as input, GEMs mathematically simulate metabolic reaction fluxes occurring in an organism and predict changes in the metabolic system under the relevant condition. Multiple tools and approaches in the literature can capture fluxes sensitive to a given condition by using GEMs. However, functional enrichment analysis of these reaction lists in a systems biology perspective is not straightforward. Here, we introduce RSEA to annotate given reaction sets to significantly related metabolic pathways: Reaction Set Enrichment Analysis web server tool. RSEA converts given reaction list derived from GEMs into proper reaction identifiers and statistically analyze its enrichment in metabolic pathways. RSEA is designed to provide researchers with a practical and user-friendly platform to explore and interpret sets of reactions in biological pathways and freely available online (https://rseatool.com/).

Biotechnology and BioengineeringVol. 122(8)
AVL (Turkey) (TR), Özyeğin University (TR), Istanbul Medeniyet University (TR)
Openalex Percentile: Top 17%
Microbial Metabolic Engineering and Bioproduction
1.61
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