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Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We validated hypeR-GEM using paired metabolomics\u2013proteomics and metabolomics\u2013transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites\u2019 annotation, demonstrating hypeR-GEM\u2019s practical utility in a real-world use case.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The hypeR-GEM R package, documentation, and workflow examples are freely available at https:\/\/github.com\/montilab\/hypeR-GEM and archived at https:\/\/doi.org\/10.5281\/zenodo.20586748.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag588","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T11:16:52Z","timestamp":1786101412000},"source":"Crossref","is-referenced-by-count":0,"title":["hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4879-8175","authenticated-orcid":false,"given":"Ziwei","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Physics, Boston University , Boston, MA 02215,","place":["United States"]},{"name":"Division of Computational Biomedicine, Boston University Chobanian & Avedisian School of Medicine , Boston, MA 02118,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Perls","sequence":"additional","affiliation":[{"name":"Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center , Boston, MA 02118,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paola","family":"Sebastiani","sequence":"additional","affiliation":[{"name":"Institute for Clinical Research and Health Policy Studies, Tufts Medical Center , Boston, MA 02111,","place":["United States"]},{"name":"Department of Medicine, School of Medicine, Tufts University , Boston, MA 02111,","place":["United States"]},{"name":"Data Intensive Study Center, Tufts University , Boston, MA 02111,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Segr\u00e8","sequence":"additional","affiliation":[{"name":"Department of Physics, Boston University , Boston, MA 02215,","place":["United States"]},{"name":"Department of Biology, Boston University , Boston, MA 02215,","place":["United States"]},{"name":"Department of Biomedical Engineering, Boston University , Boston, MA 02215,","place":["United States"]},{"name":"Bioinformatics Program, Faculty of Computing and Data Science, Boston University , Boston, MA 02215,","place":["United States"]},{"name":"Biological Design Center, Boston University , Boston, MA 02215,","place":["United 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