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We present MetaProViz (\n                    <jats:bold>Meta<\/jats:bold>\n                    bolomics\n                    <jats:bold>Pr<\/jats:bold>\n                    ocessing, functi\n                    <jats:bold>o<\/jats:bold>\n                    nal analysis and\n                    <jats:bold>Vi<\/jats:bold>\n                    suali\n                    <jats:bold>z<\/jats:bold>\n                    ation), an open-source Bioconductor R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/saezlab.github.io\/MetaProViz\/\" ext-link-type=\"uri\">https:\/\/saezlab.github.io\/MetaProViz\/<\/jats:ext-link>\n                    ). MetaProViz operates on annotated intensity values and offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to intracellular and exometabolomics experiments. To improve functional analysis, we created the\n                    <jats:bold>Met<\/jats:bold>\n                    abolism\n                    <jats:bold>Sig<\/jats:bold>\n                    nature\n                    <jats:bold>D<\/jats:bold>\n                    ata\n                    <jats:bold>b<\/jats:bold>\n                    ase (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes pathway-metabolite, metabolite-receptor, metabolite-transporter sets, and chemical class-metabolite sets. MetaProViz enables the conversion of gene sets to metabolite sets, metabolite identifier expansion and analyses mapping ambiguities. The MetaProViz functional analysis toolkit includes sample metadata analysis, enrichment analysis and biologically informed clustering. By\u00a0applying MetaProViz to kidney cancer metabolomics data, we identified increased methionine usage in line with decreased methionine levels in tumour samples. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data.\n                  <\/jats:p>","DOI":"10.1038\/s44320-026-00231-8","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:29:21Z","timestamp":1786976961000},"page":"1605-1625","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz"],"prefix":"10.1038","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3867-0881","authenticated-orcid":false,"given":"Christina","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2460-2841","authenticated-orcid":false,"given":"Jannik","family":"Franken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-9379","authenticated-orcid":false,"given":"Denes","family":"Turei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Prymidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Macabe","family":"Daley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3293-7397","authenticated-orcid":false,"given":"Christian","family":"Frezza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8552-8976","authenticated-orcid":false,"given":"Julio","family":"Saez-Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"231_CR1","doi-asserted-by":"publisher","first-page":"D679","DOI":"10.1093\/nar\/gkad960","volume":"52","author":"A Agrawal","year":"2024","unstructured":"Agrawal A, Balci H, Hanspers K, Coort SL, Martens M, Slenter DN, Ehrhart F, Digles D, Waagmeester A, Wassink I et al (2024) WikiPathways 2024: next generation pathway database. 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