{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T20:08:44Z","timestamp":1779394124135,"version":"3.53.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006364","name":"Institut National du Cancer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006364","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells\u2019 metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>DIMet is freely available at https:\/\/github.com\/cbib\/DIMet, and through https:\/\/usegalaxy.eu and https:\/\/workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https:\/\/zenodo.org\/records\/10925786.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae282","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T17:24:02Z","timestamp":1713979442000},"source":"Crossref","is-referenced-by-count":3,"title":["DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data"],"prefix":"10.1093","volume":"40","author":[{"given":"Johanna","family":"Galvis","sequence":"first","affiliation":[{"name":"University of Bordeaux, CNRS, IBGC UMR 5095 , Bordeaux, France"},{"name":"University of Bordeaux, Bordeaux Bioinformatics Center CBiB , Bordeaux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joris","family":"Guyon","sequence":"additional","affiliation":[{"name":"University of Bordeaux, INSERM, BPH U1219 , Bordeaux, France"},{"name":"Medical Pharmacology Department, Bordeaux University Hospital , 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