Evidence map›Paper›PMID 38656970›Full record

ArticleBioinformatics (Oxford, England)2024

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

Johanna Galvis, Joris Guyon, Benjamin Dartigues, Helge Hecht, Björn Grüning, Florian Specque, Hayssam Soueidan, Slim Karkar, Thomas Daubon, Macha Nikolski

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Johanna GalvisUniversity of Bordeaux, CNRS, IBGC UMR 5095, Bordeaux, France.
Joris GuyonUniversity of Bordeaux, INSERM, BPH U1219, Bordeaux, France.
Benjamin DartiguesUniversity of Bordeaux, Bordeaux Bioinformatics Center CBiB, Bordeaux, France.
Helge HechtRECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.
Björn GrüningGalaxy Europe, University of Freiburg, Freiburg, Baden-Württemberg, Germany.
Florian SpecqueUniversity of Bordeaux, CNRS, IBGC UMR 5095, Bordeaux, France.ORCID 0000-0002-4449-6381
Hayssam SoueidanUniversity of Bordeaux, Bordeaux Bioinformatics Center CBiB, Bordeaux, France.
Slim KarkarUniversity of Bordeaux, CNRS, IBGC UMR 5095, Bordeaux, France.ORCID 0000-0003-0501-3137
Thomas DaubonUniversity of Bordeaux, CNRS, IBGC UMR 5095, Bordeaux, France.
Macha NikolskiUniversity of Bordeaux, CNRS, IBGC UMR 5095, Bordeaux, France.ORCID 0000-0001-6059-577X

Funding

Institut National du Cancer
6 · The paper itself

Abstract

motivationMany 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' 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.

resultsIn 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. AVAILABILITY AND IMPLEMENTATION: 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.

Indexed as

Isotope LabelingMetabolomicsSoftwareCell Line, TumorGlioblastomaHumans

Identifiers

PMID38656970
PMCPMC11109473

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.