ArticleScientific data2021
ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- Datasets of 16S rRNA gene amplicon sequences, metabolites, and soluble immune components in bronchoalveolar lavage samples from severe asthmatic and age-matched control children.Data in brief · 2026Article
- Effective data visualization strategies in untargeted metabolomics.Natural product reports · 2025Review
- Hyperammonemia induces programmed liver cell death.Science advances · 2025Article
- Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review.European journal of drug metabolism and pharmacokinetics · 2024Review
- Anti-hyperuricemia effect of Clerodendranthus spicatus: a molecular biology study combined with metabolomics.Scientific reports · 2024Article
- Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Article
- Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation.Metabolites · 2023Article
- Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.Journal of proteome research · 2022Article
- ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.Scientific data · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.
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Registered trials
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.