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| Analyse protéomique multi-omique× | Analyse Protéomique× | |
|---|---|---|
| Domaine | Bio-informatique | Bio-informatique |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2010s (integrative multi-omics frameworks emerged ~2012–2019) | 1994–2003 (term coined 1994; shotgun proteomics established early 2000s) |
| Auteur d'origine≠ | Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work | Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations) |
| Type≠ | Integrative computational pipeline | Quantitative omics pipeline |
| Source fondatrice≠ | Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752. DOI ↗ | Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50. link ↗ |
| Alias | integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics | proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics |
| Apparentées | 6 | 6 |
| Résumé≠ | Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss. | Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology. |
| ScholarGateJeu de données ↗ |
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