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| Analisi Metabolomica× | Analisi di Arricchimento di Insiemi di Geni (GSEA)× | |
|---|---|---|
| Campo | Bioinformatica | Bioinformatica |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1998–2002 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Ideatore≠ | Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Tipo≠ | Quantitative omics pipeline | Functional genomics / enrichment analysis |
| Fonte seminale≠ | Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171. link ↗ | Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI ↗ |
| Alias | metabolome profiling, metabolic profiling, metabonomics, metabolite profiling | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time. | Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold. |
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