Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse des QTL d'expression× | Analyse d'enrichissement de jeux de gènes (GSEA)× | |
|---|---|---|
| Domaine | Bio-informatique | Bio-informatique |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2001 (term coined); widely adopted after 2005 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Auteur d'origine≠ | Ritsert C. Jansen & Jan-Peter Nap | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Type≠ | Association mapping method | Functional genomics / enrichment analysis |
| Source fondatrice≠ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ | 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 | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions. | 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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