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| Analiza eQTL× | Analiza wzbogacenia szlaków× | |
|---|---|---|
| Dziedzina | Bioinformatyka | Bioinformatyka |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2001 (term coined); widely adopted after 2005 | 2003–2005 |
| Twórca≠ | Ritsert C. Jansen & Jan-Peter Nap | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Typ≠ | Association mapping method | Statistical functional annotation method |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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. | Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments. |
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