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| Badanie różnicowe całego epigenomu (Differential EWAS)× | Analiza wzbogacenia szlaków× | |
|---|---|---|
| Dziedzina | Bioinformatyka | Bioinformatyka |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2009–2011 | 2003–2005 |
| Twórca≠ | Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014) | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Typ≠ | Comparative epigenome-wide analysis | Statistical functional annotation method |
| Źródło pierwotne≠ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. 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 ↗ |
| Inne nazwy | Differential EWAS, comparative EWAS, epigenome-wide differential methylation analysis, EWAS differential design | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | A Differential Epigenome-Wide Association Study (Differential EWAS) scans hundreds of thousands of CpG methylation sites across the genome to identify those whose methylation levels differ significantly between two or more comparison groups — such as cases vs. controls, exposed vs. unexposed, or distinct developmental stages. It is the standard epigenomic analogue of a differential expression analysis but operates at the level of DNA methylation marks rather than RNA counts. | 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. |
| ScholarGateZbiór danych ↗ |
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