Salīdzināt metodes
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| Daudzomu eQTL analīze× | Daudzomu šķērsviršķu bagātības analīze× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017) | 2014–2016 (multi-omics extension of enrichment methods established ~2005) |
| Autors≠ | GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020) | Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016) |
| Tips≠ | Integrative genomic association analysis | Integrative pathway analysis pipeline |
| Pirmavots≠ | GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213. link ↗ | Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162. link ↗ |
| Citi nosaukumi | multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics | multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA |
| Saistītās≠ | 6 | 1 |
| Kopsavilkums≠ | Multi-omics eQTL analysis maps genetic variants (SNPs or structural variants) to molecular phenotypes simultaneously across multiple omics layers — transcriptome, epigenome, proteome, and metabolome — in the same cohort. By linking genotype to gene expression and then tracing those effects through downstream molecular layers, the approach reveals how genetic variation propagates through the molecular machinery of a cell, yielding mechanistic insight that no single-omics eQTL study can provide. | Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss. |
| ScholarGateDatu kopa ↗ |
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