Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Multi-omik-proteomanalys× | Vägberikningsanalys× | |
|---|---|---|
| Ämnesområde | Bioinformatik | Bioinformatik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2010s (integrative multi-omics frameworks emerged ~2012–2019) | 2003–2005 |
| Upphovsperson≠ | Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Typ≠ | Integrative computational pipeline | Statistical functional annotation method |
| Ursprungskälla≠ | Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752. 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 | integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss. | 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. |
| ScholarGateDatamängd ↗ |
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