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| Multi-omikai proteomikai analízis× | RNA-seq differenciális expresszió× | |
|---|---|---|
| Tudományterület | Bioinformatika | Bioinformatika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 2010s (integrative multi-omics frameworks emerged ~2012–2019) | 2008–2010 (RNA-seq DE methodology established) |
| Megalkotó≠ | Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Típus≠ | Integrative computational pipeline | Quantitative genomics pipeline |
| Alapmű≠ | 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 ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| Alternatív nevek | integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateAdatkészlet ↗ |
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