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| Multi-omics metabolomics analízis× | RNA-seq differenciális expresszió× | |
|---|---|---|
| Tudományterület | Bioinformatika | Bioinformatika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s) | 2008–2010 (RNA-seq DE methodology established) |
| Megalkotó≠ | Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Típus≠ | Integrative computational pipeline | Quantitative genomics pipeline |
| Alapmű≠ | Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051. link ↗ | 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 | metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Kapcsolódó≠ | 5 | 6 |
| Összefoglaló≠ | Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone. | 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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