Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ метаболома× | Анализ дифференциальной экспрессии РНК-сек (DE)× | |
|---|---|---|
| Область | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1998–2002 | 2008–2010 (RNA-seq DE methodology established) |
| Автор метода≠ | Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Тип≠ | Quantitative omics pipeline | Quantitative genomics pipeline |
| Основополагающий источник≠ | Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171. 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 ↗ |
| Другие названия | metabolome profiling, metabolic profiling, metabonomics, metabolite profiling | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Связанные | 6 | 6 |
| Сводка≠ | Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time. | 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. |
| ScholarGateНабор данных ↗ |
|
|