Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Time-series Epigenome-wide Association Study× | Анализ дифференциальной экспрессии РНК-сек (DE)× | |
|---|---|---|
| Область | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2010s | 2008–2010 (RNA-seq DE methodology established) |
| Автор метода≠ | Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Тип≠ | Longitudinal epigenomic association pipeline | Quantitative genomics pipeline |
| Основополагающий источник≠ | Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., ... & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208. 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 | time-series EWAS, longitudinal EWAS, repeated-measures EWAS, dynamic methylation association study | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Связанные≠ | 3 | 6 |
| Сводка≠ | A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematically over time, or to characterise how epigenetic associations with an exposure or phenotype evolve across developmental stages, treatment periods, or disease trajectories. | 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 ↗ |
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