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时间序列全表观基因组关联研究×RNA-seq差异表达×
领域生物信息学生物信息学
方法族Process / pipelineProcess / pipeline
起源年份2010s2008–2010 (RNA-seq DE methodology established)
提出者Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010sMultiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型Longitudinal epigenomic association pipelineQuantitative genomics pipeline
开创性文献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 ↗
别名time-series EWAS, longitudinal EWAS, repeated-measures EWAS, dynamic methylation association studyRNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关36
摘要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.
ScholarGate数据集
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  2. 2 来源
  3. PUBLISHED
  1. v1
  2. 2 来源
  3. PUBLISHED

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ScholarGate方法对比: Time-series Epigenome-wide Association Study · RNA-seq Differential Expression. 于 2026-06-19 检索自 https://scholargate.app/zh/compare