方法对比

	时间序列ChIP-seq峰值调用	RNA-seq差异表达
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2008–2012 (ChIP-seq); time-series extensions ~2015–2020	2008–2010 (RNA-seq DE methodology established)
提出者	ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Computational epigenomics pipeline	Quantitative genomics pipeline
开创性文献	Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.	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.
别名	longitudinal ChIP-seq analysis, dynamic ChIP-seq peak calling, time-course ChIP-seq, temporal chromatin profiling	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	5	6
摘要	Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromatin remodeler binding evolve during biological processes such as differentiation, circadian cycles, or stimulus response.	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.