방법 비교

	RNA-seq 차등 발현	ChIP-seq Peak Calling
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2008–2010 (RNA-seq DE methodology established)	2007–2008
창시자	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)	Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)
유형	Quantitative genomics pipeline	Computational genomics pipeline
원전	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.	Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137.
별칭	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA	ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification
관련	6	6
요약	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.	ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms.