방법 비교

	ChIP-seq Peak Calling	전장 후성유전체 연관 분석 (EWAS)
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2007–2008	2008–2011 (term and framework established c. 2011)
창시자	Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)	Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application
유형	Computational genomics pipeline	Population-scale epigenomic association study
원전	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.	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.
별칭	ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification	EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study
관련	6	5
요약	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.	An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.