방법 비교

	차등 ChIP-seq 피크 호출	유전자 집합 농축 분석 (GSEA)
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2011-2012	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
창시자	Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
유형	Comparative genomic signal analysis pipeline	Functional genomics / enrichment analysis
원전	Ross-Innes, C. S., Stark, R., Teschendorff, A. E., Holmes, K. A., Ali, H. R., Dunning, M. J., Brown, G. D., Gojis, O., Ellis, I. O., Green, A. R., Ali, S., Chin, S. F., Palmieri, C., Caldas, C., & Carroll, J. S. (2012). Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature, 481(7381), 389-393.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
별칭	differential ChIP-seq, ChIP-seq differential binding analysis, comparative peak calling, differential chromatin occupancy analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
관련	6	5
요약	Differential ChIP-seq peak calling identifies genomic loci where a protein of interest — typically a transcription factor or histone mark — shows significantly altered binding or occupancy between two or more biological conditions. By combining standard ChIP-seq peak detection with count-based statistical testing, the method reveals condition-specific regulatory elements, providing a genome-wide map of dynamic chromatin interactions underlying cellular state changes.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.