方法对比

	差异ChIP-seq峰值识别	ATAC-seq 分析
领域	生物信息学	遗传学
方法族	Process / pipeline	Process / pipeline
起源年份	2011-2012	2013
提出者	Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community	Jason Buenrostro, Paul Giresi & William Greenleaf
类型	Comparative genomic signal analysis pipeline	Chromatin profiling method
开创性文献	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.	Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of cell populations and tissues. Nature Methods, 10(12), 1213–1218.
别名	differential ChIP-seq, ChIP-seq differential binding analysis, comparative peak calling, differential chromatin occupancy analysis	Chromatin accessibility, Open chromatin, Accessible chromatin analysis
相关	6	2
摘要	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.	ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a method for profiling the landscape of chromatin accessibility genome-wide. Developed by Buenrostro and colleagues in 2013, ATAC-seq uses hyperactive transposase to tag open, accessible chromatin regions, enabling rapid and sensitive identification of regulatory DNA elements. ATAC-seq has become a standard technique for characterizing gene regulatory landscapes, discovering cell-type-specific regulatory elements, and inferring gene regulatory networks.