Vertaile menetelmiä

	Differentiaalinen ChIP-seq-piikkien tunnistus	RNA-seq-differentiaaliekspressioanalyysi
Tieteenala	Bioinformatiikka	Bioinformatiikka
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2011-2012	2008–2010 (RNA-seq DE methodology established)
Kehittäjä	Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Tyyppi	Comparative genomic signal analysis pipeline	Quantitative genomics pipeline
Alkuperäislähde	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.	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.
Rinnakkaisnimet	differential ChIP-seq, ChIP-seq differential binding analysis, comparative peak calling, differential chromatin occupancy analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Liittyvät	6	6
Tiivistelmä	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.	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.