Methoden vergelijken

	Analyse van kopienummervariatie	RNA-seq Differentiele Expressie
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1998–2006	2008–2010 (RNA-seq DE methodology established)
Grondlegger	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Type	Genomic structural variant detection pipeline	Quantitative genomics pipeline
Oorspronkelijke bron	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.	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.
Aliassen	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Verwant	6	6
Samenvatting	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.	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.