Methoden vergleichen

	Netzwerkbasierte Analyse von Kopienzahlvariationen	Analyse von Kopienzahlvariationen (CNV) – Erkennung und Interpretation von CNVs
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2011–2015	1998–2006
Urheber	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Typ	Computational network analysis pipeline	Genomic structural variant detection pipeline
Wegweisende Quelle	Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Aliasnamen	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Verwandt	6	6
Zusammenfassung	Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies.	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.