Methoden vergleichen

	Analyse von Kopienzahlvariationen (CNV) – Erkennung und Interpretation von CNVs	Varianten-Calling
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1998–2006	2009–2010 (modern high-throughput era)
Urheber	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Typ	Genomic structural variant detection pipeline	Computational genomics pipeline
Wegweisende Quelle	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.
Aliasnamen	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
Verwandt	6	6
Zusammenfassung	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.	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.