Сравнение на методи

	Анализ на вариациите в броя на копията, базиран на мрежи	Вариантно призоваване
Област	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2011–2015	2009–2010 (modern high-throughput era)
Създател	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Тип	Computational network analysis pipeline	Computational genomics pipeline
Основополагащ източник	Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.	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.
Други названия	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
Свързани	6	6
Резюме	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.	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.