方法对比

	基于网络的拷贝数变异分析	变异检测
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2011–2015	2009–2010 (modern high-throughput era)
提出者	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
类型	Computational network analysis pipeline	Computational genomics pipeline
开创性文献	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.
别名	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
相关	6	6
摘要	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.