Comparar métodos

	Llamada de variantes basada en red	Análisis de Variación del Número de Copias
Campo	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline
Año de origen	2017–2018	1998–2006
Autor original	Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Tipo	Computational genomics pipeline	Genomic structural variant detection pipeline
Fuente seminal	Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology, 36(9), 875–879.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Alias	graph-genome variant calling, variation graph genotyping, vg-based variant calling, pangenome variant calling	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Relacionados	6	6
Resumen	Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and structural variants with substantially lower reference bias than linear-reference pipelines. Key tools include the Variation Graph Toolkit (vg) and Graphtyper.	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.