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| Anomenament de variants× | Anàlisi de Variació del Nombre de Còpies× | |
|---|---|---|
| Camp | Bioinformàtica | Bioinformàtica |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 2009–2010 (modern high-throughput era) | 1998–2006 |
| Autor original≠ | Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Tipus≠ | Computational genomics pipeline | Genomic structural variant detection pipeline |
| Font seminal≠ | 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. DOI ↗ | Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454. DOI ↗ |
| Àlies | SNP calling, genotyping from sequencing, mutation detection, variant detection | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Relacionats | 6 | 6 |
| Resum≠ | 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. | 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. |
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