Comparar métodos

	Chamada de variantes em célula única	Análise de Variação do Número de Cópias
Área	Bioinformática	Bioinformática
Família	Process / pipeline	Process / pipeline
Ano de origem	2016 (Monovar; foundational single-cell SNV calling)	1998–2006
Autor original	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Tipo	Computational genomics pipeline	Genomic structural variant detection pipeline
Fonte seminal	Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Outros nomes	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Relacionados	1	6
Resumo	Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.	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.