Porovnat metody

	Detekce variant v jednotlivých buňkách	Analýza variací počtu kopií
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2016 (Monovar; foundational single-cell SNV calling)	1998–2006
Tvůrce	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Typ	Computational genomics pipeline	Genomic structural variant detection pipeline
Původní zdroj	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.
Další názvy	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
Příbuzné	1	6
Shrnutí	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.