Salīdzināt metodes

	Kopiju skaita variāciju analīze	Vienšūnu kopiju skaita variācijas analīze
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1998–2006	2011–2015
Autors	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)
Tips	Genomic structural variant detection pipeline	Computational genomics pipeline
Pirmavots	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.	Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.
Citi nosaukumi	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis
Saistītās	6	6
Kopsavilkums	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.	Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.