Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza Variațiilor Numărului de Copii la Nivel de Celulă Unică× | Analiza variației numărului de copii× | |
|---|---|---|
| Domeniu | Bioinformatică | Bioinformatică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2011–2015 | 1998–2006 |
| Autorul original≠ | Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Tip≠ | Computational genomics pipeline | Genomic structural variant detection pipeline |
| Sursa seminală≠ | 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. link ↗ | 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 ↗ |
| Denumiri alternative | scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. | 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. |
| ScholarGateSet de date ↗ |
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