Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Variación del Número de Copias× | Análisis de variación del número de copias en células individuales× | |
|---|---|---|
| Campo | Bioinformática | Bioinformática |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1998–2006 | 2011–2015 |
| Autor original≠ | 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) |
| Tipo≠ | Genomic structural variant detection pipeline | Computational genomics pipeline |
| Fuente seminal≠ | 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 ↗ | 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 ↗ |
| Alias | 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 |
| Relacionados | 6 | 6 |
| Resumen≠ | 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. |
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