Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Одноклітинний філогенетичний аналіз× | Аналіз варіацій кількості копій× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2014-2020 (rapid development period) | 1998–2006 |
| Автор методу≠ | Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Тип≠ | Computational phylogenetic inference pipeline | Genomic structural variant detection pipeline |
| Основоположне джерело≠ | Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92. DOI ↗ | 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 ↗ |
| Інші назви | scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution. | 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. |
| ScholarGateНабір даних ↗ |
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