Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Variant Calling× | Apelarea variantelor la nivel de celulă unică× | |
|---|---|---|
| Domeniu | Bioinformatică | Bioinformatică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2009–2010 (modern high-throughput era) | 2016 (Monovar; foundational single-cell SNV calling) |
| Autorul original≠ | Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010) | Hamim Zafar, Ken Chen, Nicholas Navin and colleagues |
| Tip | Computational genomics pipeline | Computational genomics pipeline |
| Sursa seminală≠ | McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303. DOI ↗ | 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. DOI ↗ |
| Denumiri alternative | SNP calling, genotyping from sequencing, mutation detection, variant detection | scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling |
| Înrudite≠ | 6 | 1 |
| Rezumat≠ | Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications. | 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. |
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