方法对比

	变异检测	单细胞变异检测
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2009–2010 (modern high-throughput era)	2016 (Monovar; foundational single-cell SNV calling)
提出者	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues
类型	Computational genomics pipeline	Computational genomics pipeline
开创性文献	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.	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.
别名	SNP calling, genotyping from sequencing, mutation detection, variant detection	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling
相关	6	1
摘要	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.