Methoden vergelijken

	Bayesiaanse variant calling	Single-cell variant calling
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009	2016 (Monovar; foundational single-cell SNV calling)
Grondlegger	Mark DePristo, Eric Banks, and the Broad Institute GATK team	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues
Type	Probabilistic genomic inference pipeline	Computational genomics pipeline
Oorspronkelijke bron	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.
Aliassen	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling
Verwant	6	1
Samenvatting	Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based callers, Bayesian approaches explicitly model sequencing error, mapping uncertainty, and prior genotype frequencies to produce calibrated genotype likelihoods that can be used for downstream filtering and association testing.	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.