Bandingkan kaedah

	Panggilan Varian Bayesian	Pemanggilan Varian
Bidang	Bioinformatik	Bioinformatik
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009	2009–2010 (modern high-throughput era)
Pengasas	Mark DePristo, Eric Banks, and the Broad Institute GATK team	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Jenis	Probabilistic genomic inference pipeline	Computational genomics pipeline
Sumber perintis	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.	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.
Alias	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection	SNP calling, genotyping from sequencing, mutation detection, variant detection
Berkaitan	6	6
Ringkasan	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.	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.