Порівняння методів

	Мережевий виклик варіантів	Байєсівський виклик варіантів
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2017–2018	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009
Автор методу	Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics	Mark DePristo, Eric Banks, and the Broad Institute GATK team
Тип	Computational genomics pipeline	Probabilistic genomic inference pipeline
Основоположне джерело	Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology, 36(9), 875–879.	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.
Інші назви	graph-genome variant calling, variation graph genotyping, vg-based variant calling, pangenome variant calling	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection
Пов'язані	6	6
Підсумок	Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and structural variants with substantially lower reference bias than linear-reference pipelines. Key tools include the Variation Graph Toolkit (vg) and Graphtyper.	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.