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| Netværksbaseret variantkald× | Bayesiansk variantkald× | |
|---|---|---|
| Fagområde | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2017–2018 | 2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009 |
| Ophavsperson≠ | Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics | Mark DePristo, Eric Banks, and the Broad Institute GATK team |
| Type≠ | Computational genomics pipeline | Probabilistic genomic inference pipeline |
| Oprindelig kilde≠ | 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. DOI ↗ | 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 ↗ |
| Aliasser | 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 |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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