So sánh phương pháp

	Gọi biến dựa trên mạng	Variant Calling
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2017–2018	2009–2010 (modern high-throughput era)
Người khởi xướng	Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Loại	Computational genomics pipeline	Computational genomics pipeline
Công trình gốc	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.
Tên gọi khác	graph-genome variant calling, variation graph genotyping, vg-based variant calling, pangenome variant calling	SNP calling, genotyping from sequencing, mutation detection, variant detection
Liên quan	6	6
Tóm tắt	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.	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.