ArticleGenome biology2022
KAGE: fast alignment-free graph-based genotyping of SNPs and short indels.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Pangenome-based human genome analysis improves trait association and genomic prediction.bioRxiv : the preprint server for biology · 2026Article
- Building and applying pangenome references to capture genetic diversity.Nature reviews. Genetics · 2026Review
- k-mer-based approaches to unlock genebank genomics for targeted crop improvement.Nature genetics · 2026Review
- Pangenome-based genome inference using integer programming.Genome research · 2025Article
- Population-scale Long-read Sequencing in themedRxiv : the preprint server for health sciences · 2025Article
- Review
- Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA).Molecular biology and evolution · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- An alignment- and reference-free strategy usingMycology · 2025Article
- Complexity welcome: Pangenome graphs for comprehensive population genomics.Quantitative plant biology · 2025Review
- Integer programming framework for pangenome-based genome inference.bioRxiv : the preprint server for biology · 2024Article
- Haplotype-aware sequence alignment to pangenome graphs.Genome research · 2024Article
- A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline.Genome biology · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.
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