dc.contributor.author | Grytten, Ivar | |
dc.contributor.author | Dagestad Rand, Knut | |
dc.contributor.author | Sandve, Geir K. | |
dc.date.accessioned | 2022-10-11T05:03:10Z | |
dc.date.available | 2022-10-11T05:03:10Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Genome Biology. 2022 Oct 04;23(1):209 | |
dc.identifier.uri | http://hdl.handle.net/10852/97159 | |
dc.description.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. | |
dc.language.iso | eng | |
dc.rights | The Author(s); licensee BioMed Central Ltd. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | KAGE: fast alignment-free graph-based genotyping of SNPs and short indels | |
dc.type | Journal article | |
dc.date.updated | 2022-10-11T05:03:11Z | |
dc.creator.author | Grytten, Ivar | |
dc.creator.author | Dagestad Rand, Knut | |
dc.creator.author | Sandve, Geir K. | |
dc.identifier.doi | https://doi.org/10.1186/s13059-022-02771-2 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.type.version | PublishedVersion | |
cristin.articleid | 209 | |