ArticleBioinformatics (Oxford, England)2016
Fast genotyping of known SNPs through approximate k-mer matching.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 70 citations in OpenAlex.
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- Effects of spaced k-mers on alignment-free genotyping.Bioinformatics (Oxford, England) · 2023Article
- Matchtigs: minimum plain text representation of k-mer sets.Genome biology · 2023Article
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- Pitfalls of genotyping microbial communities with rapidly growing genome collections.Cell systems · 2023Article
- Theory of local k-mer selection with applications to long-read alignment.Bioinformatics (Oxford, England) · 2022Article
- KAGE: fast alignment-free graph-based genotyping of SNPs and short indels.Genome biology · 2022Article
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- Fast and accurate metagenotyping of the human gut microbiome with GT-Pro.Nature biotechnology · 2022Article
- Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes.Nature genetics · 2022Article
- Fast alignment of reads to a variation graph with application to SNP detection.Journal of integrative bioinformatics · 2021Article
- Nebula: ultra-efficient mapping-free structural variant genotyper.Nucleic acids research · 2021Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
motivationAs the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS).
resultsWe introduce lightweight assignment of variant alleles (LAVA), an NGS-based genotyping algorithm for a given set of SNP loci, which takes advantage of the fact that approximate matching of mid-size k-mers (with k = 32) can typically uniquely identify loci in the human genome without full read alignment. LAVA accurately calls the vast majority of SNPs in dbSNP and Affymetrix's Genome-Wide Human SNP Array 6.0 up to about an order of magnitude faster than standard NGS genotyping pipelines. For Affymetrix SNPs, LAVA has significantly higher SNP calling accuracy than existing pipelines while using as low as ∼5 GB of RAM. As such, LAVA represents a scalable computational method for population-level genotyping studies as well as a flexible NGS-based replacement for SNP arrays. AVAILABILITY AND IMPLEMENTATION: LAVA software is available at http://lava.csail.mit.edu CONTACT: bab@mit.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.