RT Journal Article SR Electronic T1 Fast genotyping of known SNPs through approximate k-mer matching JF bioRxiv FD Cold Spring Harbor Laboratory SP 063446 DO 10.1101/063446 A1 Ariya Shajii A1 Deniz Yorukoglu A1 Y. William Yu A1 Bonnie Berger YR 2016 UL http://biorxiv.org/content/early/2016/07/12/063446.abstract AB Motivation As 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).Results We introduce LAVA (Lightweight Assignment of Variant Alleles), 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 ~5GB 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 LAVA software is available at http://lava.csail.mit.edu.Contact bab{at}mit.eduSupplementary information Supplementary data are available at Bioinformatics online.