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We next investigated the number of SNPs required to unambiguously genotype individual strains. The ability to genotype samples was essentially unaffected by this level of thinning, indicating that even 192-fold multiplexing would yield sufficient SNPs for SNPmatch to accurately perform strain identification ( Supplementary Tables 1 and 2). thaliana samples in a single Illumina Hi-Seq 2,500 lane, or roughly 2X coverage) ( Data Citation 1). We thinned the data to one, three, and six million reads for each sample to test the effect of coverage (one million reads roughly corresponds to multiplexing 192 A.
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First, we used raw sequencing reads to investigate how our ability to genotype samples depends on sequencing depth.
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We validated SNPmatch using the data from the published ‘1,001 Genomes’ of A. Validating SNPmatch using the ‘1001 Genomes’ of A. thaliana ‘1,001 Genomes’ polymorphism databases. SNPmatch is a Python library which can be run on the command line, and we also developed AraGeno ( ), a simple web interface that allows users to query their own SNP data against the public A. This result clearly demonstrates the need for, and utility of, quality control by sequencing. These mistakes are currently being investigated and remedied. Using SNPmatch, a staggering 10% of our stocks were found to be mis-identified. We performed inexpensive, low-coverage sequencing of the seed stock collection by multi-plexing 192 libraries into a single Illumina sequencing lane, resulting in a median sequencing coverage of 1.8X per sample. We then used SNPmatch to perform a quality check of a lab seed stock collection. SNPmatch readily identified correct genotypes with only a few thousand random SNP markers-numbers easily achieved by any sequencing effort. We validated SNPmatch using published sequences of the A. SNPmatch implements a likelihood model to identify matching strains for a given set of markers (SNPs) in the individual. Here we present SNPmatch, a simple tool for efficiently identifying strains by matching them to a database of strain genotypes. In contrast, user-friendly tools for the analysis of sequencing data are only starting to become available. Sequencing and library preparation costs are dropping rapidly, both for reduced-representation methods like restriction-site-associated-DNA sequencing (RAD-seq) as well as whole-genome sequencing 6, 7. In principle, genotyping can be easily performed by short-read sequencing due to its high throughput and low error rates. Routine quality checks of seed stock genotypes can guard against common mistakes such as tube mislabeling or seed contamination during harvesting. The need for verifying seed stocks is clear 5. Recently, the genomes of 1,135 Arabidopsis thaliana strains were sequenced 4 and this panel (hereinafter referred to as ‘1,001 Genomes’ panel) is now widely used. These collections are expanding rapidly with the increasing number of experiments utilizing natural variation. The same problem applies to germplasm collections such as plant seed resources of elite cultivars and crops 3. For example, cell lines are frequently misidentified or contaminated, and the need for validation has long been underappreciated 1, 2. Sample contamination is an unavoidable problem when large-scale experiments are performed.