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ISSRseq: an extensible, low-cost, and efficient method for reduced representation sequencing

By Brandon T. Sinn, Sandra J Simon, Mathilda V Santee, Stephen P DiFazio, Nicole M Fama, Craig F. Barrett

Posted 22 Dec 2020
bioRxiv DOI: 10.1101/2020.12.21.423774

O_LIThe capability to generate densely sampled single nucleotide polymorphism (SNP) data is essential in diverse subdisciplines of biology, including crop breeding, pathology, forensics, forestry, ecology, evolution, and conservation. However, access to the expensive equipment and bioinformatics infrastructure required for genome-scale sequencing is still a limiting factor in the developing world and for institutions with limited resources. C_LIO_LIHere we present ISSRseq, a PCR-based method for reduced representation of genomic variation using simple sequence repeats as priming sites to sequence inter-simple sequence repeat (ISSR) regions. Briefly, ISSR regions are amplified with single primers, pooled, and used to construct sequencing libraries with a low-cost, efficient commercial kit, and sequenced on the Illumina platform. We also present a flexible bioinformatic pipeline that assembles ISSR loci, calls and hard filters variants, outputs data matrices in common formats, and conducts population analyses using R. C_LIO_LIUsing three angiosperm species as case studies, we demonstrate that ISSRseq is highly repeatable, necessitates only simple wet-lab skills and commonplace instrumentation, is flexible in terms of the number of single primers used, is low-cost, and can generate genomic-scale variant discovery on par with existing RRS methods that require high sample integrity and concentration. C_LIO_LIISSRseq represents a straightforward approach to SNP genotyping in any organism, and we predict that this method will be particularly useful for those studying population genomics and phylogeography of non-model organisms. Furthermore, the ease of ISSRseq relative to other RRS methods should prove useful for those conducting research in undergraduate and graduate environments, and more broadly by those lacking access to expensive instrumentation or expertise in bioinformatics. C_LI

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