Abstract
Although genetic techniques are moving toward collecting massive amounts of genome-wide data through genome-scans, microsatellite markers (µsats) still provide a simple and cost-effective method for key applications such as parentage analyses, pedigree tracking, assessing likelihoods of disease conditions and DNA fingerprinting, among others. Newer laboratory protocols using high throughput sequencing platforms can now generate µsat data more efficiently than ever before. Yet, there is a dearth of easy to use, interactive software reliably converting raw sequencing data into individual-based multi-locus µsat genotypes suitable for typical downstream analyses. We describe the development and application of NGS-µsat, an R-based software workflow capable of converting raw µsat sequence data produced using next-generation sequencing platforms into multi-locus genotypes. Because the algorithm identifies repeat motifs, it does not rely on identifying and removing extraneous sequence fragments from sequenced reads to score loci. Accordingly, the software scores ‘true’ µsat repeats and provides an accurate, and clean picture of locus information without the typical assessment ambiguity based on fragment lengths. In comparative analyses, results show that NGS-µsat leads to cleaner, more reliable genotypes that are more repeatable than those made by scoring the same data using other software based on fragment lengths. This increased reliability/reproducibility of generated data may expand the use of high throughput sequencing-based techniques to routine DNA profiling, DNA fingerprinting and parentage/pedigree analyses and revitalise the application of µsats more broadly.
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Data availability
The raw fasta files and scores for all individuals used in this study are stored and accessible through LabArchives at the following https://doi.org/10.25833/g5gp-1703.
Availability and requirements
Project home page: https://github.com/denisroy1/NGS-usat.
Operating system(s): Platform independent.
Programming language: R.
Other requirements: Bioconductor, XQuartz.
License: GNU General Public License v 3.0.
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Acknowledgments
The authors would like to thank Shawna Semple (University of Waterloo) for help testing code, Kyle Wellband (IBIS Université Laval) for help in logic based peak searches, Hans W. Borchers (CRAN Senior Developer) for pracma code, and Yellow Island Aquaculture (YIAL) for experimentation facilities and salmon resources. Wild fish were seined by the Quinsam River Hatchery and collected under Permit No. 12279 issued by Fisheries and Oceans Canada.
Funding
This work was supported by Natural Sciences and Engineering Research Council Operating Grant 814014 awarded to DDH. The work supported the development of the sequencing protocols and stipends for some students/researchers involved for code development.
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DH, DR and SL developed the concept and protocols for experimentation. SL performed experimentations and prepared DNA libraries for sequencing. DR and SL developed the software and drafted the manuscript. DR, SL and CJV scored the data and ran the various analyses. RW revised the manuscript and provided input for µsat literature review. All authors revised and wrote the final draft of the manuscript.
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The authors declare no competing interest in this work.
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The use of Chinook salmon in this research adhered to the ethical treatment of animals as mandated by the Animal Care Committee at the University of Windsor and in accordance with the Canadian Council for Animal Care.
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Roy, D., Lehnert, S.J., Venney, C.J. et al. NGS-μsat: bioinformatics framework supporting high throughput microsatellite genotyping from next generation sequencing platforms. Conservation Genet Resour 13, 161–173 (2021). https://doi.org/10.1007/s12686-020-01186-0
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DOI: https://doi.org/10.1007/s12686-020-01186-0