Abstract
The comparatively low cost of massive parallel sequencing technology, also known as next-generation sequencing (NGS), has transformed the isolation of microsatellite loci. The most common NGS approach consists of obtaining large amounts of sequence data from genomic DNA or enriched microsatellite libraries, which is then mined for the discovery of microsatellite repeats using bioinformatics analyses. Here, we describe a bioinformatics approach to isolate microsatellite loci, starting from the raw sequence data through a subset of microsatellite primer pairs. The primary difference to previously published approaches includes analyses to select the most accurate sequence data and to eliminate repetitive elements prior to the design of primers. These analyses aim to minimize the testing of primer pairs by identifying the most promising microsatellite loci.
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Acknowledgments
We thank all the members of the ToBo and Karl labs and the Hawai’i Institute of Marine Biology EPSCoR core genetics facility and staff for feedback, discussion, and assistance with this protocol. This project was funded by a Fullbright Fellowship award to I.F.S. and National Science Foundation grants (Bio OCE-0623699, OCE-0929031) to R.J.T. and B.W.B. This is contribution #1521 from the Hawai’i Institute of Marine Biology and 8755 from the School of Ocean and Earth Sciences and Technology (SOEST).
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Fernandez-Silva, I., Toonen, R.J. (2013). Optimizing Selection of Microsatellite Loci from 454 Pyrosequencing via Post-sequencing Bioinformatic Analyses. In: Kantartzi, S. (eds) Microsatellites. Methods in Molecular Biology, vol 1006. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-389-3_7
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DOI: https://doi.org/10.1007/978-1-62703-389-3_7
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