Abstract
In this chapter, I will guide the user through methods to find new SNP markers from expressed sequence (RNA-Seq) data, focusing on the sample preparation and also on the bioinformatic analyses needed to sort through the immense flood of data from high-throughput sequencing machines. The general steps included are as follows: sample preparation, sequencing, quality control of data, assembly, map**, SNP discovery, filtering, validation. The first few steps are traditional laboratory protocols, whereas steps following the sequencing are of bioinformatic nature. The bioinformatics described herein are by no means exhaustive, rather they serve as one example of a simple way of analyzing high-throughput sequence data to find SNP markers. Ideally, one would like to run through this protocol several times with a new dataset, while varying software parameters slightly, in order to determine the robustness of the results. The final validation step, although not described in much detail here, is also quite critical as that will be the final test of the accuracy of the assumptions made in silico.
There is a plethora of downstream applications of a SNP dataset, not covered in this chapter. For an example of a more thorough protocol also including differential gene expression and functional enrichment analyses, BLAST annotation and downstream applications of SNP markers, a good starting point could be the “Simple Fool’s Guide to population genomics via RNA-Seq,” which is available at http://sfg.stanford.edu.
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De Wit, P. (2016). SNP Discovery Using Next Generation Transcriptomic Sequencing. In: Bourlat, S. (eds) Marine Genomics. Methods in Molecular Biology, vol 1452. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3774-5_5
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DOI: https://doi.org/10.1007/978-1-4939-3774-5_5
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