Abstract
In the past decade and a half, different companies began developing and constantly improving a variety of high-throughput sequencing (HTS) platforms, which delivered large volumes of genomic information, improving sequencing accuracy at a cost that is progressively lowering compared to previous technologies like Sanger sequencing, aiming the 1000$ genome milestone (with all the implications it would have for personal medicine by making affordable individual genome sequencing). Parallel to this, the necessity of efficient data analysis and handling promoted biostatistics and bioinformatics evolution, to assume the handling of those big data volumes. Currently, HTS technique approaches are a trendy way to carry out a variety of experiments that have allowed gaining insight of multiple features of biological systems, their regulation and evolution, which were inaccessible until these technologies arrived.
This book is conceived to act as source of guidance through the increasing number of HTS-related techniques, enhancing their use, by bringing up a series of guidelines/recommendations that should help researchers to choose the best planning options for their HTS experiments from the wet laboratory to the in silico interpretation, so that they are able to select the best approach for testing their hypotheses and answer biological questions.
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Acknowledgments
We are grateful to Springer for giving us the opportunity of making this HTS guideline idea a reality.
This work has been supported by The Department of Industry, Tourism, and Trade of the Government of the Autonomous Community of the Basque Country (Etortek Research Programs 2013–2015) and from the Innovation Technology Department of the Bizkaia County.
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Lavín Trueba, J.L., Aransay, A.M. (2016). The High-Throughput Sequencing Technologies Triple-W Discussion: Why Use HTS, What Is the Optimal HTS Method to Use, and Which Data Analysis Workflow to Follow. In: Aransay, A., Lavín Trueba, J. (eds) Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing. Springer, Cham. https://doi.org/10.1007/978-3-319-31350-4_1
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DOI: https://doi.org/10.1007/978-3-319-31350-4_1
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