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Algorithms for Next-Generation Sequencing Data pp 299–325Cite as

An Efficient Approach to Merging Paired-End Reads and Incorporation of Uncertainties

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Abstract

Next-Generation Sequencing (NGS) technologies have reshaped the landscape of life sciences. The massive amount of data generated by NGS is rapidly transforming biological research from traditional wet-lab work into a data- intensive analytical discipline (Koboldt et al., Cell 155(1):27–38, 2013). The Illumina “sequencing by synthesis” technique (Mardis, Annu Rev Genomics Hum Genet 9:387–402, 2008) is one of the most popular and widely used NGS technologies.

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Acknowledgements

T.F is supported by DFG project STA/860-4. L.C, K.K and J.Z are funded by a HITS scholarship.

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Authors and Affiliations

  1. Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

    Tomáš Flouri, Jiajie Zhang, Lucas Czech, Kassian Kobert & Alexandros Stamatakis

  2. Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Alexandros Stamatakis

Authors
  1. Tomáš Flouri
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  2. Jiajie Zhang
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  3. Lucas Czech
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  4. Kassian Kobert
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  5. Alexandros Stamatakis
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Corresponding author

Correspondence to Tomáš Flouri .

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Editors and Affiliations

  1. LaTICE, Tunis, Tunisia

    Mourad Elloumi

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Flouri, T., Zhang, J., Czech, L., Kobert, K., Stamatakis, A. (2017). An Efficient Approach to Merging Paired-End Reads and Incorporation of Uncertainties. In: Elloumi, M. (eds) Algorithms for Next-Generation Sequencing Data. Springer, Cham. https://doi.org/10.1007/978-3-319-59826-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-59826-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59824-6

  • Online ISBN: 978-3-319-59826-0

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