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Haplotype and Repeat Separation in Long Reads

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10834))

Abstract

Resolving the correct structure and succession of highly similar sequence stretches is one of the main open problems in genome assembly. For non haploid genomes this includes determining the sequences of the different haplotypes. For all but the smallest genomes it also involves separating different repeat instances. In this paper we discuss methods for resolving such problems in third generation long reads by classifying alignments between long reads according to whether they represent true or false read overlaps. The main problem in this context is the high error rate found in such reads, which greatly exceeds the variance between the similar regions we want to separate. Our methods can separate read classes stemming from regions with as little as \(1\%\) difference.

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Acknowledgments

We thank Gene Myers for interesting algorithmical discussions related to this paper and Shilpa Garg for advice on running WhatsHap.

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Authors and Affiliations

  1. Myers Lab, Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstraße 108, Dresden, Germany

    German Tischler-Höhle

Authors
  1. German Tischler-Höhle
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Correspondence to German Tischler-Höhle .

Editor information

Editors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Massimo Bartoletti

  2. University of Genova, Genoa, Italy

    Annalisa Barla

  3. University of Stirling, Stirling, UK

    Andrea Bracciali

  4. Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

    Gunnar W. Klau

  5. Houston Methodist Research Institute, Houston, TX, USA

    Leif Peterson

  6. University of Udine, Udine, Italy

    Alberto Policriti

  7. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

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Tischler-Höhle, G. (2019). Haplotype and Repeat Separation in Long Reads. In: Bartoletti, M., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham. https://doi.org/10.1007/978-3-030-14160-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-14160-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14159-2

  • Online ISBN: 978-3-030-14160-8

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