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Identification of Variant Compositions in Related Strains Without Reference

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Algorithms for Computational Biology (AlCoB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9702))

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Abstract

Current DNA sequencing technologies do not read an entire chromosome from end to end but instead produce sets of short reads, i.e. fragments of the genome. Haplotype assembly is the problem of assigning each read to the correct chromosome in the set of chromosomes in a homologous group, with the aid of the reference sequence. In this paper, we extend an existing exact algorithm for haplotype assembly of diploid species (Patterson et al., 2014) to the reference-free, polyploid case. A reference-free method does not exploit a reference genomic sequence of a species and thus we cannot exploit a known linear order for the reads and resulting variant positions. Therefore we obtain an unordered variant composition as a result. This setting can be also applied to the study of relative abundances of related bacterial strains.

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Acknowledgements

This work was supported in part by the Academy of Finland (grants 267591 to L.S. and 284598 (CoECGR)).

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Authors and Affiliations

  1. Department of Computer Science, Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland

    Mikko Rautiainen, Leena Salmela & Veli Mäkinen

Authors
  1. Mikko Rautiainen
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  2. Leena Salmela
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  3. Veli Mäkinen
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Corresponding author

Correspondence to Veli Mäkinen .

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Editors and Affiliations

  1. CETA-Ciemat, Trujillo, Spain

    María Botón-Fernández

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Extremadura, Cáceres, Spain

    Sergio Santander-Jiménez

  4. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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Rautiainen, M., Salmela, L., Mäkinen, V. (2016). Identification of Variant Compositions in Related Strains Without Reference. In: Botón-Fernández, M., Martín-Vide, C., Santander-Jiménez, S., Vega-Rodríguez, M.A. (eds) Algorithms for Computational Biology. AlCoB 2016. Lecture Notes in Computer Science(), vol 9702. Springer, Cham. https://doi.org/10.1007/978-3-319-38827-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-38827-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38826-7

  • Online ISBN: 978-3-319-38827-4

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