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GRASShopPER—An algorithm for de novo assembly based on GPU alignments

Author

Listed:
  • Aleksandra Swiercz
  • Wojciech Frohmberg
  • Michal Kierzynka
  • Pawel Wojciechowski
  • Piotr Zurkowski
  • Jan Badura
  • Artur Laskowski
  • Marta Kasprzak
  • Jacek Blazewicz

Abstract

Next generation sequencers produce billions of short DNA sequences in a massively parallel manner, which causes a great computational challenge in accurately reconstructing a genome sequence de novo using these short sequences. Here, we propose the GRASShopPER assembler, which follows an approach of overlap-layout-consensus. It uses an efficient GPU implementation for the sequence alignment during the graph construction stage and a greedy hyper-heuristic algorithm at the fork detection stage. A two-part fork detection method allows us to identify repeated fragments of a genome and to reconstruct them without misassemblies. The assemblies of data sets of bacteria Candidatus Microthrix, nematode Caenorhabditis elegans, and human chromosome 14 were evaluated with the golden standard tool QUAST. In comparison with other assemblers, GRASShopPER provided contigs that covered the largest part of the genomes and, at the same time, kept good values of other metrics, e.g., NG50 and misassembly rate.

Suggested Citation

  • Aleksandra Swiercz & Wojciech Frohmberg & Michal Kierzynka & Pawel Wojciechowski & Piotr Zurkowski & Jan Badura & Artur Laskowski & Marta Kasprzak & Jacek Blazewicz, 2018. "GRASShopPER—An algorithm for de novo assembly based on GPU alignments," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    • Handle: RePEc:plo:pone00:0202355
    DOI: 10.1371/journal.pone.0202355
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    References listed on IDEAS

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    1. Blazewicz, Jacek & Kasprzak, Marta & Kierzynka, Michal & Frohmberg, Wojciech & Swiercz, Aleksandra & Wojciechowski, Pawel & Zurkowski, Piotr, 2018. "Graph algorithms for DNA sequencing – origins, current models and the future," European Journal of Operational Research, Elsevier, vol. 264(3), pages 799-812.
    2. Jacek Blazewicz & Edmund Burke & Graham Kendall & Wojciech Mruczkiewicz & Ceyda Oguz & Aleksandra Swiercz, 2013. "A hyper-heuristic approach to sequencing by hybridization of DNA sequences," Annals of Operations Research, Springer, vol. 207(1), pages 27-41, August.
    3. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    4. Edmund K. Burke & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2010. "A Classification of Hyper-heuristic Approaches," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 449-468, Springer.
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