IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v264y2018i3p799-812.html
   My bibliography  Save this article

Graph algorithms for DNA sequencing – origins, current models and the future

Author

Listed:
  • Blazewicz, Jacek
  • Kasprzak, Marta
  • Kierzynka, Michal
  • Frohmberg, Wojciech
  • Swiercz, Aleksandra
  • Wojciechowski, Pawel
  • Zurkowski, Piotr

Abstract

With the ubiquitous presence of next-generation sequencing in modern biological, genetic, pharmaceutical and medical research, not everyone pays attention to the underlying computational methods. Even fewer researchers know what were the origins of the current models for DNA assembly. We present original graph models used in DNA sequencing by hybridization, discuss their properties and connections between them. We also explain how these graph models evolved to adapt to the characteristics of next-generation sequencing. Moreover, we present a practical comparison of state-of-the-art DNA de novo assembly tools representing these transformed models, i.e. overlap and decomposition-based graphs. Even though the competition is tough, some assemblers perform better and certainly large differences may be observed in hardware resources utilization. Finally, we outline the most important trends in the sequencing field, and try to predict their impact on the computational models in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:264:y:2018:i:3:p:799-812
    DOI: 10.1016/j.ejor.2016.06.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716304830
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.06.043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blum, Christian & Lozano, José A. & Davidson, Pinacho, 2015. "Mathematical programming strategies for solving the minimum common string partition problem," European Journal of Operational Research, Elsevier, vol. 242(3), pages 769-777.
    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. Caroline B. Albertin & Oleg Simakov & Therese Mitros & Z. Yan Wang & Judit R. Pungor & Eric Edsinger-Gonzales & Sydney Brenner & Clifton W. Ragsdale & Daniel S. Rokhsar, 2015. "The octopus genome and the evolution of cephalopod neural and morphological novelties," Nature, Nature, vol. 524(7564), pages 220-224, August.
    4. Jacek Blazewicz & Ceyda Oguz & Aleksandra Swiercz & Jan Weglarz, 2006. "DNA Sequencing by Hybridization via Genetic Search," Operations Research, INFORMS, vol. 54(6), pages 1185-1192, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Olacir R. Castro & Gian Mauricio Fritsche & Aurora Pozo, 2018. "Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization," Journal of Heuristics, Springer, vol. 24(4), pages 581-616, August.
    3. Blum, Christian & Ochoa, Gabriela, 2021. "A comparative analysis of two matheuristics by means of merged local optima networks," European Journal of Operational Research, Elsevier, vol. 290(1), pages 36-56.
    4. Neil D. Young & Andreas J. Stroehlein & Tao Wang & Pasi K. Korhonen & Margaret Mentink-Kane & J. Russell Stothard & David Rollinson & Robin B. Gasser, 2022. "Nuclear genome of Bulinus truncatus, an intermediate host of the carcinogenic human blood fluke Schistosoma haematobium," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Hannah Schmidbaur & Akane Kawaguchi & Tereza Clarence & Xiao Fu & Oi Pui Hoang & Bob Zimmermann & Elena A. Ritschard & Anton Weissenbacher & Jamie S. Foster & Spencer V. Nyholm & Paul A. Bates & Carol, 2022. "Emergence of novel cephalopod gene regulation and expression through large-scale genome reorganization," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Ruth Styfhals & Grygoriy Zolotarov & Gert Hulselmans & Katina I. Spanier & Suresh Poovathingal & Ali M. Elagoz & Seppe Winter & Astrid Deryckere & Nikolaus Rajewsky & Giovanna Ponte & Graziano Fiorito, 2022. "Cell type diversity in a developing octopus brain," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    7. Caroline B. Albertin & Sofia Medina-Ruiz & Therese Mitros & Hannah Schmidbaur & Gustavo Sanchez & Z. Yan Wang & Jane Grimwood & Joshua J. C. Rosenthal & Clifton W. Ragsdale & Oleg Simakov & Daniel S. , 2022. "Genome and transcriptome mechanisms driving cephalopod evolution," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Lale Özbakır & Gökhan Seçme, 2022. "A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs," Operational Research, Springer, vol. 22(1), pages 577-614, March.
    9. Blazewicz, Jacek & Burke, Edmund K. & Kasprzak, Marta & Kovalev, Alexandr & Kovalyov, Mikhail Y., 2011. "The simplified partial digest problem: Approximation and a graph-theoretic model," European Journal of Operational Research, Elsevier, vol. 208(2), pages 142-152, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:264:y:2018:i:3:p:799-812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.