IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0178751.html
   My bibliography  Save this article

DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads

Author

Listed:
  • Vladimír Boža
  • Broňa Brejová
  • Tomáš Vinař

Abstract

The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the accuracy of base calling is much dependent on the underlying software and can be improved by considering modern machine learning methods. By employing carefully crafted recurrent neural networks, our tool significantly improves base calling accuracy on data from R7.3 version of the platform compared to the default base caller supplied by the manufacturer. On R9 version, we achieve results comparable to Nanonet base caller provided by Oxford Nanopore. Availability of an open source tool with high base calling accuracy will be useful for development of new applications of the MinION device, including infectious disease detection and custom target enrichment during sequencing.

Suggested Citation

  • Vladimír Boža & Broňa Brejová & Tomáš Vinař, 2017. "DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0178751
    DOI: 10.1371/journal.pone.0178751
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178751
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178751&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0178751?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
    ---><---

    Citations

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


    Cited by:

    1. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    2. Ryan R Wick & Louise M Judd & Kathryn E Holt, 2018. "Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-11, November.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0178751. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.