IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-24001-2.html
   My bibliography  Save this article

Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

Author

Listed:
  • Masateru Taniguchi

    (Osaka University)

  • Shohei Minami

    (Osaka University)

  • Chikako Ono

    (Osaka University
    Osaka University)

  • Rina Hamajima

    (Osaka University)

  • Ayumi Morimura

    (Osaka University)

  • Shigeto Hamaguchi

    (Osaka University
    Osaka University Hospital, Osaka University)

  • Yukihiro Akeda

    (Osaka University
    Osaka University
    Osaka University Hospital, Osaka University)

  • Yuta Kanai

    (Osaka University)

  • Takeshi Kobayashi

    (Osaka University)

  • Wataru Kamitani

    (Gunma University)

  • Yutaka Terada

    (University of Pittsburgh)

  • Koichiro Suzuki

    (The Research Foundation for Microbial Diseases of Osaka University)

  • Nobuaki Hatori

    (The Research Foundation for Microbial Diseases of Osaka University)

  • Yoshiaki Yamagishi

    (Osaka University
    Osaka University Hospital, Osaka University
    Osaka University Hospital, Osaka University)

  • Nobuei Washizu

    (ADVANTEST Corporation)

  • Hiroyasu Takei

    (Aipore Inc.)

  • Osamu Sakamoto

    (Aipore Inc.)

  • Norihiko Naono

    (Aipore Inc.)

  • Kenji Tatematsu

    (Osaka University)

  • Takashi Washio

    (Osaka University)

  • Yoshiharu Matsuura

    (Osaka University
    Osaka University)

  • Kazunori Tomono

    (Osaka University
    Osaka University Hospital, Osaka University)

Abstract

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Suggested Citation

  • Masateru Taniguchi & Shohei Minami & Chikako Ono & Rina Hamajima & Ayumi Morimura & Shigeto Hamaguchi & Yukihiro Akeda & Yuta Kanai & Takeshi Kobayashi & Wataru Kamitani & Yutaka Terada & Koichiro Suz, 2021. "Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24001-2
    DOI: 10.1038/s41467-021-24001-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-24001-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-021-24001-2?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. Pingping Fan & Shanyu Zhang & Yuqin Wang & Tian Li & Hanhan Zhang & Panke Zhang & Shuo Huang, 2024. "Nanopore analysis of salvianolic acids in herbal medicines," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    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:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24001-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.