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    Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

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    Type
    Article
    Authors
    Teng, Haotian
    Cao, Minh Duc
    Hall, Michael B
    Duarte, Tania
    Wang, Sheng
    Coin, Lachlan J M
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Date
    2018-04-10
    Online Publication Date
    2018-04-10
    Print Publication Date
    2018-05-01
    Permanent link to this record
    http://hdl.handle.net/10754/627895
    
    Metadata
    Show full item record
    Abstract
    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.
    Citation
    Teng H, Cao MD, Hall MB, Duarte T, Wang S, et al. (2018) Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience 7. Available: http://dx.doi.org/10.1093/gigascience/giy037.
    Sponsors
    We thank Jianhua Guo for contributing the DNA for the E. coli sample. We thank Arnold Bainomugisa for extracting DNA for the M. tuberculosis sample. We thank Sheng Wang and Han Qiao for the helpful discussion. We thank Jain et al. [14] for the open Human nanopore dataset.
    Publisher
    Oxford University Press (OUP)
    Journal
    GigaScience
    DOI
    10.1093/gigascience/giy037
    PubMed ID
    29648610
    Additional Links
    https://academic.oup.com/gigascience/article/7/5/giy037/4966989#116612803
    Relations
    Is Supplemented By:
    • [Software]
      Title: haotianteng/Chiron: A basecaller for Oxford Nanopore Technologies' sequencers. Publication Date: 2017-05-07. github: haotianteng/Chiron Handle: 10754/667012
    ae974a485f413a2113503eed53cd6c53
    10.1093/gigascience/giy037
    Scopus Count
    Collections
    Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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