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    WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

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    Wang2018_Article_WaveNanoASignal-levelNanoporeB.pdf
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    Type
    Article
    Authors
    Wang, Sheng
    Li, Zhen
    Yu, Yizhou
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    KAUST Grant Number
    FCC/1/1976-04
    URF/1/2601-01
    URF/1/3007-01
    URF/1/3412-01
    URF/1/3450-01
    Date
    2018-11-24
    Online Publication Date
    2018-11-24
    Print Publication Date
    2018-12
    Permanent link to this record
    http://hdl.handle.net/10754/630140
    
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    Abstract
    Background \nThe Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of applications. \n \nMethods \nIn this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability matrix. \nResults \nOur proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda phage. \nConclusions \nThe signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.
    Citation
    Wang S, Li Z, Yu Y, Gao X (2018) WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets. Quantitative Biology. Available: http://dx.doi.org/10.1007/s40484-018-0155-4.
    Sponsors
    We thank Minh Duc Cao and Lachlan J. M. Coin for providing the nanopore sequencing data for the Lambda phage sample. We thank Haotian Teng for providing helpful discussions. This work was supported by the Kind Abdullah Unviersity of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards Nos. FCC/1/1976-04, URF/1/2601-01, URF/1/3007-01, URF/1/3412-01 and URF/1/3450-01.
    Publisher
    Springer Nature
    Journal
    Quantitative Biology
    DOI
    10.1007/s40484-018-0155-4
    Additional Links
    http://link.springer.com/article/10.1007/s40484-018-0155-4
    ae974a485f413a2113503eed53cd6c53
    10.1007/s40484-018-0155-4
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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