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    Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing.

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    Thumbnail
    Name:
    BIOINF-2019-0700.R1_Proof_hi.pdf
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    2.821Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Han, Renmin
    Wang, Sheng
    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/1/1976-18-01
    FCC/1/1976-23-01
    FCC/1/1976-25-01
    FCC/1/1976-26-01
    URF/1/3412-01
    URF/1/3450-01-01
    Date
    2019-10-08
    Embargo End Date
    2020-10-09
    Permanent link to this record
    http://hdl.handle.net/10754/658636
    
    Metadata
    Show full item record
    Abstract
    MOTIVATION:Genome diagnostics have gradually become a prevailing routine for human healthcare. With the advances in understanding the causal genes for many human diseases, targeted sequencing provides a rapid, cost-efficient and focused option for clinical applications, such as SNP detection and haplotype classification, in a specific genomic region. Although nanopore sequencing offers a perfect tool for targeted sequencing because of its mobility, PCR-freeness, and long read properties, it poses a challenging computational problem of how to efficiently and accurately search and map genomic subsequences of interest in a pool of nanopore reads (or raw signals). Due to its relatively low sequencing accuracy, there is no reliable solution to this problem, especially at low sequencing coverage. RESULTS:Here, we propose a brand new signal-based subsequence inquiry pipeline as well as two novel algorithms to tackle this problem. The proposed algorithms follow the principle of subsequence dynamic time warping and directly operate on the electrical current signals, without loss of information in base-calling. Therefore, the proposed algorithms can serve as a tool for sequence inquiry in targeted sequencing. Two novel criteria are offered for the consequent signal quality analysis and data classification. Comprehensive experiments on real-world nanopore datasets show the efficiency and effectiveness of the proposed algorithms. We further demonstrate the potential applications of the proposed algorithms in two typical tasks in nanopore-based targeted sequencing: SNP detection under low sequencing coverage, and haplotype classification under low sequencing accuracy. AVAILABILITY:The project is accessible at https://github.com/icthrm/cwSDTWnano.git, and the presented bench data is available upon request.
    Citation
    Han, R., Wang, S., & Gao, X. (2019). Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing. Bioinformatics. doi:10.1093/bioinformatics/btz742
    Sponsors
    The authors thank Minh Duc Cao, Lachlan J.M. Coin, Louise Roddam and Tania Duarte for providing the nanopore sequencing data. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/3412-01-01, and URF/1/3450-01-01.
    Publisher
    Oxford University Press (OUP)
    Journal
    Bioinformatics
    DOI
    10.1093/bioinformatics/btz742
    Additional Links
    https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz742/5583772
    Relations
    Is Supplemented By:
    • [Software]
      Title: icthrm/cwSDTWnano. Publication Date: 2018-10-28. github: icthrm/cwSDTWnano Handle: 10754/667008
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/btz742
    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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