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    De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage

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    Type
    Preprint
    Authors
    Cao, Chen
    He, Jingni
    Mak, Lauren
    Perera, Deshan
    Kwok, Devin
    Wang, Jia
    Li, Minghao
    Mourier, Tobias
    Gavriliuc, Stefan
    Greenberg, Matthew
    Morrissy, A. Sorana
    Sycuro, Laura K.
    YANG, GUANG
    Jeffares, Daniel C.
    Long, Quan
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Date
    2020-03-30
    Permanent link to this record
    http://hdl.handle.net/10754/662468
    
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    Abstract
    AbstractDNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or ‘haplotypes’. However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools in the above four fields, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of HIV previously unobserved in single position-based analysis.
    Citation
    Cao, C., He, J., Mak, L., Perera, D., Kwok, D., Wang, J., … Long, Q. (2020). De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage. doi:10.1101/2020.03.29.014704
    Sponsors
    Q.L. is supported by an NSERC Discovery Grant (RGPIN-2017-04860), a 540 Canada Foundation for Innovation JELF grant (36605), and an ACHRI Startup grant. C.C., 541 M.L. and L.M. are supported by ACHRI scholarship. L.M. is supported by a QEII award. G.Y. is 542 supported by an NSERC Discovery Grant (RGPIN/04246-2018)
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.03.29.014704
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.03.29.014704
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.03.29.014704
    Scopus Count
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Preprints

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