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    The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

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    Author
    Ponsero, Alise J.
    Hurwitz, Bonnie L.
    Affiliation
    Univ Arizona, Dept Biosyst Engn
    Univ Arizona, BIO5 Inst
    Issue Date
    2019-04-16
    Keywords
    virus
    metagenomic
    machine learning
    sequence classification
    viral signature
    
    Metadata
    Show full item record
    Publisher
    FRONTIERS MEDIA SA
    Citation
    Ponsero AJ and Hurwitz BL (2019) The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes. Front. Microbiol. 10:806. doi: 10.3389/fmicb.2019.00806
    Journal
    FRONTIERS IN MICROBIOLOGY
    Rights
    © 2019 Ponsero and Hurwitz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.
    Note
    Open access journal.
    ISSN
    1664-302X
    DOI
    10.3389/fmicb.2019.00806
    Version
    Final published version
    Sponsors
    National Science Foundation [1640775]
    ae974a485f413a2113503eed53cd6c53
    10.3389/fmicb.2019.00806
    Scopus Count
    Collections
    UA Faculty Publications

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