The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes
AffiliationUniv Arizona, Dept Biosyst Engn
Univ Arizona, BIO5 Inst
MetadataShow full item record
PublisherFRONTIERS MEDIA SA
CitationPonsero 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
JournalFRONTIERS 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.
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AbstractTools 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.
NoteOpen access journal.
VersionFinal published version
SponsorsNational Science Foundation