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Rapid discovery of novel prophages using biological feature engineering and machine learning

View ORCID ProfileKimmo Sirén, View ORCID ProfileAndrew Millard, View ORCID ProfileBent Petersen, View ORCID ProfileM Thomas P Gilbert, View ORCID ProfileMartha RJ Clokie, View ORCID ProfileThomas Sicheritz-Pontén
doi: https://doi.org/10.1101/2020.08.09.243022
Kimmo Sirén
1Section for Evolutionary Genomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
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Andrew Millard
5Department of Genetics and Genome Biology, University of Leicester, Leicester
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Bent Petersen
1Section for Evolutionary Genomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
2Centre of Excellence for Omics-Driven Computational Biodiscovery, AIMST University, Kedah, Malaysia
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M Thomas P Gilbert
1Section for Evolutionary Genomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
3Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Denmark
4University Museum, NTNU, Trondheim, Norway
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Martha RJ Clokie
5Department of Genetics and Genome Biology, University of Leicester, Leicester
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Thomas Sicheritz-Pontén
1Section for Evolutionary Genomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
2Centre of Excellence for Omics-Driven Computational Biodiscovery, AIMST University, Kedah, Malaysia
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  • For correspondence: thomassp@sund.ku.dk
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ABSTRACT

Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://doi.org/10.17894/ucph.64136536-6353-430b-96ca-701ce89921c4

  • http://phageboost.ml

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 10, 2020.
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Rapid discovery of novel prophages using biological feature engineering and machine learning
Kimmo Sirén, Andrew Millard, Bent Petersen, M Thomas P Gilbert, Martha RJ Clokie, Thomas Sicheritz-Pontén
bioRxiv 2020.08.09.243022; doi: https://doi.org/10.1101/2020.08.09.243022
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Rapid discovery of novel prophages using biological feature engineering and machine learning
Kimmo Sirén, Andrew Millard, Bent Petersen, M Thomas P Gilbert, Martha RJ Clokie, Thomas Sicheritz-Pontén
bioRxiv 2020.08.09.243022; doi: https://doi.org/10.1101/2020.08.09.243022

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