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dc.contributor.authorSirén, Kimmo
dc.contributor.authorMillard, Andrew
dc.contributor.authorPetersen, Bent
dc.contributor.authorGilbert, Marcus Thomas Pius
dc.contributor.authorClokie, Martha R.J.
dc.contributor.authorSicheritz-Pontén, Thomas
dc.date.accessioned2022-03-02T09:22:32Z
dc.date.available2022-03-02T09:22:32Z
dc.date.created2021-12-16T15:00:07Z
dc.date.issued2021
dc.identifier.citationNAR Genomics and Bioinformatics. 2021, 3 (1), .en_US
dc.identifier.issn2631-9268
dc.identifier.urihttps://hdl.handle.net/11250/2982340
dc.description.abstractProphages 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.en_US
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleRapid discovery of novel prophages using biological feature engineering and machine learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber10en_US
dc.source.volume3en_US
dc.source.journalNAR Genomics and Bioinformaticsen_US
dc.source.issue1en_US
dc.identifier.doi10.1093/nargab/lqaa109
dc.identifier.cristin1969525
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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