| Literature DB >> 33575651 |
Kimmo Sirén1, Andrew Millard2, Bent Petersen1, M Thomas P Gilbert1, Martha R J Clokie2, Thomas Sicheritz-Pontén1.
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.Entities:
Year: 2021 PMID: 33575651 PMCID: PMC7787355 DOI: 10.1093/nargab/lqaa109
Source DB: PubMed Journal: NAR Genom Bioinform ISSN: 2631-9268