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iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria
The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host as...
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| Published in: | PLoS biology 2023-04, Vol.21 (4), p.e3002083-e3002083 |
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| Main Authors: | , , , , , , |
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| creator | Roux, Simon Camargo, Antonio Pedro Coutinho, Felipe H Dabdoub, Shareef M Dutilh, Bas E Nayfach, Stephen Tritt, Andrew |
| description | The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses. |
| doi_str_mv | 10.1371/journal.pbio.3002083 |
| format | article |
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While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.</description><identifier>ISSN: 1545-7885</identifier><identifier>ISSN: 1544-9173</identifier><identifier>EISSN: 1545-7885</identifier><identifier>DOI: 10.1371/journal.pbio.3002083</identifier><identifier>PMID: 37083735</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Archaea ; Archaea - genetics ; Bacteria ; Bacteria - genetics ; Bacteriophages ; Benchmarks ; Biology and Life Sciences ; Computer and Information Sciences ; CRISPR ; Datasets ; Ecology and Environmental Sciences ; Engineering and Technology ; Gene sequencing ; Genome, Viral - genetics ; Genomes ; Identification and classification ; Machine Learning ; Metabolism ; Metagenome - genetics ; Metagenomics ; Metagenomics - methods ; Methods ; Methods and Resources ; Predictions ; Research and Analysis Methods ; Taxonomy ; Viral infections ; Virulence ; Viruses ; Viruses - genetics</subject><ispartof>PLoS biology, 2023-04, Vol.21 (4), p.e3002083-e3002083</ispartof><rights>Copyright: © 2023 Roux et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Roux et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. 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While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. 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| subjects | Analysis Archaea Archaea - genetics Bacteria Bacteria - genetics Bacteriophages Benchmarks Biology and Life Sciences Computer and Information Sciences CRISPR Datasets Ecology and Environmental Sciences Engineering and Technology Gene sequencing Genome, Viral - genetics Genomes Identification and classification Machine Learning Metabolism Metagenome - genetics Metagenomics Metagenomics - methods Methods Methods and Resources Predictions Research and Analysis Methods Taxonomy Viral infections Virulence Viruses Viruses - genetics |
| title | iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria |
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