iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

Roux, Simon; Camargo, Antonio Pedro; Coutinho, Felipe H.; Dabdoub, Shareef M.; Dutilh, Bas E.; Nayfach, Stephen; Tritt, Andrew

doi:10.1371/journal.pbio.3002083

Title: iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

Journal Article · 21 April 2023 · PLoS Biology (Online)

DOI: https://doi.org/10.1371/journal.pbio.3002083 · OSTI ID:1972401

^[1]; Camargo, Antonio Pedro ^[1]; Coutinho, Felipe H. ^[2]; Dabdoub, Shareef M. ^[3]; Dutilh, Bas E. ^[4]; Nayfach, Stephen ^[1]; Tritt, Andrew ^[5]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). USDOE Joint Genome Institute
Instituto de Ciencias del Mar (ICM-CSIC), Barcelona (Spain)
University of Iowa College of Dentistry, Iowa City, IA (United States)
Friedrich Schiller University, Jena (Germany); Utrecht University (Netherlands)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); European Research Council (ERC)

Grant/Contract Number:: AC02-05CH11231; 865694

OSTI ID:: 1972401

Alternate ID(s):: OSTI ID: 1971185; OSTI ID: 2228615

Journal Information:: PLoS Biology (Online), Vol. 21, Issue 4; ISSN 1545-7885

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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