MetaCompare: a computational pipeline for prioritizing environmental resistome risk

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dc.contributor.authorOh, Minen
dc.contributor.authorPruden, Amyen
dc.contributor.authorChen, Chaoqien
dc.contributor.authorHeath, Lenwood S.en
dc.contributor.authorXia, Kangen
dc.contributor.authorZhang, Liqingen
dc.contributor.departmentCivil and Environmental Engineeringen
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.date.accessioned2019-09-05T17:12:01Zen
dc.date.available2019-09-05T17:12:01Zen
dc.date.issued2018-07en
dc.description.abstractThe spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking 'resistome risk', which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage.en
dc.description.notesThis work was funded in part by USDA NIFA AFRI awards #2014-05280 and 2017-68003-26498 and National Science Foundation Partnership in International Research and Education award 1545756.en
dc.description.sponsorshipUSDA NIFA AFRI [2014-05280, 2017-68003-26498]; National Science Foundation Partnership in International Research and Education award [1545756]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1093/femsec/fiy079en
dc.identifier.eissn1574-6941en
dc.identifier.issn0168-6496en
dc.identifier.issue7en
dc.identifier.otherfiy079en
dc.identifier.pmid29718191en
dc.identifier.urihttp://hdl.handle.net/10919/93399en
dc.identifier.volume94en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectantibiotics resistance geneen
dc.subjectresistomeen
dc.subjectresistome risken
dc.subjectmetagenomicsen
dc.subjectenvironmental samplesen
dc.titleMetaCompare: a computational pipeline for prioritizing environmental resistome risken
dc.title.serialFems Microbiology Ecologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Scholarly Works, Computer Science
Destination Area: Global Systems Science (GSS)
Scholarly Works, Civil and Environmental Engineering
Scholarly Works, School of Plant and Environmental Sciences