Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes

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dc.contributor.authorBrown, Connor L.en
dc.contributor.authorKeenum, Ishi M.en
dc.contributor.authorDai, Dongjuanen
dc.contributor.authorZhang, Liqingen
dc.contributor.authorVikesland, Peter J.en
dc.contributor.authorPruden, Amyen
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2021-05-18T12:24:39Zen
dc.date.available2021-05-18T12:24:39Zen
dc.date.issued2021-02-12en
dc.description.abstractIn the fight to limit the global spread of antibiotic resistance, the assembly of environmental metagenomes has the potential to provide rich contextual information (e.g., taxonomic hosts, carriage on mobile genetic elements) about antibiotic resistance genes (ARG) in the environment. However, computational challenges associated with assembly can impact the accuracy of downstream analyses. This work critically evaluates the impact of assembly leveraging short reads, nanopore MinION long-reads, and a combination of the two (hybrid) on ARG contextualization for ten environmental metagenomes using seven prominent assemblers (IDBA-UD, MEGAHIT, Canu, Flye, Opera-MS, metaSpades and HybridSpades). While short-read and hybrid assemblies produced similar patterns of ARG contextualization, raw or assembled long nanopore reads produced distinct patterns. Based on an in-silico spike-in experiment using real and simulated reads, we show that low to intermediate coverage species are more likely to be incorporated into chimeric contigs across all assemblers and sequencing technologies, while more abundant species produce assemblies with a greater frequency of inversions and insertion/deletions (indels). In sum, our analyses support hybrid assembly as a valuable technique for boosting the reliability and accuracy of assembly-based analyses of ARGs and neighboring genes at environmentally-relevant coverages, provided that sufficient short-read sequencing depth is achieved.en
dc.description.notesThis study was supported by NSF PIRE (PI Vikesland) Award 1545756, USDA National Institute of Food and Agriculture competitive Grant 2017-68003-26498, Water Research Foundation Project 4961, the Genetics, Bioinformatics, and Computational Biology Interdisciplinary Graduate Education Program (IGEP), the Virginia Tech Sustainable NanoTechnology IGEP, NanoEarth, Fralin Life Sciences Institute, the Virginia Tech Open Access Support Fund, and the Virginia Tech ICTAS Center for Science and Engineering of the Exposome. The authors acknowledge the Advanced Research Computing at Virginia Tech for providing computational resources. The authors would like to thank Chujia Chen, Yirui Chen, Bailey Walker, and Bowen Shen.en
dc.description.sponsorshipNSF PIRE AwardNational Science Foundation (NSF)NSF - Office of the Director (OD) [1545756]; USDA National Institute of Food and Agriculture competitive Grant [2017-68003-26498]; Water Research Foundation Project [4961]; Genetics, Bioinformatics, and Computational Biology Interdisciplinary Graduate Education Program (IGEP); Virginia Tech Sustainable NanoTechnology IGEP; NanoEarth; Fralin Life Sciences Institute; Virginia Tech Open Access Support Fund; Virginia Tech ICTAS Center for Science and Engineering of the Exposomeen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-021-83081-8en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other3753en
dc.identifier.pmid33580146en
dc.identifier.urihttp://hdl.handle.net/10919/103352en
dc.identifier.volume11en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCritical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomesen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Scholarly Works, Civil and Environmental Engineering
Destination Area: Global Systems Science (GSS)