The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
MetaCompare: a computational pipeline for prioritizing environmental resistome risk
Open accessAbstract
The 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.
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Cites
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MetaPhlAn2 for enhanced metagenomic taxonomic profiling
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Clemente, J. C., Pehrsson, E. C., Blaser, M. J., Sandhu, K., Gao, Z., Wang, B., Magris, M., Hidalgo, G., Contreras, M., Noya-Alarcón, Ó., et al. (2015) · Science Advances · doi:10.1126/sciadv.1500183 · Cited by 851
Antibiotic resistance is prevalent in an isolated cave microbiome
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Cited by
An omics-based framework for assessing the health risk of antimicrobial resistance genes
Zhang, A.-N., Gaston, J. M., Dai, C. L., Zhao, S., Poyet, M., Groussin, M., Yin, X., Li, L.-G., van Loosdrecht, M. C. M., Topp, E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25096-3 · Cited by 939
Karkman, A., Pärnänen, K., & Larsson, D. G. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07992-3 · Cited by 628
Nguyen, A. Q., Vu, H. P., Nguyen, L. N., Wang, Q., Djordjevic, S. P., Donner, E., Yin, H., & Nghiem, L. D. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146964 · Cited by 339
Liguori, K., Keenum, I., Davis, B. C., Calarco, J., Milligan, E., Harwood, V. J., & Pruden, A. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c08918 · Cited by 294
Self-citationHamilton, K. A., Garner, E., Joshi, S., Ahmed, W., Ashbolt, N., Medema, G., & Pruden, A. (2020) · Current Opinion in Environmental Science & Health · doi:10.1016/j.coesh.2020.02.012 · Cited by 32
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Metadata
- Authors
- Oh, M., Pruden, A., Chen, C., Heath, L. S., Xia, K., & Zhang, L.
- Venue
- FEMS Microbiology Ecology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-26
- Volume
- 94
- Issue
- 7
- ISSN
- 0168-6496 (ISSN-L)
- 1574-6941
- OpenAlex
- W2799291493
- Cited by
- 186
- References
- 6
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Oh, M., Pruden, A., Chen, C., Heath, L. S., Xia, K., & Zhang, L. (2018). MetaCompare: a computational pipeline for prioritizing environmental resistome risk. FEMS Microbiology Ecology. https://doi.org/10.1093/femsec/fiy079
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