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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ed7qfvctlnb2ndkdqumgyzhqwe" style="color: black;">FEMS Microbiology Ecology</a>
One sentence summary: A computational pipeline ranking resistome risk for given environmental samples based on shotgun metagenomic sequencing data was developed. Editor: Edward Topp † Min Oh, http://orcid.org/0000-0002-0938-9046 ABSTRACT 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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/femsec/fiy079">doi:10.1093/femsec/fiy079</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29718191">pmid:29718191</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5995210/">pmcid:PMC5995210</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6zll2wzbfzhmncy6snqubjrtlu">fatcat:6zll2wzbfzhmncy6snqubjrtlu</a> </span>
more »... presented 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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