{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Higgs C"],"funding":["Department of Health | National Health and Medical Research Council"],"pagination":["509"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8792028"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to inf"],"journal":["Nature communications"],"pubmed_title":["Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings."],"pmcid":["PMC8792028"],"funding_grant_id":["APP1196103"],"pubmed_authors":["Bond K","Gorrie CL","Marshall C","Kinsella P","Sherry NL","Stinear TP","Grayson ML","Kwong JC","Higgs C","Walpola H","Howden BP","Seemann T","Horan K","Williamson DA"],"additional_accession":[]},"is_claimable":false,"name":"Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings.","description":"Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to inf","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2026-07-09T12:17:39.349Z","creation":"2025-04-04T08:58:35.198Z"},"accession":"S-EPMC8792028","cross_references":{"pubmed":["35082278"],"doi":["10.1038/s41467-022-28156-4"]}}