SAMEA14081592 ERS11684729 Streptococcus mitis This sample represents a Third Party Annotation (TPA) Metagenome-Assembled Genome (MAG) assembled from the metagenomic run ERR1332588 of study ERP014653. EBI Generic Generic.1.0 ERC000047 2023-01-03 2023-01-03 SAMEA14081592 EBI European Bioinformatics Institute 2023-01-03T00:32:45Z 2023-01-03T00:32:45Z public ERR1332588_bin.12_CONCOCT_v1.1_MAG Many fragments with little to no review of assembly other than reporting of standard assembly statistics metaspadesv3.11.1 Default CONCOCT v1.1 EMG broker account, EMBL-EBI 2013-06-01 98.42 CheckM 0.35 Engineered Engineered Engineered USA 41.779858 -87.6001 metagenome-assembled genome metagenome metagenome The microorganisms that inhabit hospitals may significantly influence patient recovery rates and outcomes, though the complexity and diversity of these communities confound our ability to focus on potential pathogens in isolation. To develop a community level understating of how microorganisms colonize and move through the hospital environment, we mapped microbial dynamics between hospital surfaces, air and water to patients and staff over the course of one year as a new hospital became operational. Immediately following the introduction of staff and patients, the hospital microbiome became dominated by human skin-associated bacteria. Patient skin samples had the lowest microbial diversity, while the greatest diversity was found on surfaces interacting with outdoor environments. The microbiota of patient room surfaces, especially bedrails, consistently resembled the skin microbial community of the current patient within 24 hours of occupancy, with degree of similarity significantly correlated to higher humidity and lower temperatures. Microbial similarity between staff members showed a significant seasonal trend suggesting interpersonal microbial transmission in hospital environments is influenced by built environment parameters. SAMEA3904669 ERR1332588_bin.12_CONCOCT_v1.1_MAG Streptococcus mitis Illumina HiSeq 1500 multi-marker approach