ArticlePloS one2016
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 38 citations in OpenAlex.
- A Different Microbiome Gene Repertoire in the Airways of Cystic Fibrosis Patients with Severe Lung Disease.International journal of molecular sciences · 2017Trial
- Human Contact Frequency as a Dominant Ecological Driver of Fungal Community Assembly and Homogenization in Public Built Environments.Journal of microbiology and biotechnology · 2026Article
- Contrasting the gut microbiome in Colombian patients with diarrhea: a comparative metagenomic study in hospitalization and emergency room services.Gut pathogens · 2026Article
- Air Bacterial Microbiomes in Hospitals: Case Studies from a Metropolis and a Small City of Thailand.Computational and structural biotechnology journal · 2026Article
- Contribution of DNA Metabarcoding to the Environmental Fungal Assessments in Hospitals.Microbial ecology · 2025Article
- From air to insight: the evolution of airborne DNA sequencing technologies.Microbiology (Reading, England) · 2025Review
- Analysis of metagenomic data.Nature reviews. Methods primers · 2025Article
- Antibiotic Resistance as a Functional Characteristic of Urban Dust Particles' Microbial Communities.Biology · 2024Article
- Metagenomic airborne resistome from urban hot spots through the One Health lens.Environmental microbiology reports · 2024Article
- Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide.Nature communications · 2023Article
- Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions.Foods (Basel, Switzerland) · 2023Review
- Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples.International journal of environmental research and public health · 2022Article
- Detection of antimicrobial resistance genes in urban air.MicrobiologyOpen · 2021Article
- Advances in the Microbiology of Stenotrophomonas maltophilia.Clinical microbiology reviews · 2021Article
- Molecular Epidemiology ofJournal of fungi (Basel, Switzerland) · 2021Review
- Emerging priorities for microbial metagenome research.Bioresource technology reports · 2020Review
- Performance evaluation of high-volume electret filter air samplers in aerosol microbiome research.Environmental microbiome · 2020Article
- Article
- Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research.Environmental microbiome · 2020Article
- Succinct colored de Bruijn graphs.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe the sampling of sixty-three uncultured hospital air samples collected over a six-month period and analysis using shotgun metagenomic sequencing. Our primary goals were to determine the longitudinal metagenomic variability of this environment, identify and characterize genomes of potential pathogens and determine whether they are atypical to the hospital airborne metagenome. Air samples were collected from eight locations which included patient wards, the main lobby and outside. The resulting DNA libraries produced 972 million sequences representing 51 gigabases. Hierarchical clustering of samples by the most abundant 50 microbial orders generated three major nodes which primarily clustered by type of location. Because the indoor locations were longitudinally consistent, episodic relative increases in microbial genomic signatures related to the opportunistic pathogens Aspergillus, Penicillium and Stenotrophomonas were identified as outliers at specific locations. Further analysis of microbial reads specific for Stenotrophomonas maltophilia indicated homology to a sequenced multi-drug resistant clinical strain and we observed broad sequence coverage of resistance genes. We demonstrate that a shotgun metagenomic sequencing approach can be used to characterize the resistance determinants of pathogen genomes that are uncharacteristic for an otherwise consistent hospital air microbial metagenomic profile.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.