ArticlePloS one2012
Genome analysis of Legionella pneumophila strains using a mixed-genome microarray.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Genomic heterogeneity differentiates clinical and environmental subgroups of Legionella pneumophila sequence type 1.PloS one · 2018Article
- Multiplex polymerase chain reaction of genetic markers for detection of potentially pathogenic environmentalThe Indian journal of medical research · 2017Article
- Differential Proteome Between Patient-Related and Non-related Environmental Isolates of Legionella pneumophila.Current microbiology · 2017Article
- Feature selection and validated predictive performance in the domain of Legionella pneumophila: a comparative study.BMC research notes · 2016Article
- Article
- Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray.BMC genomics · 2013Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLegionella, the causative agent for Legionnaires' disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.
methodsMicroarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in The Netherlands in the period 2002-2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.
resultsFour genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.
conclusionsThe Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.
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Registered trials
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