ArticleBMC bioinformatics2011
Empirical Bayesian models for analysing molecular serotyping microarrays.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 33 citations in OpenAlex.
- Impact of COVID-19 Nonpharmaceutical Interventions on Pneumococcal Carriage Prevalence and Density in Vietnam.Microbiology spectrum · 2023Article
- Direct and indirect effects of 13-valent pneumococcal conjugate vaccine on pneumococcal carriage in children hospitalised with pneumonia from formal and informal settlements in Mongolia: an observational study.The Lancet regional health. Western Pacific · 2021Article
- Observational
- Determining the serotype composition of mixed samples of pneumococcus using whole-genome sequencing.Microbial genomics · 2021Article
- Evaluation of Pneumococcal Serotyping of Nasopharyngeal-Carriage Isolates by Latex Agglutination, Whole-Genome Sequencing (PneumoCaT), and DNA Microarray in a High-Pneumococcal-Carriage-Prevalence Population in Malawi.Journal of clinical microbiology · 2020Article
- Observational
- A novel genetic variant of Streptococcus pneumoniae serotype 11A discovered in Fiji.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2018Article
- A comparison of machine learning and Bayesian modelling for molecular serotyping.BMC genomics · 2017Article
- The PneuCarriage Project: A Multi-Centre Comparative Study to Identify the Best Serotyping Methods for Examining Pneumococcal Carriage in Vaccine Evaluation Studies.PLoS medicine · 2015Article
- Pneumococcal Capsules and Their Types: Past, Present, and Future.Clinical microbiology reviews · 2015Review
- High multiple carriage and emergence of Streptococcus pneumoniae vaccine serotype variants in Malawian children.BMC infectious diseases · 2015Article
- Multi-serotype pneumococcal nasopharyngeal carriage prevalence in vaccine naïve Nepalese children, assessed using molecular serotyping.PloS one · 2015Article
- Observational
- Revisiting molecular serotyping of Streptococcus pneumoniae.BMC genomics · 2015Article
- Two novel pathway analysis methods based on a hierarchical model.Bioinformatics (Oxford, England) · 2014Article
- Defective pneumococcal-specific Th1 responses in HIV-infected adults precedes a loss of control of pneumococcal colonization.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2013Article
- Decrease in pneumococcal co-colonization following vaccination with the seven-valent pneumococcal conjugate vaccine.PloS one · 2012Article
- Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMicroarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assays. In this study applied to infectious diseases, data from a microarray designed for molecular serotyping of Streptococcus pneumoniae was used, identifying the presence of any one of 91 known pneumococcal serotypes from DNA extracts. This microarray incorporated oligonucleotide probes for all known capsular polysaccharide synthesis genes and required a statistical analysis of the microarray intensity data to determine which serotype, or combination of serotypes, were present within a sample based on the combination of genes detected.
resultsWe propose an empirical Bayesian model for calculating the probabilities of combinations of serotypes from the microarray data. The model takes into consideration the dependencies between serotypes, induced by genes they have in common, and by homologous genes which, although not identical, are similar to each other in sequence. For serotypes which are very similar in capsular gene composition, extra probes are included on the microarray, providing additional information which is integrated into the Bayesian model. For each serotype combination with high probability, a second model, a Bayesian random effects model is applied to determine the relative abundance of each serotype.
conclusionsTo assess the accuracy of the proposed analysis we applied our methods to experimental data from samples containing individual serotypes and samples containing combinations of serotypes with known levels of abundance. All but two of the known serotypes of S. pneumoniae that were tested as individual samples could be uniquely determined by the Bayesian model. The model also enabled the presence of combinations of serotypes within samples to be determined. Serotypes with very low abundance within a combination of serotypes can be detected (down to 2% abundance in this study). As well as detecting the presence of serotype combinations, an approximate measure of the percentage abundance of the serotypes within the combination can be obtained.
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