ArticleBMC microbiology2016
SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli.
Article in BMC microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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
8 citing papers in PubMed.
- Global epidemiological insights on the antimicrobial resistance ofMicrobial genomics · 2025Article
- Whole-Genome Sequencing of Shiga Toxin-ProducingMicroorganisms · 2023Article
- Identification of Novel Biomarkers for Priority Serotypes of Shiga Toxin-ProducingFrontiers in microbiology · 2018Article
- Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses.Database : the journal of biological databases and curation · 2018Article
- Genomic Analysis of Third Generation Cephalosporin Resistant Escherichia coli from Dairy Cow Manure.Veterinary sciences · 2017Article
- The Importance of Bacterial Culture to Food Microbiology in the Age of Genomics.Frontiers in microbiology · 2017Review
- Pan-genome Analyses of the SpeciesFrontiers in microbiology · 2017Article
- Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing.Clinical microbiology reviews · 2016Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data.
resultsIn this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype.
conclusionsSuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.
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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.