↓ Skip to main content

SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli

Overview of attention for article published in BMC Microbiology, April 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
55 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli
Published in
BMC Microbiology, April 2016
DOI 10.1186/s12866-016-0680-0
Pubmed ID
Authors

Matthew D. Whiteside, Chad R. Laing, Akiff Manji, Peter Kruczkiewicz, Eduardo N. Taboada, Victor P. J. Gannon

Abstract

Predictive 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. In 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. SuperPhy 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Denmark 1 2%
Australia 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 29%
Student > Master 9 16%
Student > Ph. D. Student 7 13%
Professor 4 7%
Student > Bachelor 4 7%
Other 10 18%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 36%
Biochemistry, Genetics and Molecular Biology 7 13%
Medicine and Dentistry 5 9%
Computer Science 5 9%
Immunology and Microbiology 4 7%
Other 7 13%
Unknown 7 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 April 2016.
All research outputs
#6,971,964
of 22,862,742 outputs
Outputs from BMC Microbiology
#775
of 3,194 outputs
Outputs of similar age
#98,908
of 300,876 outputs
Outputs of similar age from BMC Microbiology
#27
of 68 outputs
Altmetric has tracked 22,862,742 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 3,194 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,876 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.