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TypOn: the microbial typing ontology

Overview of attention for article published in Journal of Biomedical Semantics, October 2014
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Title
TypOn: the microbial typing ontology
Published in
Journal of Biomedical Semantics, October 2014
DOI 10.1186/2041-1480-5-43
Pubmed ID
Authors

Cátia Vaz, Alexandre P Francisco, Mickael Silva, Keith A Jolley, James E Bray, Hannes Pouseele, Joerg Rothganger, Mário Ramirez, João A Carriço

Abstract

Bacterial identification and characterization at subspecies level is commonly known as Microbial Typing. Currently, these methodologies are fundamental tools in Clinical Microbiology and bacterial population genetics studies to track outbreaks and to study the dissemination and evolution of virulence or pathogenicity factors and antimicrobial resistance. Due to advances in DNA sequencing technology, these methods have evolved to become focused on sequence-based methodologies. The need to have a common understanding of the concepts described and the ability to share results within the community at a global level are increasingly important requisites for the continued development of portable and accurate sequence-based typing methods, especially with the recent introduction of Next Generation Sequencing (NGS) technologies. In this paper, we present an ontology designed for the sequence-based microbial typing field, capable of describing any of the sequence-based typing methodologies currently in use and being developed, including novel NGS based methods. This is a fundamental step to accurately describe, analyze, curate, and manage information for microbial typing based on sequence based typing methods.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Sweden 1 3%
Portugal 1 3%
Germany 1 3%
Unknown 29 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Master 6 18%
Student > Ph. D. Student 4 12%
Student > Postgraduate 3 9%
Professor 2 6%
Other 6 18%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 27%
Agricultural and Biological Sciences 8 24%
Computer Science 5 15%
Medicine and Dentistry 3 9%
Business, Management and Accounting 1 3%
Other 4 12%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 October 2014.
All research outputs
#21,486,023
of 26,411,386 outputs
Outputs from Journal of Biomedical Semantics
#306
of 371 outputs
Outputs of similar age
#200,701
of 271,768 outputs
Outputs of similar age from Journal of Biomedical Semantics
#8
of 9 outputs
Altmetric has tracked 26,411,386 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 371 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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 271,768 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.