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Deep Sequencing Identifies Ethnicity-Specific Bacterial Signatures in the Oral Microbiome

Overview of attention for article published in PLOS ONE, October 2013
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Title
Deep Sequencing Identifies Ethnicity-Specific Bacterial Signatures in the Oral Microbiome
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077287
Pubmed ID
Authors

Matthew R. Mason, Haikady N. Nagaraja, Terry Camerlengo, Vinayak Joshi, Purnima S. Kumar

Abstract

Oral infections have a strong ethnic predilection; suggesting that ethnicity is a critical determinant of oral microbial colonization. Dental plaque and saliva samples from 192 subjects belonging to four major ethnicities in the United States were analyzed using terminal restriction fragment length polymorphism (t-RFLP) and 16S pyrosequencing. Ethnicity-specific clustering of microbial communities was apparent in saliva and subgingival biofilms, and a machine-learning classifier was capable of identifying an individual's ethnicity from subgingival microbial signatures. The classifier identified African Americans with a 100% sensitivity and 74% specificity and Caucasians with a 50% sensitivity and 91% specificity. The data demonstrates a significant association between ethnic affiliation and the composition of the oral microbiome; to the extent that these microbial signatures appear to be capable of discriminating between ethnicities.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 294 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Germany 1 <1%
Netherlands 1 <1%
France 1 <1%
Colombia 1 <1%
Belgium 1 <1%
Ireland 1 <1%
Japan 1 <1%
Russia 1 <1%
Other 0 0%
Unknown 282 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 19%
Researcher 43 15%
Student > Master 36 12%
Student > Bachelor 35 12%
Other 16 5%
Other 57 19%
Unknown 52 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 86 29%
Biochemistry, Genetics and Molecular Biology 46 16%
Medicine and Dentistry 42 14%
Immunology and Microbiology 18 6%
Computer Science 6 2%
Other 35 12%
Unknown 61 21%