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Gene content dissimilarity for subclassification of highly similar microbial strains

Overview of attention for article published in BMC Genomics, August 2016
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Title
Gene content dissimilarity for subclassification of highly similar microbial strains
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2991-9
Pubmed ID
Authors

Qichao Tu, Lu Lin

Abstract

Identification and classification of highly similar microbial strains is a challenging issue in microbiology, ecology and evolutionary biology. Among various available approaches, gene content analysis is also at the core of microbial taxonomy. However, no threshold has been determined for grouping microorgnisms to different taxonomic levels, and it is still not clear that to what extent genomic fluidity should occur to form a microbial taxonomic group. By taking advantage of the eggNOG database for orthologous groups, we calculated gene content dissimilarity among different microbial strains based on the orthologous gene profiles and tested the possibility of applying gene content dissimilarity as a quantitative index in classifying microbial taxonomic groups, as well as its potential application in subclassification of highly similar microbial strains. Evaluation of gene content dissimilarity to completed microbial genomes at different taxonomic levels suggested that cutoffs of 0.2 and 0.4 can be respectively used for species and family delineation, and that 0.2 gene content dissimilarity cutoff approximately corresponded to 98 % 16S rRNA gene identity and 94 % ANI for microbial species delineation. Furthermore, application of gene content dissimilarity to highly similar microbial strains suggested it as an effective approach in classifying closely related microorganisms into subgroups. This approach is especially useful in identifying pathogens from commensals in clinical microbiology. It also provides novel insights into how genomic fluidity is linked with microbial taxonomy.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 5 14%
Student > Master 5 14%
Professor > Associate Professor 3 9%
Student > Bachelor 3 9%
Other 6 17%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 34%
Biochemistry, Genetics and Molecular Biology 8 23%
Immunology and Microbiology 3 9%
Nursing and Health Professions 1 3%
Business, Management and Accounting 1 3%
Other 5 14%
Unknown 5 14%
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 12 September 2017.
All research outputs
#15,821,622
of 23,498,099 outputs
Outputs from BMC Genomics
#6,810
of 10,787 outputs
Outputs of similar age
#221,227
of 344,857 outputs
Outputs of similar age from BMC Genomics
#171
of 265 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,787 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 265 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.