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Using “Omics” and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases

Overview of attention for article published in Frontiers in Microbiology, February 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
2 blogs
policy
1 policy source
twitter
25 X users
facebook
1 Facebook page

Readers on

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252 Mendeley
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Title
Using “Omics” and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases
Published in
Frontiers in Microbiology, February 2016
DOI 10.3389/fmicb.2016.00068
Pubmed ID
Authors

Eria A. Rebollar, Rachael E. Antwis, Matthew H. Becker, Lisa K. Belden, Molly C. Bletz, Robert M. Brucker, Xavier A. Harrison, Myra C. Hughey, Jordan G. Kueneman, Andrew H. Loudon, Valerie McKenzie, Daniel Medina, Kevin P. C. Minbiole, Louise A. Rollins-Smith, Jenifer B. Walke, Sophie Weiss, Douglas C. Woodhams, Reid N. Harris

Abstract

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 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 252 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 1%
United States 2 <1%
Portugal 1 <1%
South Africa 1 <1%
Sweden 1 <1%
Puerto Rico 1 <1%
Mexico 1 <1%
Unknown 242 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 24%
Researcher 40 16%
Student > Bachelor 31 12%
Student > Master 28 11%
Student > Doctoral Student 19 8%
Other 34 13%
Unknown 39 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 111 44%
Biochemistry, Genetics and Molecular Biology 42 17%
Environmental Science 18 7%
Immunology and Microbiology 15 6%
Computer Science 6 2%
Other 16 6%
Unknown 44 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 01 January 2019.
All research outputs
#1,154,609
of 23,498,099 outputs
Outputs from Frontiers in Microbiology
#646
of 25,939 outputs
Outputs of similar age
#22,160
of 400,357 outputs
Outputs of similar age from Frontiers in Microbiology
#15
of 487 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,939 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done particularly well, scoring higher than 97% 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 400,357 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 487 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.