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RevEcoR: an R package for the reverse ecology analysis of microbiomes

Overview of attention for article published in BMC Bioinformatics, July 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
33 X users

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
148 Mendeley
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1 CiteULike
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Title
RevEcoR: an R package for the reverse ecology analysis of microbiomes
Published in
BMC Bioinformatics, July 2016
DOI 10.1186/s12859-016-1088-4
Pubmed ID
Authors

Yang Cao, Yuanyuan Wang, Xiaofei Zheng, Fei Li, Xiaochen Bo

Abstract

All species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecological methods can only be applied on a small scale and for relatively well-understood biological systems. Recently, a graph-theory-based algorithm called the reverse ecology approach has been developed that can analyze the metabolic networks of all the species in a microbial community, and predict the metabolic interface between species and their environment. Here, we present RevEcoR, an R package and a Shiny Web application that implements the reverse ecology algorithm for determining microbe-microbe interactions in microbial communities. This software allows users to obtain large-scale ecological insights into species' ecology directly from high-throughput metagenomic data. The software has great potential for facilitating the study of microbiomes. RevEcoR is open source software for the study of microbial community ecology. The RevEcoR R package is freely available under the GNU General Public License v. 2.0 at http://cran.r-project.org/web/packages/RevEcoR/ with the vignette and typical usage examples, and the interactive Shiny web application is available at http://yiluheihei.shinyapps.io/shiny-RevEcoR , or can be installed locally with the source code accessed from https://github.com/yiluheihei/shiny-RevEcoR .

X Demographics

X Demographics

The data shown below were collected from the profiles of 33 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
United Kingdom 1 <1%
New Zealand 1 <1%
Estonia 1 <1%
Japan 1 <1%
Unknown 143 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 24%
Student > Ph. D. Student 34 23%
Student > Master 13 9%
Student > Bachelor 11 7%
Student > Doctoral Student 9 6%
Other 22 15%
Unknown 24 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 30%
Biochemistry, Genetics and Molecular Biology 25 17%
Computer Science 12 8%
Environmental Science 9 6%
Immunology and Microbiology 8 5%
Other 23 16%
Unknown 26 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 14 September 2017.
All research outputs
#1,416,313
of 24,885,505 outputs
Outputs from BMC Bioinformatics
#191
of 7,601 outputs
Outputs of similar age
#26,878
of 374,279 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 100 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,601 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 374,279 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 92% of its contemporaries.
We're also able to compare this research output to 100 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.