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MMinte: an application for predicting metabolic interactions among the microbial species in a community

Overview of attention for article published in BMC Bioinformatics, September 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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
39 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
163 Mendeley
citeulike
2 CiteULike
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Title
MMinte: an application for predicting metabolic interactions among the microbial species in a community
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1230-3
Pubmed ID
Authors

Helena Mendes-Soares, Michael Mundy, Luis Mendes Soares, Nicholas Chia

Abstract

The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities. We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte's simulations take into account the nutritional environment, which plays a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate the utility of this software. In the first, we show how diet influences the nature of the microbe-microbe interactions. In the second, we use MMinte's modular feature set to better understand how the growth of Desulfovibrio piger is affected by, and affects the growth of, other members in a simplified gut community under metabolic conditions suggested to be determinant for their dynamics. By applying metabolic models to commonly available sequence data, MMinte grants the user insight into the metabolic relationships between microbes, highlighting important features that may relate to ecological stability, susceptibility, and cross-feeding. These relationships are at the foundation of a wide range of ecological questions that impact our ability to understand problems such as microbially-derived toxicity in colon cancer.

Twitter Demographics

The data shown below were collected from the profiles of 39 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 163 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%
Denmark 1 <1%
United States 1 <1%
Unknown 159 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 46 28%
Student > Ph. D. Student 41 25%
Student > Master 17 10%
Student > Bachelor 14 9%
Unspecified 11 7%
Other 33 20%
Unknown 1 <1%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 34%
Biochemistry, Genetics and Molecular Biology 23 14%
Unspecified 20 12%
Immunology and Microbiology 14 9%
Computer Science 11 7%
Other 38 23%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 05 August 2017.
All research outputs
#526,553
of 13,318,687 outputs
Outputs from BMC Bioinformatics
#75
of 4,992 outputs
Outputs of similar age
#17,241
of 261,852 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 29 outputs
Altmetric has tracked 13,318,687 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,992 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 98% 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 261,852 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 93% of its contemporaries.
We're also able to compare this research output to 29 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.