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MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles

Overview of attention for article published in BMC Bioinformatics, November 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
27 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
165 Mendeley
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Title
MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1359-0
Pubmed ID
Authors

Grace Tzun-Wen Shaw, Yueh-Yang Pao, Daryi Wang

Abstract

The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data. In this paper, we present the Lotka-Volterra model based tool, the Metagenomic Microbial Interacticon Simulator (MetaMIS), which is designed to analyze the time series data of microbial community profiles. MetaMIS first infers underlying microbial interactions from abundance tables for operational taxonomic units (OTUs) and then interprets interaction networks using the Lotka-Volterra model. We also embed a Bray-Curtis dissimilarity method in MetaMIS in order to evaluate the similarity to biological reality. MetaMIS is designed to tolerate a high level of missing data, and can estimate interaction information without the influence of rare microbes. For each interaction network, MetaMIS systematically examines interaction patterns (such as mutualism or competition) and refines the biotic role within microbes. As a case study, we collect a human male fecal microbiome and show that Micrococcaceae, a relatively low abundance OTU, is highly connected with 13 dominant OTUs and seems to play a critical role. MetaMIS is able to organize multiple interaction networks into a consensus network for comparative studies; thus we as a case study have also identified a consensus interaction network between female and male fecal microbiomes. MetaMIS provides an efficient and user-friendly platform that may reveal new insights into metagenomics data. MetaMIS is freely available at: https://sourceforge.net/projects/metamis/ .

Twitter Demographics

The data shown below were collected from the profiles of 27 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
Ireland 1 <1%
Belgium 1 <1%
Germany 1 <1%
Unknown 159 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 22%
Student > Ph. D. Student 34 21%
Student > Master 23 14%
Student > Bachelor 20 12%
Student > Doctoral Student 11 7%
Other 20 12%
Unknown 21 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 28%
Biochemistry, Genetics and Molecular Biology 26 16%
Environmental Science 16 10%
Engineering 12 7%
Computer Science 8 5%
Other 20 12%
Unknown 36 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 20 July 2017.
All research outputs
#1,179,647
of 18,822,351 outputs
Outputs from BMC Bioinformatics
#240
of 6,437 outputs
Outputs of similar age
#34,113
of 406,651 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 425 outputs
Altmetric has tracked 18,822,351 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,437 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 96% 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 406,651 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 91% of its contemporaries.
We're also able to compare this research output to 425 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 93% of its contemporaries.