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Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters

Overview of attention for article published in PLoS Computational Biology, December 2011
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
6 X users

Citations

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6 Dimensions

Readers on

mendeley
73 Mendeley
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4 CiteULike
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Title
Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002228
Pubmed ID
Authors

Thadeous Kacmarczyk, Peter Waltman, Ashley Bate, Patrick Eichenberger, Richard Bonneau

Abstract

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures - results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 1 1%
Israel 1 1%
Belgium 1 1%
Poland 1 1%
Unknown 65 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 32%
Researcher 23 32%
Professor > Associate Professor 9 12%
Student > Master 4 5%
Student > Doctoral Student 3 4%
Other 8 11%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 63%
Biochemistry, Genetics and Molecular Biology 10 14%
Computer Science 4 5%
Immunology and Microbiology 2 3%
Mathematics 1 1%
Other 5 7%
Unknown 5 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 17 December 2011.
All research outputs
#3,188,053
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#2,812
of 8,958 outputs
Outputs of similar age
#23,477
of 246,209 outputs
Outputs of similar age from PLoS Computational Biology
#25
of 133 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 68% 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 246,209 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 90% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.