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A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

Overview of attention for article published in PLoS Computational Biology, February 2009
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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)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

dimensions_citation
120 Dimensions

Readers on

mendeley
279 Mendeley
citeulike
11 CiteULike
connotea
2 Connotea
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Title
A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189
Published in
PLoS Computational Biology, February 2009
DOI 10.1371/journal.pcbi.1000285
Pubmed ID
Authors

Patrick F. Suthers, Madhukar S. Dasika, Vinay Satish Kumar, Gennady Denisov, John I. Glass, Costas D. Maranas

Abstract

With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 279 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
United Kingdom 8 3%
Germany 4 1%
France 2 <1%
Iran, Islamic Republic of 2 <1%
Russia 2 <1%
Portugal 2 <1%
India 2 <1%
Sweden 1 <1%
Other 5 2%
Unknown 242 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 74 27%
Researcher 66 24%
Student > Master 46 16%
Student > Bachelor 19 7%
Professor > Associate Professor 14 5%
Other 37 13%
Unknown 23 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 142 51%
Biochemistry, Genetics and Molecular Biology 37 13%
Computer Science 18 6%
Engineering 16 6%
Chemical Engineering 8 3%
Other 23 8%
Unknown 35 13%
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 09 July 2022.
All research outputs
#3,274,685
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#2,895
of 8,960 outputs
Outputs of similar age
#16,213
of 189,057 outputs
Outputs of similar age from PLoS Computational Biology
#8
of 39 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 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 67% 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 189,057 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 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.