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Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions

Overview of attention for article published in BMC Genomics, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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9 X users

Citations

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37 Dimensions

Readers on

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143 Mendeley
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1 CiteULike
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Title
Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions
Published in
BMC Genomics, April 2017
DOI 10.1186/s12864-017-3676-8
Pubmed ID
Authors

Colin W. Brown, Viswanadham Sridhara, Daniel R. Boutz, Maria D. Person, Edward M. Marcotte, Jeffrey E. Barrick, Claus O. Wilke

Abstract

Post-translational modification (PTM) of proteins is central to many cellular processes across all domains of life, but despite decades of study and a wealth of genomic and proteomic data the biological function of many PTMs remains unknown. This is especially true for prokaryotic PTM systems, many of which have only recently been recognized and studied in depth. It is increasingly apparent that a deep sampling of abundance across a wide range of environmental stresses, growth conditions, and PTM types, rather than simply cataloging targets for a handful of modifications, is critical to understanding the complex pathways that govern PTM deposition and downstream effects. We utilized a deeply-sampled dataset of MS/MS proteomic analysis covering 9 timepoints spanning the Escherichia coli growth cycle and an unbiased PTM search strategy to construct a temporal map of abundance for all PTMs within a 400 Da window of mass shifts. Using this map, we are able to identify novel targets and temporal patterns for N-terminal N α acetylation, C-terminal glutamylation, and asparagine deamidation. Furthermore, we identify a possible relationship between N-terminal N α acetylation and regulation of protein degradation in stationary phase, pointing to a previously unrecognized biological function for this poorly-understood PTM. Unbiased detection of PTM in MS/MS proteomics data facilitates the discovery of novel modification types and previously unobserved dynamic changes in modification across growth timepoints.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
China 1 <1%
Unknown 141 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 25%
Researcher 23 16%
Student > Bachelor 23 16%
Student > Master 19 13%
Student > Doctoral Student 6 4%
Other 18 13%
Unknown 18 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 54 38%
Agricultural and Biological Sciences 30 21%
Immunology and Microbiology 7 5%
Chemistry 6 4%
Pharmacology, Toxicology and Pharmaceutical Science 4 3%
Other 21 15%
Unknown 21 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 19 October 2017.
All research outputs
#6,002,812
of 24,063,285 outputs
Outputs from BMC Genomics
#2,363
of 10,892 outputs
Outputs of similar age
#90,569
of 313,562 outputs
Outputs of similar age from BMC Genomics
#48
of 202 outputs
Altmetric has tracked 24,063,285 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,892 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 78% 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 313,562 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 202 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.