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Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria

Overview of attention for article published in BMC Genomics, November 2005
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

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2 Wikipedia pages

Citations

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36 Dimensions

Readers on

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23 Mendeley
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Title
Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria
Published in
BMC Genomics, November 2005
DOI 10.1186/1471-2164-6-162
Pubmed ID
Authors

Sébastien Rey, Jennifer L Gardy, Fiona SL Brinkman

Abstract

Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 13%
Student > Postgraduate 2 9%
Student > Doctoral Student 1 4%
Student > Ph. D. Student 1 4%
Researcher 1 4%
Other 1 4%
Unknown 14 61%
Readers by discipline Count As %
Medicine and Dentistry 5 22%
Agricultural and Biological Sciences 2 9%
Social Sciences 2 9%
Computer Science 1 4%
Nursing and Health Professions 1 4%
Other 0 0%
Unknown 12 52%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 June 2014.
All research outputs
#3,577,604
of 12,373,386 outputs
Outputs from BMC Genomics
#2,328
of 7,296 outputs
Outputs of similar age
#77,991
of 264,849 outputs
Outputs of similar age from BMC Genomics
#81
of 252 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,296 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 60% 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 264,849 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 66% of its contemporaries.
We're also able to compare this research output to 252 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.