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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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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

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%
Other 1 4%
Lecturer 1 4%
Student > Doctoral Student 1 4%
Other 3 13%
Unknown 12 52%
Readers by discipline Count As %
Medicine and Dentistry 6 26%
Agricultural and Biological Sciences 3 13%
Social Sciences 2 9%
Unspecified 1 4%
Nursing and Health Professions 1 4%
Other 1 4%
Unknown 9 39%
Attention Score in Context

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
#7,453,350
of 22,786,087 outputs
Outputs from BMC Genomics
#3,597
of 10,647 outputs
Outputs of similar age
#37,870
of 146,270 outputs
Outputs of similar age from BMC Genomics
#5
of 11 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,647 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% 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 146,270 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.