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New players in the same old game: a system level in silico study to predict type III secretion system and effector proteins in bacterial genomes reveals common themes in T3SS mediated pathogenesis

Overview of attention for article published in BMC Research Notes, July 2013
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Title
New players in the same old game: a system level in silico study to predict type III secretion system and effector proteins in bacterial genomes reveals common themes in T3SS mediated pathogenesis
Published in
BMC Research Notes, July 2013
DOI 10.1186/1756-0500-6-297
Pubmed ID
Authors

Vineet Sadarangani, Sunando Datta, Manonmani Arunachalam

Abstract

Type III secretion system (T3SS) plays an important role in virulence or symbiosis of many pathogenic or symbiotic bacteria [CHM 2:291-294, 2007; Physiology (Bethesda) 20:326-339, 2005]. T3SS acts like a tunnel between a bacterium and its host through which the bacterium injects 'effector' proteins into the latter [Nature 444:567-573, 2006; COSB 18:258-266, 2008]. The effectors spatially and temporally modify the host signalling pathways [FEMS Microbiol Rev 35:1100-1125, 2011; Cell Host Microbe5:571-579, 2009]. In spite its crucial role in host-pathogen interaction, the study of T3SS and the associated effectors has been limited to a few bacteria [Cell Microbiol 13:1858-1869, 2011; Nat Rev Microbiol 6:11-16, 2008; Mol Microbiol 80:1420-1438, 2011]. Before one set out to perform systematic experimental studies on an unknown set of bacteria it would be beneficial to identify the potential candidates by developing an in silico screening algorithm. A system level study would also be advantageous over traditional laboratory methods to extract an overriding theme for host-pathogen interaction, if any, from the vast resources of data generated by sequencing multiple bacterial genomes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
France 1 3%
Turkey 1 3%
United Kingdom 1 3%
Austria 1 3%
Unknown 27 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 30%
Student > Master 7 21%
Researcher 7 21%
Professor > Associate Professor 3 9%
Student > Doctoral Student 2 6%
Other 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 58%
Biochemistry, Genetics and Molecular Biology 7 21%
Computer Science 3 9%
Immunology and Microbiology 1 3%
Medicine and Dentistry 1 3%
Other 0 0%
Unknown 2 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 August 2013.
All research outputs
#20,196,821
of 22,715,151 outputs
Outputs from BMC Research Notes
#3,553
of 4,257 outputs
Outputs of similar age
#173,941
of 197,903 outputs
Outputs of similar age from BMC Research Notes
#54
of 62 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,257 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 62 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.