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Predictive Sequence Analysis of the Candidatus Liberibacter asiaticus Proteome

Overview of attention for article published in PLOS ONE, July 2012
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Title
Predictive Sequence Analysis of the Candidatus Liberibacter asiaticus Proteome
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0041071
Pubmed ID
Authors

Qian Cong, Lisa N. Kinch, Bong-Hyun Kim, Nick V. Grishin

Abstract

Candidatus Liberibacter asiaticus (Ca. L. asiaticus) is a parasitic gram-negative bacterium that is closely associated with Huanglongbing (HLB), a worldwide citrus disease. Given the difficulty in culturing the bacterium and thus in its experimental characterization, computational analyses of the whole Ca. L. asiaticus proteome can provide much needed insights into the mechanisms of the disease and guide the development of treatment strategies. In this study, we applied state-of-the-art sequence analysis tools to every Ca. L. asiaticus protein. Our results are available as a public website at http://prodata.swmed.edu/liberibacter_asiaticus/. In particular, we manually curated the results to predict the subcellular localization, spatial structure and function of all Ca. L. asiaticus proteins (http://prodata.swmed.edu/liberibacter_asiaticus/curated/). This extensive information should facilitate the study of Ca. L. asiaticus proteome function and its relationship to disease. Pilot studies based on the information from our website have revealed several potential virulence factors, discussed herein.

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The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Mexico 1 1%
Netherlands 1 1%
Brazil 1 1%
Unknown 65 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 37%
Student > Ph. D. Student 15 21%
Student > Master 9 13%
Student > Doctoral Student 4 6%
Student > Postgraduate 4 6%
Other 10 14%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 68%
Biochemistry, Genetics and Molecular Biology 15 21%
Unspecified 1 1%
Business, Management and Accounting 1 1%
Computer Science 1 1%
Other 2 3%
Unknown 3 4%