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Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank Candidate Effectors of Rust Fungi

Overview of attention for article published in PLOS ONE, January 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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13 X users
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Citations

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305 Mendeley
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Title
Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank Candidate Effectors of Rust Fungi
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0029847
Pubmed ID
Authors

Diane G. O. Saunders, Joe Win, Liliana M. Cano, Les J. Szabo, Sophien Kamoun, Sylvain Raffaele

Abstract

Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that known effector proteins from filamentous pathogens have at least one of the following properties: (i) contain a secretion signal, (ii) are encoded by in planta induced genes, (iii) have similarity to haustorial proteins, (iv) are small and cysteine rich, (v) contain a known effector motif or a nuclear localization signal, (vi) are encoded by genes with long intergenic regions, (vii) contain internal repeats, and (viii) do not contain PFAM domains, except those associated with pathogenicity. We used Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance components.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 305 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Mexico 3 <1%
Netherlands 2 <1%
Australia 2 <1%
Brazil 2 <1%
Germany 2 <1%
Turkey 1 <1%
South Africa 1 <1%
India 1 <1%
Other 6 2%
Unknown 279 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 26%
Researcher 57 19%
Student > Master 57 19%
Student > Bachelor 23 8%
Student > Doctoral Student 16 5%
Other 45 15%
Unknown 28 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 203 67%
Biochemistry, Genetics and Molecular Biology 48 16%
Engineering 5 2%
Medicine and Dentistry 4 1%
Computer Science 4 1%
Other 9 3%
Unknown 32 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 June 2019.
All research outputs
#3,732,786
of 23,267,128 outputs
Outputs from PLOS ONE
#46,319
of 198,831 outputs
Outputs of similar age
#31,098
of 243,916 outputs
Outputs of similar age from PLOS ONE
#541
of 3,086 outputs
Altmetric has tracked 23,267,128 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 198,831 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.2. This one has done well, scoring higher than 76% 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 243,916 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 3,086 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.