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Use of metabolomics for the chemotaxonomy of legume-associated Ascochyta and allied genera

Overview of attention for article published in Scientific Reports, February 2016
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Article details
Title
Use of metabolomics for the chemotaxonomy of legume-associated Ascochyta and allied genera
Published in
Scientific Reports, February 2016
DOI 10.1038/srep20192
Pubmed ID
Authors
Abstract

Chemotaxonomy and the comparative analysis of metabolic features of fungi have the potential to provide valuable information relating to ecology and evolution, but have not been fully explored in fungal biology. Here, we investigated the chemical diversity of legume-associated Ascochyta and Phoma species and the possible use of a metabolomics approach using liquid chromatography-mass spectrometry for their classification. The metabolic features of 45 strains including 11 known species isolated from various legumes were extracted, and the datasets were analyzed using chemometrics methods such as principal component and hierarchical clustering analyses. We found a high degree of intra-species consistency in metabolic profiles, but inter-species diversity was high. Molecular phylogenies of the legume-associated Ascochyta/Phoma species were estimated using sequence data from three protein-coding genes and the five major chemical groups that were detected in the hierarchical clustering analysis were mapped to the phylogeny. Clusters based on similarity of metabolic features were largely congruent with the species phylogeny. These results indicated that evolutionarily distinct fungal lineages have diversified their metabolic capacities as they have evolved independently. This whole metabolomics approach may be an effective tool for chemotaxonomy of fungal taxa lacking information on their metabolic content.

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The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 18 22%
Researcher 15 18%
Student > Doctoral Student 9 11%
Student > Master 5 6%
Student > Bachelor 3 4%
Other 10 12%
Unknown 22 27%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 28 34%
Chemistry 10 12%
Biochemistry, Genetics and Molecular Biology 9 11%
Pharmacology, Toxicology and Pharmaceutical Science 5 6%
Medicine and Dentistry 3 4%
Other 4 5%
Unknown 23 28%
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 06 February 2016.
All research outputs
#20,305,223
of 22,844,985 outputs
Outputs from Scientific Reports
#105,399
of 123,354 outputs
Outputs of similar age
#334,186
of 397,234 outputs
Outputs of similar age from Scientific Reports
#2,856
of 3,222 outputs
Altmetric has tracked 22,844,985 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 123,354 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. 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 3,222 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.