Novel Approach to Classify Plants Based on Metabolite-Content Similarity
Joint Authors
Altaf-Ul-Amin, Md.
Abdullah, Azian Azamimi
Nishioka, Takaaki
Huang, Ming
Liu, Kang
Kanaya, Shigehiko
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Secondary metabolites are bioactive substances with diverse chemical structures.
Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes).
This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants.
However, such a chemical taxonomic approach has limitations of incomplete metabolomics data.
We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content.
Structurally similar metabolites have been clustered using the network clustering algorithm DPClus.
Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward’s method of hierarchical clustering.
Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants.
This finding reveals the significance of metabolite content as a taxonomic marker.
We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants.
As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
American Psychological Association (APA)
Liu, Kang& Abdullah, Azian Azamimi& Huang, Ming& Nishioka, Takaaki& Altaf-Ul-Amin, Md.& Kanaya, Shigehiko. 2017. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. BioMed Research International،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1137544
Modern Language Association (MLA)
Liu, Kang…[et al.]. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. BioMed Research International No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1137544
American Medical Association (AMA)
Liu, Kang& Abdullah, Azian Azamimi& Huang, Ming& Nishioka, Takaaki& Altaf-Ul-Amin, Md.& Kanaya, Shigehiko. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1137544
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137544