ArticleScientific data2018
The natural variance of the Arabidopsis floral secondary metabolites.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Article
- A single-cell, spatial transcriptomic atlas of the Arabidopsis life cycle.Nature plants · 2025Article
- NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy.BMC plant biology · 2024Article
- The Changes of Microbial Communities and Key Metabolites after EarlyPlants (Basel, Switzerland) · 2022Article
- Plant and pest: The art of (cyanide) war.Plant physiology · 2022Article
- A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.Frontiers in plant science · 2022Article
- Cross-Species Metabolic Profiling of Floral Specialized Metabolism Facilitates Understanding of Evolutional Aspects of Metabolism Among Brassicaceae Species.Frontiers in plant science · 2021Article
- On the road to robust data citation.Scientific data · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Application of mass spectrometry-based metabolomics enables the detection of genotype-related natural variance in metabolism. Differences in secondary metabolite composition of flowers of 64 Arabidopsis thaliana (Arabidopsis) natural accessions, representing a considerable portion of the natural variation in this species are presented. The raw metabolomic data of the accessions and reference extracts derived from flavonoid knockout mutants have been deposited in the MetaboLights database. Additionally, summary tables of floral secondary metabolite data are presented in this article to enable efficient re-use of the dataset either in metabolomics cross-study comparisons or correlation-based integrative analysis of other metabolomic and phenotypic features such as transcripts, proteins and growth and flowering related phenotypes.
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