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The use of web resources for metabolomics in horticultural crops.

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Bibliographic Details
Title:	The use of web resources for metabolomics in horticultural crops.
Authors:	Karakas, Esra, Bulut, Mustafa, Fernie, Alisdair R.
Source:	Horticulture Advances; Dec2025, Vol. 3 Issue 1, p1-14, 14p
Subject Terms:	METABOLOMICS, DATABASES, BIOINFORMATICS, CROP yields, HORTICULTURAL crops, MACHINE learning, BOTANY, WEB-based user interfaces
Abstract:	Metabolomics, a rapidly evolving field, has revolutionized horticultural crop research by enabling comprehensive analysis of metabolites that influence plant yield, growth, quality and nutritional value. The integration of web-based resources, including databases, computational tools and analytical platforms has significantly enhanced metabolomics studies by facilitating data processing, metabolite identification and pathway analysis. Moreover, the application of machine learning algorithms to these web resources has further optimized data interpretation, enabling more accurate prediction of metabolic profiles. Publicly available reference libraries and bioinformatic tools support precision of breeding, postharvest quality assessment and ultimately improving crop yield and sustainability. In this mini-review, we explore the current status of the diverse range of plant metabolomics databases in horticultural crops, highlighting the synergy between machine learning and traditional bioinformatics methods, their applications, challenges and future prospects in advancing plant science and agricultural innovation. [ABSTRACT FROM AUTHOR]
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Database:	Complementary Index

Description
Abstract:	Metabolomics, a rapidly evolving field, has revolutionized horticultural crop research by enabling comprehensive analysis of metabolites that influence plant yield, growth, quality and nutritional value. The integration of web-based resources, including databases, computational tools and analytical platforms has significantly enhanced metabolomics studies by facilitating data processing, metabolite identification and pathway analysis. Moreover, the application of machine learning algorithms to these web resources has further optimized data interpretation, enabling more accurate prediction of metabolic profiles. Publicly available reference libraries and bioinformatic tools support precision of breeding, postharvest quality assessment and ultimately improving crop yield and sustainability. In this mini-review, we explore the current status of the diverse range of plant metabolomics databases in horticultural crops, highlighting the synergy between machine learning and traditional bioinformatics methods, their applications, challenges and future prospects in advancing plant science and agricultural innovation. [ABSTRACT FROM AUTHOR]
DOI:	10.1007/s44281-025-00073-8