Summary
MetMiner represents a comprehensive bioinformatics pipeline designed to democratise large-scale plant metabolomics analysis. Built on R Shiny for accessibility and computational scalability, the tool integrates a plant-specific metabolite database, statistical analysis modules, and an iterative weighted co-expression network strategy for biomarker discovery. Two case studies validated the pipeline's effectiveness in metabolite annotation and data mining, positioning it as a practical resource for researchers without programming expertise seeking to extract biological insights from complex metabolomics datasets.
UK applicability
MetMiner provides UK plant research teams—whether in agronomy, horticulture, or plant breeding—with an open-source, accessible tool for metabolomics analysis that could support mechanistic studies of crop stress response, nutrient density, and cultivar selection. Its server-deployable architecture suits institutional research environments and collaborative multi-site studies common in UK agricultural research networks.
Key measures
Software functionality for metabolite annotation, data processing efficiency, robustness of biomarker identification, user interface accessibility, reproducibility of analytical workflows
Outcomes reported
MetMiner, an R Shiny-based software pipeline, enables user-friendly analysis of large-scale plant metabolomics datasets without requiring programming skills. The pipeline incorporates a plant-specific mass spectrometry database, statistical analysis tools, metabolite classification, enrichment analysis, and a weighted gene co-expression network strategy for biomarker screening.
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