MetMiner

MetMiner streamlines large-scale plant metabolomics data analysis through an integrated pipeline that combines metabolite annotation, statistical analysis, and biomarker screening.

Key Features:

• Large-scale data processing: Deploys on servers to utilize additional computational resources for handling extensive metabolomics datasets.
• Plant-specific mass spectrometry database: A constructed and integrated database optimizes metabolite annotation for plant metabolomics.
• MDAtoolkits: Includes a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis.
• Iterative weighted gene co-expression network analysis: A proposed strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining.
• Reproducibility: Ensures transparency, traceability, and reproducibility throughout the analytical process.

Scientific Applications:

• Plant metabolic mechanism exploration: Analyzes metabolic mechanisms underlying plant fitness and adaptation to dynamic environments.
• Metabolite annotation: Performs robust metabolite annotation using a plant-specific mass spectrometry database.
• Biomarker metabolite screening: Identifies biomarker metabolites through iterative weighted gene co-expression network analysis.
• Biological meaning mining: Facilitates extraction of biological insights from datasets through statistical analysis, metabolite classification, and enrichment analysis.

Methodology:

MetMiner is built on R shiny and integrated into TBtools. The pipeline incorporates MDAtoolkits for statistical analysis, metabolite classification, and enrichment analysis, and employs an iterative weighted gene co-expression network analysis strategy for biomarker metabolite screening.