PMhub

PMhub provides a curated database of 188,837 plant metabolites with reference spectra, genetic information, metabolic pathways, and 1,467,041 in-silico HRMS/MS spectra for metabolite annotation and metabolic network analysis.

Key Features:

• Comprehensive metabolite collection: Contains data on 188,837 plant metabolites including reference metabolites and derived features.
• Reference spectra: Includes reference spectra for metabolites used as anchors for annotation and comparison.
• In-silico HRMS/MS library: Provides 1,467,041 in-silico HRMS/MS spectra for spectral matching and annotation.
• Detected metabolic features: Catalogues 144,366 detected metabolic features across ten plant species.
• In-silico annotation: Reports 16,423 features annotated using in-silico HRMS/MS data.
• Metabolic pathway data: Integrates known metabolic pathways linked to metabolites.
• Metabolic network reconstruction: Reconstructs simulated networks for metabolites based on structural similarities and known pathways.
• Genomic and transcriptomic integration: Incorporates genomic and transcriptomic data to support genetic analysis of metabolites.
• Curated multi-source data: Aggregates literature-derived and experimental metabolite information from multiple sources.

Scientific Applications:

• Metabolite annotation: Use HRMS/MS reference and in-silico spectra to annotate detected metabolic features.
• Metabolic network analysis: Simulate and analyze metabolite networks based on structural similarity and known pathways.
• Genetic association of metabolites: Link metabolites to genomic and transcriptomic data for genetic analysis.
• Comparative plant metabolomics: Compare detected metabolic features across ten plant species.
• Spectral library expansion: Leverage the large in-silico HRMS/MS library to expand spectral coverage for plant metabolites.

Methodology:

Data are curated from multiple sources; 1,467,041 in-silico HRMS/MS spectra are generated/used for annotation (16,423 features annotated); simulated metabolic networks are reconstructed using structural similarities and known pathways; genomic and transcriptomic data are integrated for genetic analysis.