MEANtools

MEANtools integrates multi-omics data to predict candidate metabolic pathways de novo in plants by leveraging general reaction rules and metabolic structures from public databases.

Key Features:

• Unsupervised computational integrative omics workflow: Predicts biosynthetic pathways without requiring prior knowledge of target pathways.
• Reaction rule-based metabolite-transcript connection: Identifies possible connections between metabolites and transcripts using reaction rules linked to transcript-encoded enzyme families.
• Correlation-based pathway assessment: Assesses whether reactions can connect transcript-correlated mass features within a candidate metabolic pathway.
• Multi-omics data integration: Combines genomics, transcriptomics, and metabolomics data for pathway elucidation.

Scientific Applications:

• Specialized metabolite pathway elucidation: Reconstructs biosynthetic pathways for plant specialized metabolites that remain largely elusive.
• Hypothesis generation: Identifies candidate pathways involved in specialized metabolism for experimental validation.
• Validation with characterized pathways: Correctly anticipated five out of seven steps of the characterized falcarindiol biosynthetic pathway in tomato.

Methodology:

Possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families; these reactions are then assessed for their ability to connect transcript-correlated mass features within a candidate metabolic pathway.