MetaMapR

MetaMapR integrates enzymatic transformations, metabolite structural similarity, mass spectral similarity, and empirical associations to construct richly connected metabolic networks that mitigate sparsity caused by incomplete biochemical domain and pathway annotations.

Key Features:

• Integration of Multiple Data Types: Combines enzymatic transformations with metabolite structural similarity, mass spectral similarity, and empirical associations to generate connected metabolic networks.
• Database Utilization: Leverages biochemical databases including KEGG and PubChem to derive empirical associations between metabolites.
• Cross-Database Compatibility: Interfaces with the Chemical Translation System (CTS) to translate metabolite identifiers across more than 200 common biochemical databases.
• Implementation: Implemented in the R programming language.
• Export and Interoperability: Produces numerical output compatible with downstream network analysis and visualization tools.

Scientific Applications:

• Metabolomics data integration: Integrates experimental metabolomics data with biochemical domain knowledge to enhance network connectivity and interpretation.
• Metabolic pathway and network analysis: Facilitates reconstruction and analysis of metabolic pathways and interactions when annotations are incomplete or ambiguous.
• Drug discovery and biomarker identification: Supports applied studies by enabling discovery of pathway-level relationships relevant to drug targets and disease biomarkers.

Methodology:

Systematic integration of enzymatic transformations with structural similarity and mass spectral similarity, supported by empirical associations from databases such as KEGG and PubChem, and translation of metabolite identifiers across >200 databases via the Chemical Translation System (CTS).