RaMP-DB

RaMP-DB integrates pathway, chemical class, and structural annotations to support functional interpretation of metabolomic and multi-omic datasets.

Key Features:

• Expanded annotation content: Integrates updated annotations from source databases including Reactome, HMDB, and Wikipathways.
• Structural information: Stores metabolite structural identifiers including SMILES, InChIs, and InChIKeys.
• Chemical class integration: Incorporates chemical class data from ClassyFire and LIPID MAPS for metabolite categorization.
• Query capabilities: Supports queries for pathways, common reactions, ontologies, chemical classes, and chemical structures via an R package and an API.
• Enrichment analyses support: Provides functionality for enrichment analyses on pathways and chemical classes.
• Annotation integration and harmonization: Harmonizes annotations across multiple resources to facilitate integrated analyses and updates.
• Programmatic access and storage: Provides a relational database backend accessible programmatically through an API and an R package.

Scientific Applications:

• Functional interpretation: Enables functional interpretation of metabolomic and multi-omic datasets by linking metabolites to pathways and classes.
• Pathway analysis: Supports mapping metabolites to biological pathways for pathway-level interpretation using Reactome and Wikipathways annotations.
• Chemical classification: Facilitates classification and categorization of metabolites using ClassyFire and LIPID MAPS annotations.
• Structural identification and interoperability: Uses SMILES, InChIs, and InChIKeys to enable precise chemical identification and interoperability with cheminformatics tools.
• Enrichment detection: Detects statistically significant associations within datasets through enrichment analyses on pathways and chemical classes.

Methodology:

Integrates updated annotations from Reactome, HMDB, and Wikipathways into a relational database, incorporates SMILES, InChIs, and InChIKeys for metabolites, imports chemical class data from ClassyFire and LIPID MAPS, harmonizes annotations across resources, and exposes query and enrichment functionality via an R package and an API.