Reactome

Reactome provides a manually curated database of human biological pathways and molecular processes to support pathway annotation and functional analysis.

Key Features:

• Manual curation and data model: Biologist-curated molecular details are annotated as ordered networks of molecular transformations within a consistent data model.
• Pathway visualization (SBGN): Pathway representations use Systems Biology Graphical Notation (SBGN) for standardized visual encoding.
• Interaction integration (PSIQUIC): Integrates molecular interaction data via PSIQUIC web services and overlays a Functional Interaction Network with external databases including IntAct, BioGRID, ChEMBL, iRefIndex, MINT, and STRING.
• Pathway analysis tools: Provides ID mapping, pathway assignment, and overrepresentation analysis for user-supplied datasets.
• Orthology-based inference (Ensembl Compara): Infers pathways for 20 other species using Ensembl Compara and supports comparative analyses via a Species Comparison tool.
• Expanded annotations: Includes ongoing annotation efforts covering the entire human proteome, disease-causing genetic variants, small-molecule drugs, and cell- and tissue-specific pathways.
• Interoperability: Supports mappings and interoperability efforts with external resources such as the Gene Ontology.
• Data archive and analytical role: Serves as a digital archive and analytical resource for datasets including gene expression profiles and somatic mutation catalogs from tumor cells.

Scientific Applications:

• Pathway annotation: Curating and representing molecular pathways and regulatory processes in a consistent data model.
• Functional interpretation of omics data: Interpreting gene expression profiles through pathway assignment and overrepresentation analysis.
• Somatic mutation analysis: Mapping and analyzing somatic mutation catalogs from tumor cells within pathway contexts.
• Comparative pathway analysis: Comparing inferred pathways across species using Ensembl Compara and the Species Comparison tool.
• Variant and drug contextualization: Placing disease-causing genetic variants and small-molecule drugs into pathway contexts for mechanistic interpretation.

Methodology:

Manual curation by biologists; annotation as ordered networks of molecular transformations within a consistent data model; visualization using Systems Biology Graphical Notation (SBGN); integration of molecular interactions via PSIQUIC and overlays with IntAct, BioGRID, ChEMBL, iRefIndex, MINT, and STRING; orthology-based pathway inference using Ensembl Compara; pathway analysis methods including ID mapping, pathway assignment, and overrepresentation analysis; Species Comparison tool for cross-species inferences.