reactome-database

Reactome Database - Biological Pathway Analysis and Enrichment Tool

Overview

Reactome Database is a free, open-source pathway database tool that provides pathway enrichment analysis, gene expression analysis, molecular interaction queries, and pathway visualization via a REST API and a Python client, suitable for systems biology research.

Use Cases

Gene/Protein Enrichment Analysis

When you have a list of genes or proteins and need to identify the biological pathways they participate in and which pathways are significantly enriched, this tool can quickly perform Overrepresentation Analysis to obtain statistically significant pathway results.

Pathway Analysis of Gene Expression Data

When you have gene expression datasets (e.g., RNA-seq or microarray data) and need to analyze which pathways are activated or suppressed under specific conditions, use the tool's expression data analysis feature to combine quantitative values for pathway-level interpretation.

Biological Pathway Query and Visualization

When you need to query specific pathway information, explore molecular interactions, or visualize analysis results in the Reactome Pathway Browser, this tool can provide the complete pathway hierarchy, participating molecules, and reaction details.

Core Features

Content Service - Pathway Data Query

Query pathway information, molecular entities, reactions, and interactions in the Reactome database via REST API. Supports retrieving database version, pathway hierarchy, participating molecules, event details, etc., and returns JSON-formatted data for programmatic processing.

Analysis Service - Pathway Enrichment and Expression Analysis

Provides two analysis types: Overrepresentation Analysis (gene list enrichment analysis) and Expression Data Analysis (expression data analysis). Supports multiple gene identifier formats (UniProt, Gene Symbol, Ensembl, EntrezGene). Analysis results include statistical metrics such as p-value and FDR. Tokens are valid for 7 days.

reactome2py Python client

Provides a Python package that wraps API calls to simplify interaction with Reactome. Although the current version (3.0.0, released 2021) has limited maintenance, its core functionality is fully usable. For the latest features, it is recommended to use the REST API directly.

Frequently Asked Questions

What gene identifier formats does Reactome support?

Reactome supports a variety of common identifiers, including UniProt accession numbers (e.g., P04637), gene symbols (e.g., TP53), Ensembl IDs (e.g., ENSG00000141510), EntrezGene IDs (e.g., 7157), and small-molecule ChEBI IDs. The system automatically detects the identifier type, so no manual specification is required.

What is the difference between the reactome2py package and calling the REST API directly?

reactome2py is a Python wrapper for the Reactome API that allows for more concise calls after installation. However, this package (version 3.0.0) has not been actively maintained since 2021 and may lack the latest features. Direct use of the REST API requires a bit more code but provides access to the newest functionality. It is recommended to use reactome2py for simple tasks and the REST API for complex scenarios or new features.

How can Reactome analysis results be visualized?

After analysis completes, you receive a token (valid for 7 days). By constructing a specific URL you can visualize the results in the Reactome Pathway Browser. The format is: https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}. This allows interactive browsing of pathway diagrams, viewing gene locations within pathways, and viewing statistical information.

How much data does Reactome currently contain?

As of version 94 (September 2025), Reactome contains 2,825 human pathways, 16,002 reactions, 11,630 proteins, 2,176 small molecules, 1,070 drugs, and 41,373 literature references. The database is continuously updated; you can query the current version via the Content Service.

What are the input format requirements for expression data analysis?

Expression data must be in TSV format, with the header line beginning with a '#'. The first column should be gene identifiers, and subsequent columns should contain numeric expression values, using a decimal point as the separator. For example: #Gene Sample1 Sample2 followed by data rows. Note that identifiers must be compatible with the Reactome database.