Reactome presents a high-performance in-memory approach for pathway analysis. The method uses four steps, each optimized with specific data structures to improve performance and reduce memory usage. The first step uses a radix tree for efficient identifier lookup in Reactome. The second step models proteins, chemicals, and their orthologs using a graph. The third and fourth steps aggregate results and calculate statistics using a double-linked tree. This approach allows Reactome to handle genome-wide datasets quickly, enabling interactive analysis. The pathway analysis is available via the AnalysisService or the PathwayBrowser. Reactome is an open-source project with all source code available on GitHub. The method improves performance by using in-memory data structures and algorithms, allowing fast and reliable analysis. Compared to other pathway analysis tools, Reactome offers a stable, high-performance solution with programmatic access and a user-friendly interface. The system handles a large number of analysis requests efficiently, with low memory usage. The approach is based on a combination of data structures and algorithms, making it suitable for high-throughput data analysis. The method is implemented in Java and is available as a web service or through a graphical user interface. Reactome provides a comprehensive set of tools for pathway analysis, supporting both programmatic and interactive use. The system is designed to handle large datasets efficiently, with fast response times and minimal server load. The approach is scalable and suitable for a wide range of applications in bioinformatics and systems biology.Reactome presents a high-performance in-memory approach for pathway analysis. The method uses four steps, each optimized with specific data structures to improve performance and reduce memory usage. The first step uses a radix tree for efficient identifier lookup in Reactome. The second step models proteins, chemicals, and their orthologs using a graph. The third and fourth steps aggregate results and calculate statistics using a double-linked tree. This approach allows Reactome to handle genome-wide datasets quickly, enabling interactive analysis. The pathway analysis is available via the AnalysisService or the PathwayBrowser. Reactome is an open-source project with all source code available on GitHub. The method improves performance by using in-memory data structures and algorithms, allowing fast and reliable analysis. Compared to other pathway analysis tools, Reactome offers a stable, high-performance solution with programmatic access and a user-friendly interface. The system handles a large number of analysis requests efficiently, with low memory usage. The approach is based on a combination of data structures and algorithms, making it suitable for high-throughput data analysis. The method is implemented in Java and is available as a web service or through a graphical user interface. Reactome provides a comprehensive set of tools for pathway analysis, supporting both programmatic and interactive use. The system is designed to handle large datasets efficiently, with fast response times and minimal server load. The approach is scalable and suitable for a wide range of applications in bioinformatics and systems biology.