The paper discusses the development and application of the Kyoto Encyclopedia of Genes and Genomes (KEGG) as a reference knowledge base for biological interpretation of genome sequences and high-throughput data. It emphasizes the importance of computational methods in extracting information from data, but also highlights the need for reference databases to compile knowledge. KEGG has been expanded to include various types of high-throughput data, such as metagenomes, transcriptomes, and metabolomes, and now includes drug labels from Japan and the USA. The KEGG Orthology (KO) system is used to link genomes to pathways, and KEGG modules defined by Boolean expressions of K numbers have been improved to automate the interpretation of phenotypic features. The KEGG modules are used to analyze the design principles of metabolic networks and to improve the definition of K numbers and associated annotations. The KEGG MEDICUS resource has been developed to integrate drug labels and drug products, enabling the integration of scientific and practical data. The paper also discusses the use of reaction modules to improve K number grouping and annotations, and the modular architecture of metabolic networks. The KEGG pathway maps are used to present an overview of metabolic networks, and the paper highlights the importance of integrating different types of data and information to understand the basic principles of metabolic networks. The paper also discusses the use of KEGG in translational bioinformatics, which aims to bring research results into practical applications and help understand the scientific basis of diseases and drugs of personal interest. The KEGG resource is available at both the KEGG website and the GenomeNet website, and the internal KEGG Oracle database is copied daily to a public version. The paper concludes that KEGG is a valuable resource for integrating and interpreting large-scale molecular datasets and for understanding the basic principles of metabolic networks.The paper discusses the development and application of the Kyoto Encyclopedia of Genes and Genomes (KEGG) as a reference knowledge base for biological interpretation of genome sequences and high-throughput data. It emphasizes the importance of computational methods in extracting information from data, but also highlights the need for reference databases to compile knowledge. KEGG has been expanded to include various types of high-throughput data, such as metagenomes, transcriptomes, and metabolomes, and now includes drug labels from Japan and the USA. The KEGG Orthology (KO) system is used to link genomes to pathways, and KEGG modules defined by Boolean expressions of K numbers have been improved to automate the interpretation of phenotypic features. The KEGG modules are used to analyze the design principles of metabolic networks and to improve the definition of K numbers and associated annotations. The KEGG MEDICUS resource has been developed to integrate drug labels and drug products, enabling the integration of scientific and practical data. The paper also discusses the use of reaction modules to improve K number grouping and annotations, and the modular architecture of metabolic networks. The KEGG pathway maps are used to present an overview of metabolic networks, and the paper highlights the importance of integrating different types of data and information to understand the basic principles of metabolic networks. The paper also discusses the use of KEGG in translational bioinformatics, which aims to bring research results into practical applications and help understand the scientific basis of diseases and drugs of personal interest. The KEGG resource is available at both the KEGG website and the GenomeNet website, and the internal KEGG Oracle database is copied daily to a public version. The paper concludes that KEGG is a valuable resource for integrating and interpreting large-scale molecular datasets and for understanding the basic principles of metabolic networks.