Genome annotation

Genome annotation in KEGG assigns KO (KEGG Orthology) identifiers or K numbers to genes in a single genome or simultaneously to genes in multiple genomes. With the addition or revision of a KEGG pathway map or BRITE hierarchy, KO groups (K numbers) are defined for the pathway nodes (boxes) or the hierarchy nodes (bottom leaves). Then the corresponding genes in selected organisms (usually in the literature) are manually annotated with the new K numbers, which are reflected in KEGG GENES. Thus, KEGG GENES can be used as a reference database for genome annotation. The number of KO groups has been increasing at a rate of about 2000 per year, and it is now over 10 000.

The KO assignment is applied to a new genome as follows. First, the new genome is subject to SSDB computation, a comparison of protein coding genes against all existing genomes by the SSEARCH program. The result is stored in KEGG SSDB containing sequence similarity scores and best-hit information for all gene pairs. Then, computational KO assignment is done by the KAAS-SSDB program, followed by manual verification and additional assignment with the GFIT tool. An automated version of this genome annotation procedure is made available as the KAAS web service (3), which utilizes BLAST rather than SSEARCH for pairwise genome comparisons.

The KO system is the basis for linking genomes to biological systems through the process of pathway mapping and BRITE mapping. For each organism in KEGG, organism-specific pathways and BRITE hierarchies are computationally generated based on its assigned K numbers. Microarray gene expression profile data may then be mapped to these pathways and hierarchies to infer systemic functions of the cell or the organism. In addition to the hierarchies of genes and proteins (K numbers), KEGG BRITE contains the hierarchies of chemical substances (C, D, G, R numbers) together with known relationships to K numbers, such as ligand–receptor interactions and drug–target relationships. By using these relationships, the BRITE mapping will be improved to present clues for understanding the interactions with the environments.

Chemical annotation

The KO system can also be used for chemical annotation, which is the linking of genomic or transcriptomic contents of genes to chemical structures of endogenous molecules. This is achieved by finer classifications of KO groups for specific classes of enzymes distinguishing different substrate specificity, as well as accumulating knowledge of biosynthetic pathways. For example, glycans are synthesized by a series of reactions catalyzed by glycosyltransferases. With the KEGG pathway maps for glycan structures (map01030 and map01031) or the KEGG GLYCAN composite structure map (4), where edges (glycosidic linkages) correspond to K numbers (glycosyltransferase orthologs), the gene content in the genome can be converted to possible glycan structures. In a similar but more sophisticated way, glycan structures can be predicted from microarray gene expression data (5). The KEGG resource will be made suitable to cope with the diversity of other molecules as well, including polyketides/non-ribosomal peptides (6), polyunsaturated fatty acids and terpenoids.

Another type of chemical annotation is to characterize biological meaning in the chemical structures of small molecules. As reported previously (2), the knowledge of enzymatic reactions and associated chemical structure transformations is stored in KEGG REACTION and KEGG RPAIR. Each structure transformation is characterized by the RDM pattern (7), and most of the patterns are found uniquely or preferentially in specific categories of KEGG pathways (8). This tendency was used to predict the metabolic fate of xenobiotic chemical compounds. Software for reaction/pathway prediction is being developed as an upgrade of e-zyme and PathComp in KEGG LIGAND.

Enhancements to KEGG pathway

KEGG PATHWAY has been significantly expanded over the last 2 years with the addition of about 50 new pathway maps, mostly for signal transduction, cellular processes and human diseases. However, the traditional KEGG metabolic pathway maps are still most widely used including the KGML (KEGG XML) version. They are now supplemented with two new features introduced as a response to user feedback. The first feature is a global map shown in Figure 1, which is created as an SVG file by manually combining about 120 existing maps. Each node (circle) is a chemical compound and each line (curved or straight) connecting two nodes is a series of reactions (one to several reactions), which is also manually defined as a segment lacking branches. The new KEGG metabolism map allows the user to view and compare the entire metabolism, such as by mapping metagenomics data or microarray data. KGML users should also find the new KEGG metabolism map much easier to manipulate.

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Figure 1.

The new KEGG metabolism map created as an SVG file.

The other feature is KEGG MODULE, a new database that collects pathway modules and other functional units as a set of K numbers. Pathway modules are smaller pieces of subpathways (see the BRITE hierarchy ko00002), manually defined as consecutive reaction steps, operon or other regulatory units, phylogenetic units obtained by genome comparisons, etc. This new database also contains molecular complexes, facilitating better organization of data and knowledge, especially in KEGG BRITE. The hierarchy of molecular organization, such as the subunit organization of transporters or receptors, is represented by the M number that corresponds to a set of K numbers. Incidentally, a line segment in the new KEGG metabolism map that also corresponds to a set of K numbers is identified by the N number, representing a mechanistically defined network segment.

THE KEGG project is supported by the Institute for Bioinformatics Research and Development of the Japan Science and Technology Agency, the 21st Century COE program ‘Genome Science’, and a grant-in-aid for scientific research on the priority area ‘Comprehensive Genomics’ from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The computational resource was provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University. Funding to pay the Open Access publication charges for this article was provided by the grant-in-aid for scientific research.

Conflict of interest statement. None declared.