1

Genome Research

Biological Systems Database and Genome Information ScienceProject Leader:
Minoru KANEHISAProfessor, Institute of Chemical Research, Kyoto University

1. Objective:
The increasing amount of genomic information is thebasis for understanding principles of how higher-order
biological systems, such as the cell, the organism, andthe biosphere, are formed, as well as for medical,industrial, and other practical applications. However,current informatics technologies cannot readily uncoverhigher-level complexity of such biological systems,
although they are quite effective to find and characterizebuilding blocks of genes and proteins. Here we develop
knowledge-based methods for uncovering higher-ordersystemic behaviors of the cell and the organism fromgenomic information. The reference knowledge is storedin KEGG, Kyoto Encyclopedia of Genes and Genomes,
and associated bioinformatics technologies are developed
both for basic research and practical applications.

2.2 The KEGG database
The KEGG database project was initiated in ourlaboratory in 1995, the last year of the first five-year
phase of the Japanese Human Genome Program, andthen continued in the second five-year phase, withsupports from the Ministry of Education as grants-in-aid
for scientific research on priority areas. With a new
funding under the Millennium Project the current
research-for-the-future program was launched in 2000,and KEGG was significantly expanded. As shown below,
KEGG is a biological systems database, integrating
genomic information (GENES database) and chemicalinformation (LIGAND database) in terms of network
information (PATHWAY database) [2, 3].

2. Summary
2.1 New concept of the database
An ultimate goal of bioinformatics is a complete
computer representation of the cell and the organism,
which will enable computational prediction ofhigher-level complexity, such as molecular interaction
networks involving various cellular processes andphenotypes of entire organisms, from genomic
information [1]. From this perspective, a new concept ofthe biological database has been developed. As shown
below, the database is a computer representation of thebiological system, and it has been successfullyimplemented as KEGG.

Traditional bioinformatics technologies have focused on
finding useful genes and molecules, such as disease
genes and drug targets, by screening of large-scale data.
In contrast, our approach is first to understand wiring
diagrams (molecular interaction networks) of building
blocks and then to find functions and utilities of
biological systems as a whole. KEGG is a reference
knowledge base containing current knowledge on suchwiring diagrams, and it is used worldwide as a unique
resource for reconstructing metabolism and other cellular
processes from genomic information and forunderstanding systemic functional meanings and utilities.
During the five years of this project we have developed
various new features in KEGG, which are summarized
below.

(1) For network information, we expanded thePATHWAY database from a collection of metabolic
pathways to a more comprehensive collection containing
signaling and various other regulatory pathways as wellas human disease pathways. In addition, an XML versionof pathway maps was made available to facilitatecomputational analysis of KEGG pathways. KEGG has
become the international standard of pathway
information.
(2) For genomic information in the GENES database, we
introduced the KO (KEGG Orthology) system, anddeveloped an automatic method of KO assignment andKEGG pathway mapping, enabling rapid analysis of
genomic sequences and EST sequences. By improving
the KO system and the automatic assignment program,
we hope to make KEGG as the international standard forgenome annotations.
(3) For chemical information in the LIGAND database,
we introduced GLYCAN [4] in addition toCOMPOUND and REACTION. We also developed
graph-based algorithms for chemical compound structurecomparison and glycan structure comparison, as well as
the RC (Reaction Classification) system, which can beused for automatic assignment of EC numbers [5, 6].
These advanced activities resulted in the international
collaborations with the NCBI, the EBI, and theConsortium for Functional Glycomics.
(4) The entire KEGG resource is made available at theenhanced KEGG website for general use, as well as
through the newly developed KEGG API (applicationprogramming interface) for custom use to meet specificneeds.

2.2

Integration of Genomics and Chemistry

The representation of the biological system (ontology) in
KEGG is based on the concept of the graph, especiallythe nested graph and the line graph. The nested graph is agraph whose nodes can themselves be graphs. It is used
for representing KEGG network hierarchy and forpathway reconstruction and functional inference.
The line graph is a graph derived by interchanging nodes
and edges. The metabolic pathway can be viewed either
as a network of genes (enzymes) or as a network ofcompounds, meaning that one can be generated from theother by the line graph transformation. With this concept,
we have undertaken new research on integrated analysis
of genomic and chemical information [4-6]. The gene
repertoire in the genome would tell us about all
substances that should be produced by an organism, andconversely chemical structures of natural substances
would tell us about the genes that should be present inthe genome. The integration of genomics and chemistry
is becoming more important now that chemical genomics
initiatives generate large amounts of experimental data.
3. Concluding Remarks
The number of accesses to the KEGG/GenomeNet
website has increased four fold during the five-year
period of this project. It is by far the best-used databasesite in Japan, despite the fact that it has been developed
and maintained by a single laboratory. In fact, it is one of
the major sites in the world as is apparent by the Google
links search showing the number of links made fromother sites. We acknowledge the Millennium funding forthis achievement.

Primary Publications
1. Kanehisa, M. and Bork, P. (2003) Bioinformatics inthe post-sequence era. Nat. Genet. 33, 305-310.
2. Kanehisa, M., Goto, S., Kawashima, S., and Nakaya,A. (2002) The KEGG databases at GenomeNet.
Nucleic Acids Res. 30, 42-46.
3. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y.,
and Hattori, M. (2004) The KEGG resource fordeciphering the genome. Nucleic Acids Res. 32,
D277-D280.
4. Hashimoto, K., Goto, S., Kawano, S., Aoki-Kinoshita,K.F., Ueda, N., Hamajima, M., Kawasaki, T., andKanehisa, M. (2005) KEGG as a glycome informatics
resource. Glycobiology, in press.
5. Hattori, M., Okuno, Y., Goto, S., and Kanehisa, M.
(2003) Development of a chemical structurecomparison method for integrated analysis of chemicaland genomic information in the metabolic pathways. J.
Am. Chem. Soc. 125, 11853-11865.
6. Kotera, M., Okuno, Y., Hattori, M., Goto, S., andKanehisa, M. (2004) Computational assignment of theEC numbers for genomic-scale analysis of enzymatic
reactions. J. Am. Chem. Soc. 126, 16487-16498.