KEGG OC: A large-scale automatic construction of taxonomy-based ortholog clusters

Akihiro Nakaya, Toshiaki Katayama, Masumi Itoh, Kazushi Hiranuka, Shuichi Kawashima, Yuki Moriya, Shujiro Okuda, Michihiro Tanaka, Toshiaki Tokimatsu, Yoshihiro Yamanishi, Akiyasu C. Yoshizawa, Minoru Kanehisa, Susumu Goto

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


The identification of orthologous genes in an increasing number of fully sequenced genomes is a challenging issue in recent genome science. Here we present KEGG OC (, a novel database of ortholog clusters (OCs). The current version of KEGG OC contains 1 176 030 OCs, obtained by clustering 8 357 175 genes in 2112 complete genomes (153 eukaryotes, 1830 bacteria and 129 archaea). The OCs were constructed by applying the quasi-clique-based clustering method to all possible protein coding genes in all complete genomes, based on their amino acid sequence similarities. It is computationally efficient to calculate OCs, which enables to regularly update the contents. KEGG OC has the following two features: (i) It consists of all complete genomes of a wide variety of organisms from three domains of life, and the number of organisms is the largest among the existing databases; and (ii) It is compatible with the KEGG database by sharing the same sets of genes and identifiers, which leads to seamless integration of OCs with useful components in KEGG such as biological pathways, pathway modules, functional hierarchy, diseases and drugs. The KEGG OC resources are accessible via OC Viewer that provides an interactive visualization of OCs at different taxonomic levels.

Original languageEnglish
Pages (from-to)D353-D357
JournalNucleic acids research
Issue numberD1
Publication statusPublished - Jan 1 2013

All Science Journal Classification (ASJC) codes

  • Genetics


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