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KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters.
Nakaya, Akihiro; Katayama, Toshiaki; Itoh, Masumi; Hiranuka, Kazushi; Kawashima, Shuichi; Moriya, Yuki; Okuda, Shujiro; Tanaka, Michihiro; Tokimatsu, Toshiaki; Yamanishi, Yoshihiro; Yoshizawa, Akiyasu C; Kanehisa, Minoru; Goto, Susumu.
Afiliação
  • Nakaya A; Center for Transdisciplinary Research, Niigata University, 1-757 Asahimachi-dori, Chuo-ku, Niigata 951-8585, Japan.
Nucleic Acids Res ; 41(Database issue): D353-7, 2013 Jan.
Article em En | MEDLINE | ID: mdl-23193276
ABSTRACT
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 (http//www.genome.jp/tools/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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genes Arqueais / Bases de Dados Genéticas / Genes / Genes Bacterianos Idioma: En Ano de publicação: 2013 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genes Arqueais / Bases de Dados Genéticas / Genes / Genes Bacterianos Idioma: En Ano de publicação: 2013 Tipo de documento: Article