title: KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research.
abstract: A database (DB) describing the relationships between species and their metabolites would be useful for metabolomics research, because it targets systematic analysis of enormous numbers of organic compounds with known or unknown structures in metabolomics. We constructed an extensive species-metabolite DB for plants, the KNApSAcK Core DB, which contains 101,500 species-metabolite relationships encompassing 20,741 species and 50,048 metabolites. We also developed a search engine within the KNApSAcK Core DB for use in metabolomics research, making it possible to search for metabolites based on an accurate mass, molecular formula, metabolite name or mass spectra in several ionization modes. We also have developed databases for retrieving metabolites related to plants used for a range of purposes. In our multifaceted plant usage DB, medicinal/edible plants are related to the geographic zones (GZs) where the plants are used, their biological activities, and formulae of Japanese and Indonesian traditional medicines (Kampo and Jamu, respectively). These data are connected to the species-metabolites relationship DB within the KNApSAcK Core DB, keyed via the species names. All databases can be accessed via the website http://kanaya.naist.jp/KNApSAcK_Family/. KNApSAcK WorldMap DB comprises 41,548 GZ-plant pair entries, including 222 GZs and 15,240 medicinal/edible plants. The KAMPO DB consists of 336 formulae encompassing 278 medicinal plants; the JAMU DB consists of 5,310 formulae encompassing 550 medicinal plants. The Biological Activity DB consists of 2,418 biological activities and 33,706 pairwise relationships between medicinal plants and their biological activities. Current statistics of the binary relationships between individual databases were characterized by the degree distribution analysis, leading to a prediction of at least 1,060,000 metabolites within all plants. In the future, the study of metabolomics will need to take this huge number of metabolites into consideration.
authors: Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi, Nara, 630-0192 Japan.
authors: Farit Mochamad
authors: FM
authors: Afendi
authors: Taketo
authors: T
authors: Okada
authors: Mami
authors: M
authors: Yamazaki
authors: Aki
authors: A
authors: Hirai-Morita
authors: Yukiko
authors: Y
authors: Nakamura
authors: Kensuke
authors: K
authors: Nakamura
authors: Shun
authors: S
authors: Ikeda
authors: Hiroki
authors: H
authors: Takahashi
authors: Md
authors: M
authors: Altaf-Ul-Amin
authors: Latifah K
authors: LK
authors: Darusman
authors: Kazuki
authors: K
authors: Saito
authors: Shigehiko
authors: S
authors: Kanaya
mesh_terms: D019295:Computational Biology; D016208:Databases, Factual; D005843:Geography; D007214:Indonesia; D020407:Internet; D007564:Japan; D008518:Medicine, East Asian Traditional; D055432:Metabolomics / Q000379:methods*; D010946:Plants, Medicinal / Q000378:metabolism*; D057386:Search Engine