Databases (DBs) are required by various omics fields because the volume of molecular biology data is increasing rapidly. In this study, we provide instructions for users and describe the current status of our metabolite activity DB. To facilitate a comprehensive understanding of the interactions between the metabolites of organisms and the chemical-level contribution of metabolites to human health, we constructed a metabolite activity DB known as the KNApSAcK Metabolite Activity DB. It comprises 9,584 triplet relationships (metabolite-biological activity-target species), including 2,356 metabolites, 140 activity categories, 2,963 specific descriptions of biological activities and 778 target species. Approximately 46% of the activities described in the DB are related to chemical ecology, most of which are attributed to antimicrobial agents and plant growth regulators. The majority of the metabolites with antimicrobial activities are flavonoids and phenylpropanoids. The metabolites with plant growth regulatory effects include plant hormones. Over half of the DB contents are related to human health care and medicine. The five largest groups are toxins, anticancer agents, nervous system agents, cardiovascular agents and non-therapeutic agents, such as flavors and fragrances. The KNApSAcK Metabolite Activity DB is integrated within the KNApSAcK Family DBs to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics. The KNApSAcK Metabolite Activity DB could also be utilized for developing novel drugs and materials, as well as for identifying viable drug resources and other useful compounds.

A rapid and easy method for extracting features from spectra obtained from Fourier transform near-infrared (FT-NIR) reflectance spectroscopy was examined by using the 1 st and 2 nd derivatives and Spearman's rank correlation. This method can select features from the overall wavelength. Therefore, this method can be considered suitable for the quality estimation of foods. Practically, a set of ranked green tea samples from a Japanese commercial tea contest were analyzed by FT-NIR in order to create a reliable quality-prediction model. The 2 nd derivative was determined for reducing noise and amplifying the fundamental features. Feature selection from the amplified data was performed using relations between the tea ranks and the derivative coefficients. Finally, a reliable quality-prediction model of green tea was formulated by using single linear and PLS regressions. Furthermore, we discuss possibility of the derivative coefficients as feature representation in FT-NIR.

In a metabolomics research, assignment of measured spectra to a specific metabolite is one of the most fundamental processes. The assignment is usually made by taking a match with known compounds, and therefore it is necessary to scan the spectra against whole previously studied compounds. This means the survey of whole natural products reported in the literature, which is an extremely tedious and daunting process. In order to make this process feasible, we have developed a metabolite database called KNApSAcK, which currently contains 76,357 species–metabolite relations involving 37,693 metabolites. In the present study, we review the current status of KNApSAcK database and its application to metabolomics and introduce the multifaceted retrieval system KNApSAcK family, which consists of seven parts for the purpose of retrieving metabolites from several different aspects. “Pocket” includes search system for species related to human living such as edible plants in Japan (“Lunch Box”), herb teas (“Tea Pot”, in progress), traditional Japanese medicine (“KAMPO”), poisonous plant (“Poison”, in progress), and bio‐fuel resources (“Fuel”, in progress). “KNApSAcK from around the world” includes medicinal and edible plants utilized in each country. Seven thousand three hundred and fifty‐six pairwise relations between medicinal/edible plants and 119 nations worldwide have been accumulated from scientific literatures.