Currently, in mass spectrometry-based metabolomics, limited reference mass spectra are available for flavonoid identification. In the present study, a database of probable mass fragments for 6,867 known flavonoids (FsDatabase) was manually constructed based on new structure- and fragmentation-related rules using new heuristics to overcome flavonoid complexity. We developed the FlavonoidSearch system for flavonoid annotation, which consists of the FsDatabase and a computational tool (FsTool) to automatically search the FsDatabase using the mass spectra of metabolite peaks as queries. This system showed the highest identification accuracy for the flavonoid aglycone when compared to existing tools and revealed accurate discrimination between the flavonoid aglycone and other compounds. Sixteen new flavonoids were found from parsley, and the diversity of the flavonoid aglycone among different fruits and vegetables was investigated.

SummaryFor metabolite annotation in metabolomics, variations in the registered states of compounds (charged molecules and multiple components, such as salts) and their redundancy among compound databases could be the cause of misannotations and hamper immediate recognition of the uniqueness of metabolites while searching by mass values measured using mass spectrometry. We developed a search system named UC2 (Unique Connectivity of Uncharged Compounds), where compounds are tentatively neutralized into uncharged states and stored on the basis of their unique connectivity of atoms after removing their stereochemical information using the first block in the hash of the IUPAC International Chemical Identifier, by which false-positive hits are remarkably reduced, both charged and uncharged compounds are properly searched in a single query and records having a unique connectivity are compiled in a single search result.Availability and implementationThe UC2 search tool is available free of charge as a REST web service (http://webs2.kazusa.or.jp/mfsearcher) and a Java-based GUI tool.Supplementary information Supplementary data are available at Bioinformatics online.

To understand how metabolism changes during fruit ripening in Jatropha curcas L., we performed a nontargeted analysis of metabolites in fruit (the pericarp and developing young seeds) from each maturation stage by means of liquid chromatography-Orbitrap-mass spectrometry, which provides m/z data with approximately 2 ppm precision.e chromatographic data were processed using bioinformatics tools. e total number of metabolites detected decreased substantially with fruit maturation. Self-organizing map (SOM) analysis and metabolite annotation of the ions detected suggested that dynamic metabolic changes occur during fruit maturation. All chromatographic data were deposited in databases accessible by the public.

Papaya (Carica papaya L.) is widely cultivated in tropical and subtropical countries. While ripe fruit is a popular food item globally, the unripe fruit are only consumed in some Asian countries. To promote the utilization of unripe papaya based on the compositional changes of biological active metabolites, we performed liquid chromatography-Orbitrap-mass spectrometry-based analysis to reveal the comprehensive metabolite profile of the peel and pulp of unripe and ripe papaya fruit. The number of peaks annotated as phenolics and aminocarboxylic acids increased in the pulp and peel of ripe fruit, respectively. Putative carpaine derivatives, known alkaloids with cardiovascular effects, decreased, while carpamic acid derivatives increased in the peel of ripe fruit. Furthermore, the functionality of unripe fruit, the benzyl glucosinolate content, total polyphenol content, and proteolytic activity were detectable after heating and powder processing treatments, suggested a potential utilization in powdered form as functional material.

Abstracte publication of the whole genome sequence of Jatropha curcas L. has contributed to the study of gene functions of this plant, especially in data-driven investigations such as transcriptome and proteome analyses. Metabolomics analyses of Jatropha have also been reported in recent years. However, the analytical tools for omics data from Jatropha are limited. We prepared a set of pathway maps where the predicted genes of Jatropha were assigned based on KEGG pathway maps, and developed an omics viewer named KaPPA-View4-Jatropha where the pathway maps were implemented. Out of 40,929 predicted genes, 8085 genes were mapped on the KEGG Metabolism maps, other KEGG maps, or gene category maps that were generated from gene classi cation data of KEGG BRITE. Two transcriptome datasets, four metabolome datasets and one gene co-expression dataset were registered in the viewer. To facilitate data sharing of unpublished omics data among research collaborators, we developed a local database system, KaPPA-Loader. ese data mining environments and the supporting database system will help Jatropha researchers to discover key genes such as those involved in oil production, biosynthesis of toxic compounds, and stress resistance. KaPPA-View4-Jatropha and KaPPA-Loader are available from the KaPPA-View portal site (http://kpv.kazusa.or.jp/).