RiceCyc RiceCyc is a new compound and pathway database obtained from known literature pathways and by computational approaches obtained from the fully sequenced rice genomes. Datasets were obtained using the Stanford Research Institute SRI PathWay tools [32] and by later curating the database with Arabidopsis-rice ortholog annotations. The database statistics shows 1,500 annotated compounds with 1,463 compounds encoded in SMILES structure format [33]. Although SMILES formats are not as universal as the newer International Chemical Identifier codes (InChI) [34], but at least SMILES structures are machine readable and thus can in principle be compared to hit lists from other databases. Unfortunately RiceCyc has no direct link-out to PubChem and only 580 of those structures have assigned CAS numbers, so direct overlap analysis of metabolites retrieved from RiceCyc to compounds from other databases are very difficult at this point. Nevertheless, RiceCyc is certainly a very valuable tool on retrieving information which metabolites can be expected in rice samples, although not yet supported through experimental or literature evidence. KEGG KEGG is a well known metabolite and pathway database. The current version covers 91,879 pathways generated from 240 reference pathways and around 850 taxonomic species with 10,303 reactant pairs and 15,217 metabolites. Recently, coverage of lipid metabolism was improved with inclusion and links to LipidMaps databases [35] and missing secondary plant metabo- lites are now obtained from the KNApSAcK DB. Furthermore KEGG is indexed in PubChem and provides multiple outlinks to other databases. With KEGG Brite [36], a collection of hierarchical classifications representing the knowledge on various aspects of biological systems, the database possesses a powerful query frontend. Altogether 3,661 compounds were obtained for rice based on pathway mappings. As with all in-silico approaches there is no guarantee that computed substances truly occur in the species. For example, KEGG predicts the bacterial metabolite Rhodopinal or the chemotherapeutic anti-cancer drug Tegafur to be found in rice, which appears unlikely to refer to genuine plant metabolites without experimental proof. Nevertheless, plants are able to metabolize xenobiotic drugs and pesticides; therefore, the inclusion of such compounds in pathway databases is important to obtain a complete picture of metabolism. An important curation of KEGG and other metabolic repositories would be to distinguish between authentic organism-specific metabolites and xenobiotic compounds. KNApSAcK DB The KNApSAcK species-metabolite database search resulted in 48 compounds, most of them referring to secondary metabolism. The database contains no outlinks to other databases but the KEGG database links some of its results to the KNApSAcK DB. The database contains a simple text search and web search frontend. Molecules are coded as pictures and in some cases contain also CAS numbers. Around 600 taxonomic species are currently covered with 30 species having more than one hundred metabolites assigned, whereas the median of the number of assigned metabolites is four. Reactome DB The Reactome pathway database covered 2,836 proteins, 865 reactions and 447 pathways and 396 metabolites for rice. Reactome allows the download in SBML and BioPax format as Data Exchange in Chemistry well as ChEBI and KEGG identifiers for single reaction maps but also for specific taxonomic species. Patent databases A patent search in the CAS database revealed around 3,000 patents dealing with rice. The SureChem DB revealed 2,531 US patent office granted patents, 609 European granted patents and 2,385 world patents. The IBM Chemical patent search was used to export chemical structures from 1,384 covered world patents issued by the World Intellectual Property Organization. A total of 69,215 chemical compounds were mentioned and 9,780 unique chemicals were obtained after refinement with Instant-JChem [37] using chemical structure overlap analysis. Patents as sources for extracting molecular biological knowledge are unreliable because of the different scope of patent databases [38]. Usually they also contain molecules from secondary literature with no proof if such molecules were indeed found in rice. Without manual curation it is not possible to separate between genuine rice compounds and herbicides and pesticides and other chemicals mentioned in patents. Additional metabolite databases MetaCyc and BioCyc [39] are umbrella databases covering 1,500 organisms and 1,100 metabolic pathways and the collection of Pathway/Genome Databases. These databases were not used for searches, because the oryza sativa metabolite and pathway information are explicitly stored in the RiceCyc database. The integrated rice science database Oryzabase [40] does not contain small molecule information but mostly gene annotations. The MetaCrop [41] database contains information about rice obtained from the literature and multiple other pathway databases. The whole database contains around 300 metabolites but most of the other information is enzyme related and it is not possible to refine metabolites which only occur in rice. A very important feature of the MetaCrop database is that for some substances and enzymes it contains data on developmental state of plant, organs, tissue and compartment together with a related link to the literature on PubMed. The National Library of Medicine Medical Subject Headings (MeSH) contains only a small subset of annotated organisms. No information is included if a certain metabolite is genuine and specifically found in single species. Other metabolite databases like the Madison Metabolomics Consortium Database [42], the Human Metabolome Database [43] or the Metlin database [44] and ChEBI [45] were not included because they contained no oryza sativa taxonomy information or resulted in zero hits. Discussion It was not possible to automatically compute a single combined large knowledge repository of all small molecules of rice (oryza sativa) which would comprise both experimentally verified compounds and putative compounds annotated from genomic and pathway databases. Three major obstacles were hindering this approach: (1) A number of databases did not allow or did not enable batch downloads and storage of compound lists and structures. (2) A number of databases did not cross-reference compound identifiers or did not export structures in machine-readable formats, so that analysis of overlaps of hit lists was not possible. (3) Many databases did not distinguish between metabolites that are produced by the biochemical machinery in rice from PLoS ONE | www.plosone.org 5 May 2009 | Volume 4 | Issue 5 | e5440