more arbitrary and also less meaningful. Xenobiotic compounds can be metabolized by plants and such pathways would be important for herbicide design and in phytoremediation processes when plants are used for soil clean-up. Common cheminformatics tasks during database curation like multiple stereoisomers, different salt forms and tautomer normalization [46] and compound overlap calculations were not investigated in this report due to the multiple problems in the first place. A paradigm change is inevitable and possible A paradigm change for publishing chemicals structures, spectra and molecular properties is inevitable. Implementing new paradigms or new standards in databases and journals often seems too difficult to achieve. Nevertheless, there are multiple success stories in biological sciences, from the protein databank (PDB) to GenBank and the Gene Expression Omnibus to crystal structures. Although use of such standards seem to be of little benefit for the individual researcher at first glance, once public databases can be queried across studies, benefits will be obvious even for small datasets, e.g. to prove the novelty of findings for a specific (small-scale) experiment. Conclusions In order to obtain an open access and free full coverage of all rice metabolites, approximately 50,000 journal articles would be needed to be reanalyzed using fulltext chemical annotation services. Once such a database would be established, further updates on novel identified metabolites should use a radically different approach. While we have taken oryza sativa as an example with the RiceCyc database already in place, this call for a paradigm change extends to all 300,000 species currently comprised in the NCBI taxonomy database. While one option to improve the situation would be to compile databases from literature data (i.e. bitmap PDF documents) using error-prone optical character recognition (OCR), free access databases such as ChemSpider and PubChem would be better suited to store compound annotation, spectral and taxonomy data for chemical reports. The loss of experimental and semantic (ontological) information using traditional analogue publishing methods is tremendous and underestimated. This is especially the case for high resolution mass spectra and NMR spectra [75]. It has been shown that an enforcement for the publication of metadata is possible as in the crystallographic community and the Cambridge Structural Database [76]. Successful fully automated aggregation of crystal structures from open access supplement data has been shown by the CrystalEye project [48]. Data driven research approaches which require access to large amounts of experimental data are not possible with current database collections that are not linked with global and open identifiers and have limited access strategies. Chemical database providers should allow batch download of compounds or batch matching of large compound data sets, provided that there are no financial or legal restrictions. Chemical and biological database providers should also enhance their database records with PubChem substance or compound IDs and with InChI codes and the hashed InChIKeys to promote a universal connection between modern life sciences and chemistry databases. The problem of differentiation between xenobiotic molecules and metabolites that are produced by the biochemical machinery can only be solved by additional database annotations and use of ontologies. Especially the differentiation of the endo- and exo-metabolome for superorganism like humans, where many compounds are believed to belong to the human enzyme machinery but are actually excreted by the gut microbiome are Data Exchange in Chemistry only solvable by special experimental setups and extended annotations. Computational approaches based on orthology mapping seem to be the most intuitive and therefore successful methods to increase coverage on small molecules in databases for all taxonomic species. Such computationally predicted metabolites could be included as putative compounds in ChemSpider/ PubChem repositories once these chemical databases are enriched with taxonomy, literature and spectral data. Further improve- ments could then annotate absolute metabolite concentrations reported for specific organs and cellular compartments to allow cross-database and cross-species queries. Only concerted efforts between scientific societies, funding agencies, database providers and journals could break up traditional ways of metabolic reporting. Nevertheless, even single projects like RSC Project Prospect, CrystalEye, SetupX or ChemSpider can lead into the right direction in a pull-strategy, by leading by example. Compound annotations from text (Name; PubChem CID; InChIKey) Also see [Dataset S1] 2-acetyl-1-pyrroline; CID 522834; DQBQWWSFRPLIAX- UHFFFAOYAG Vitamin-A; CID 445354; FPIPGXGPPPQFEQ-OVSJKPM- PBW Beta-carotene; CID 5280489; OENHQHLEOONYIE-JLTX- GRSLBT Bisbynin; CID NA; ICHJNTDKHBXTFN-CMZGOGIXBZ Trans-luteine; CID 5368396; KBPHJBAIARWVSC-DKLM- TRRABK Cholesterol; CID 5997; HVYWMOMLDIMFJA-DPAQB- DIFBB Malathion; CID 4004; JXSJBGJIGXNWCI-UHFFFAOYAK Chlorpyrifos; CID 2730; SBPBAQFWLVIOKP-UHFF- FAOYAG Ribosylnicotinamide; CID 439924; JLEBZPBDRKPWTD- ARWKKGFBBE Omeprazol; CID 4594; SUBDBMMJDZJVOS-UHFFFAOY- AZ Rhodopinal; CID 20055178; GOJQFVQXKNNAAY-XQHL- YSSHBM Tegafur; CID 5386; WFWLQNSHRPWKFK-UHFFFAOYAE Arginine; CID 232; ODKSFYDXXFIFQN-UHFFFAOYAT Supporting Information Dataset S1 Supporting ZIP file for publication. Contains chemical compound structures and figures and other information. Found at: doi:10.1371/journal.pone.0005440.s001 (0.55 MB ZIP) Acknowledgments We thank database providers which allowed free academic access. We thank ChemAxon for free academic licenses of Marvin and Instant-JChem for structure handling. We thank Peter Murray Rust (Cambridge University) for open discussions on his blog. We thank Dinesh Kumar Barupal (FiehnLab) for discussions. We thank Stephen Boyer (IBM Almaden Research Center) for access to the IBM Chemical Patent Search Beta. Author Contributions Conceived and designed the experiments: TK OF. Performed the experiments: TK. Analyzed the data: TK MS OF. Contributed reagents/materials/analysis tools: TK. Wrote the paper: TK OF. Was frustrated: TK. Created SetupX: MS. Was optimistic: OF. PLoS ONE | www.plosone.org 8 May 2009 | Volume 4 | Issue 5 | e5440