DBTGR (10) or added manually by individual ORegAnno users from literature searches and review articles. The expert entry queue currently contains 4438 gene regulation papers of which 3478 are open or pending and 960 are closed. Development of text-mining strategies and the ‘text-mining queue’ The publication queue represents an unprecedented resource for researchers interested in developing text- mining approaches to identify papers involved in gene regulation and/or extract regulatory data from these papers. We used both the ‘success’ and the ‘failure’ papers from the ‘expert-entry’ queue to validate and compare diﬀerent vector space models (27) for cis-regulatory document retrieval (Aerts and coworkers, manuscript in preparation). The model with the best performance in terms of sensitivity and speciﬁcity was chosen to rank the entire corpus of PubMed abstracts. By manually curating uniformly distributed samples from the top 100 000 scoring abstracts, a cut-oﬀ was set at 58 000 so that the positive predictive value (PPV) of top-scoring abstracts reached 50%, a success rate similar to that achieved during the RegCreative Jamboree (54%), and judged satisfactory by the Jamboree participants. These 58 000 papers, containing an estimated 29 000 papers that will result in regulatory annotations, have been added to the ORegAnno queue (54 351 new additions after removing duplications). We estimate that this large cis-regulatory text corpus will require around 15–30 person-years to be fully curated. Therefore, the Open Regulatory Annotation Consortium is actively pursuing research in text-mining techniques to identify the actual cis-regulatory sequences, the species and the target gene automatically from the full text papers. In a pilot study, sequences were extracted from full-text articles for papers in the ORegAnno expert-based queue and the top 4501 papers from the text-mining-based queue. When comparing the automatically extracted data with the collection of manual ORegAnno annotations, this study achieved a reasonably high PPV (62%) at the sequence level, showing that automatic draft annotation of cis-regulatory elements is indeed feasible by text-mining (Aerts and coworkers, manuscript in preparation). Such draft annotations should help accelerate the manual curation and can also serve directly as benchmark data to validate cis-element prediction algorithms. Ontologies in ORegAnno The ORegAnno evidence ontology is a simple ontology of evidence classes, types and subtypes for describing experiments that demonstrate the identity and/or function of regulatory sequences and their factors. These lines of evidence capture critical details from primary experi- ments and allow end users to ﬁlter the ORegAnno sequence set, based on their own criteria for experi- mental credibility. The ontology has been considerably extended since last published, and currently consists of six classes (e.g. Transcription regulator site), 14 evidence types (e.g. Reporter gene assay) and 72 evidence subtypes Nucleic Acids Research, 2008, Vol. 36, Database issue D111 (e.g. Transient transfection luciferase assay). This ontol- ogy has been adopted by the PAZAR resource (28) and is being developed in collaboration with that group using Prote´ ge´ (http://protege.stanford.edu/). The complete evidence ontology can be obtained in XML format (http://www.oreganno.org/oregano/evidence.xml) or as a Prote´ g e´ project ﬁle (http://www.pazar.info/ontologies/ newevidence.pprj). Within each line of evidence, a user can also specify the cell type in which experiments were conducted using the eVOC cell type ontology (29). We are working to incorporate additional Ontologies such as the BRENDA Tissue Ontology, and improvements to the Sequence Ontology are currently being developed for the cis-regulatory domain. Other improvements The ORegAnno website has been updated to use Ajax technology, improving the ease of annotation. Ajax improves a web page’s usability by exchanging small amounts of data with the server behind the scenes, so that the entire web page does not have to be reloaded each time the user requests a change (http://www.xul.fr/en-xml- ajax.html). A detailed case study has been added to the help pages to guide users through the entire process of annotating a paper. Annotation pages have been improved so that individual ‘help bubbles’ are available next to each ﬁeld. Additional web services methods have been created to allow programmatic access to the publication queue and genome mappings. DATA ACCESS The website (http://www.oreganno.org) provides access to an advanced search page for the entire record set, the publication queue, simple tools for scanning or extracting sequences, database dumps and extensive help documen- tation. Each record page represents a complete summary of the data for a veriﬁed regulatory sequence along with links to external sources such as UCSC, Ensembl and PubMed. All data are freely available in a number of formats without any user registration. Users are required to register and login only if they wish to add records, comments or scores. Nightly data dumps of the database are posted in XML format on the website. Human (hg18) and ﬂy (dm3) records are available through the UCSC genome browser (http://genome.ucsc.edu/) as a standard track under the ‘Expression and Regulation’ tab. Mouse (mm8), worm (ce4) and rat (rn4) are available through the UCSC ‘genome-test’ browser (http://genome-test. cse.ucsc.edu/). The ORegAnno dataset is also in the process of being incorporated into the PAZAR database (760 records to date). Programmatic interaction with ORegAnno is available through web services using the Perl SOAP modules (see ‘Dump’ page for examples). Requests for the entire database (e.g. a MySQL dump) or other formats can be addressed to the authors. ORegAnno records are typically mapped to only the most current genome build for each species as provided by UCSC (e.g. hg18 for human). However, mapping can easily be performed for any other genome build upon request.