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In collaboration with the journal GENETICS, we've developed and launched a pipeline by which interactive full-text HTML/PDF journal articles are published with named entities linked to corresponding resource pages in "WormBase":http://www.wormbase.org/ (WB). Our interactive articles allow a reader to click on over ten different data type objects (gene, protein, transgene, etc.) and be directed to the relevant webpage. This seamless connection from the article to summaries of data types promotes a deeper level of understanding for the naïve reader, and incisive evaluation for the sophisticated reader. Further, this collaboration allows us to identify and collect information before the publication of the article. The pipeline uses automated recognition scripts to identify entities that already exist in the database and a self-reporting form we created at WB that is sent to the author by GENETICS for submitting entities that do not already exist in our database. We include a manual quality control step to make sure ambiguous links are corrected, and that all new entities have been reported and linked properly. The automated entity recognition scripts allows us to potentially link any object found in a database as well as to expand this pipeline to other databases. We have already adapted this pipeline for linking Saccharomyces cerevisiae GENETICS articles to the "Saccharomyces Genome Database":http://www.yeastgenome.org/ (SGD) and are currently expanding this pipeline for linking genes in Drosophila articles to "FlyBase":http://flybase.org/. By integrating journals and databases, we are integrating the major modes of communication in the biological sciences, which will undoubtedly increase the pace of discovery.

The Gene Ontology (GO) is a well-established, structured vocabulary that has been successfully used for 10 years in the annotation of proteins. GO terms, created in consultation with the biology community, are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently GO consists of more than 26,500 terms distributed over three ontologies that describe the molecular function, biological process and subcellular location of a protein in a generic cell.The Gene Ontology Annotation (GOA) database ("http://www.ebi.ac.uk/GOA":http://www.ebi.ac.uk/GOA) aims to provide high-quality manual and electronic GO annotations to proteins within the UniProt Knowledgebase (UniProtKB). By annotating all ‘known’ proteins with GO terms and transferring this knowledge to highly similar ‘unknown’ proteins, GOA offers a valuable contribution to the understanding of all proteomes.As well as generating manual annotation, made by extracting experimental evidence from full text peer-reviewed publications, GOA produces electronic annotation by making large-scale assignments of GO terms to proteins using computational methods. To date we have six electronic annotation methods including; InterPro2GO, Swiss-Prot Keyword2GO and the projection of annotations between orthologous species using Ensembl Compara.GOA provides annotated entries for over 180,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. In addition, by integrating GO annotations from model organism groups (e.g. FlyBase, GeneDB, MGI, RGD, SGD and TAIR), GOA ensures the dataset remains a key reference. GOA prioritises the annotation of the human proteome and provides this annotation to the GO Consortium’s Reference Genome project.GOA produces monthly releases of annotations to the human, mouse, rat, zebrafish, cow, chicken and Arabidopsis proteomes as well as a file for the multiple species within UniProtKB. The GOA dataset can be queried through a user-friendly web interface via our QuickGO browser("http://www.ebi.ac.uk/QuickGO":http://www.ebi.ac.uk/QuickGO) or downloaded in a parsable format via the EBI("ftp://ftp.ebi.ac.uk/pub/databases/GO/goa":ftp://ftp.ebi.ac.uk/pub/databases/GO/goa) and GO FTP sites. The GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results.

Complete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).