peer-reviewed literature. At SGD, curation has been, and will continue to be, a core responsibility that we conduct supplying a valued service to biomedical research communities. Providing comprehensive information for gene products, derived via literature curation, is of special value because it aids experimental design, facili- tates gene function discovery and enhances gene product annotations provided by other genomic resources. Structured controlled vocabularies (i.e. ontologies) include precise deﬁnitions representing conserved common biological concepts, thereby ensuring a consist- ent interpretation of results being represented. Controlled vocabularies facilitate the integration of diverse data types and promote computational analysis. SGD uses controlled vocabularies to describe experimental results selected from the complete literature on budding yeast. This approach has allowed us to maintain the gold standard of annotations describing the complete represen- tation of yeast chromosomal features, their functions and interactions. The quality of our literature annotations as a whole is addressed regularly with group curation exercises to ensure that information is entered into SGD accurately and consistently. In addition, we are actively researching and developing new methods for quality control of Gene Ontology (GO) functional annotations (6). These efforts in ensuring consistency and quality, coupled with a quick review of new papers after publication, ensure the gold standard annotations are in pace with scientiﬁc progress, as deﬁned by experimental results. Gene Ontology (GO) annotations Manual literature curation provides the highest quality representation of published experimental results. At SGD, annotation using GO terms captures biological in- formation regarding speciﬁc gene products in a search- able, computable form that can be readily compared across organisms. Manual GO annotations for speciﬁc gene products are chosen carefully within the context of all available biological knowledge, and represent the best possible summary of the most relevant information. The annotations for each gene product are reviewed as a whole, and updated as new information becomes avail- able, a time- and resource-intensive process. GO annotations may also be assigned via computation- al methods based on criteria such as protein domains, HMMs, or patterns of experimental data, allowing the rapid annotation of multiple genes using consistent par- ameters. Limitations of computational predictions do exist: such as incomplete genomic coverage; the use of too-general GO terms; and the inherent biases of different methods (6). In 2007, SGD began including computation- al predictions from the Gene Ontology Annotation (GOA) project at UniProtKB (7,8), whose InterPro tools rely in part on the presence of experimentally characterized protein domains with manually curated con- nections to GO terms. Researchers often transfer GO functional annotations between homologous proteins, thus the accuracy of SGD’s literature annotations is of utmost importance (9,10). In order to identify efﬁcient methods to keep GO Nucleic Acids Research, 2012, Vol. 40, Database issue D701 annotations comprehensive and current, we explored whether the large set of computational predictions could be leveraged to ﬁnd inaccuracies or omissions in our manual literature-based GO annotations. In an initial feasibility study (6), we examined a subset of genes that had a manual GO annotation that was less speciﬁc than the computational prediction. Speciﬁcally we selected genes that lacked published functional information (manual ‘unknown’ annotations) but had InterPro com- putational predictions. Review of the publications for these genes found that a signiﬁcant proportion of the manual annotations could be updated to a deﬁned, experi- mentally supported function. In contrast, review of the literature for a comparable control set of genes (those with manual ‘unknown’ annotations, but without corres- ponding InterPro computational predictions) did not identify as many possible improvements. Thus, we have demonstrated that the presence of InterPro computational predictions can effectively identify annotations that need to be updated. We expanded this strategy so that each literature-based annotation for a gene was compared to computation- al predictions generated by different methods in each of the three GO aspects (Molecular Function, Biological Process, and Cellular Component) (Park,J., Costanzo,M.C., Balakrishnan,R., Cherry,J.M. and Hong, E.L. CvManGO, a method for leveraging compu- tational predictions to improve literature-based GO annotations. submitted to Database for Biocuration 2012 virtual issue.). The comparison between literature-based annotations and computational predictions identiﬁed two types of literature-based annotations that needed review: those that were less speciﬁc than the computation- al predictions and those that do not have the same lineage to the root term as the computational predictions. Our goal is to develop an automated method to identify and prioritize genes whose annotations may need to be re-examined based on the number of literature-based an- notations that need review as well as the distance between the GO terms used by the literature-based and computa- tional annotations, the number of computational predic- tions that suggest a gene should be reviewed, and the date the gene was last reviewed. This strategy will be applicable to GO annotations for any organism for which both literature-based and computational annotation is performed. Phenotypes Complete descriptions of mutant phenotypes are also rep- resented using a controlled vocabulary known as the Ascomycete Phenotype Ontology (APO; available from the OBO Foundry, http://www.obofoundry.org). Each phenotype annotation in SGD is represented as an observ- able, which describes the entity or process, paired with a qualiﬁer that describes the change in that entity or process. For example, if a mutant strain has increased sensitivity to a chemical compound, relative to wild-type, in which the observable is captured as ‘resistance to chemicals’ and the qualiﬁer is ‘decreased’. Chemical compounds, which are often used to elicit phenotypes, are speciﬁed using a