Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.

BACKGROUND: The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. RESULTS: Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. CONCLUSIONS: Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

A method for increasing expressivity of Gene Ontology annotations using a compositional approach.

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. RESULTS: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions. CONCLUSIONS: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

Representing kidney development using the gene ontology.

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

Use of Gene Ontology Annotation to understand the peroxisome proteome in humans.

The Gene Ontology (GO) is the de facto standard for the functional description of gene products, providing a consistent, information-rich terminology applicable across species and information repositories. The UniProt Consortium uses both manual and automatic GO annotation approaches to curate UniProt Knowledgebase (UniProtKB) entries. The selection of a protein set prioritized for manual annotation has implications for the characteristics of the information provided to users working in a specific field or interested in particular pathways or processes. In this article, we describe an organelle-focused, manual curation initiative targeting proteins from the human peroxisome. We discuss the steps taken to define the peroxisome proteome and the challenges encountered in defining the boundaries of this protein set. We illustrate with the use of examples how GO annotations now capture cell and tissue type information and the advantages that such an annotation approach provides to users. Database URL: http://www.ebi.ac.uk/GOA/ and http://www.uniprot.org.

The UniProt-GO Annotation database in 2011.

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

The impact of focused Gene Ontology curation of specific mammalian systems.

UNLABELLED: The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome. AVAILABILITY: GO ANNOTATION DATA IS FREELY AVAILABLE FROM: ftp://ftp.geneontology.org/pub/go/gene-associations/

The Renal Gene Ontology Annotation Initiative.

The gene ontology (go) resource provides dynamic controlled vocabularies to aid in the description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). A renal-focused curation initiative, funded by Kidney Research UK and supported by the GO Consortium, has started at the European Bioinformatics Institute and aims to provide a detailed GO resource for mammalian proteins implicated in renal development and function. This report outlines the aims of this initiative and explains how the renal community can become involved to help improve the availability, quality and quantity of GO terms and their association to specific proteins.