Search

Abstract Background Maintaining a bio-ontology in the long term requires improving and updating its contents so that it adequately captures what is known about biological phenomena. This paper illustrates how these processes are carried out, by studying the ways in which curators at the Gene Ontology have hitherto incorporated new knowledge into their resource. Results Five types of circumstances are singled out as warranting changes in the ontology: (1) the emergence of anomalies within GO; (2) the extension of the scope of GO; (3) divergence in how terminology is used across user communities; (4) new discoveries that change the meaning of the terms used and their relations to each other; and (5) the extension of the range of relations used to link entities or processes described by GO terms. Conclusion This study illustrates the difficulties involved in applying general standards to the development of a specific ontology. Ontology curation aims to produce a faithful representation of knowledge domains as they keep developing, which requires the translation of general guidelines into specific representations of reality and an understanding of how scientific knowledge is produced and constantly updated. In this context, it is important that trained curators with technical expertise in the scientific field(s) in question are involved in supervising ontology shifts and identifying inaccuracies.

Abstract Background The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL. Results Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types. Conclusion Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.

Abstract Background Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration. Results To enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function. Conclusion This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from http://www.obofoundry.org.

Abstract Background The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community. Results The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature. Conclusion The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki http://wiki.geneontology.org/index.php/Muscle_Biology.