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An improved ontological representation of dendritic cells as a paradigm for all cell types.

Masci AM, Arighi CN, Diehl AD, Lieberman AE, Mungall C, Scheuermann RH, Smith B, Cowell LG - BMC Bioinformatics (2009)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu

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.

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The ontologies and relations referred to in the Dendritic Cell Ontology (DC-CL). The rectangles and ovals represent ontologies, and the arrows represent relations joining terms in the ontologies. Abbreviations for the ontology names are shown in normal font, and the relations used to link DC-CL to each ontology are shown in italics. The black arrow indicates relations used to join DC-CL terms to other DC-CL terms, while the blue arrows indicate trans-ontological relations. Ontologies and relations shown in rectangles are used to define DC-CL types, while the ontologies and relations shown in ovals are used to make non-classificatory assertions about DC-CL types. Abbreviations used in the figure are: GO CC, Gene Ontology Cellular Component Ontology; PRO, Protein Ontology; IO, Immunology Ontology; FMA, Foundational Model of Anatomy; and GO BP, Gene Ontology Biological Process Ontology.
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Figure 3: The ontologies and relations referred to in the Dendritic Cell Ontology (DC-CL). The rectangles and ovals represent ontologies, and the arrows represent relations joining terms in the ontologies. Abbreviations for the ontology names are shown in normal font, and the relations used to link DC-CL to each ontology are shown in italics. The black arrow indicates relations used to join DC-CL terms to other DC-CL terms, while the blue arrows indicate trans-ontological relations. Ontologies and relations shown in rectangles are used to define DC-CL types, while the ontologies and relations shown in ovals are used to make non-classificatory assertions about DC-CL types. Abbreviations used in the figure are: GO CC, Gene Ontology Cellular Component Ontology; PRO, Protein Ontology; IO, Immunology Ontology; FMA, Foundational Model of Anatomy; and GO BP, Gene Ontology Biological Process Ontology.

Mentions: We next identified a set of ontology development principles designed to maximize both an ontology's utility for computational analysis and reasoning and its interoperability with existing resources. The basis of this approach is use of relations from the OBO Foundry Relation Ontology (RO) [19] to link terms both within DC-CL and also from DC-CL to other OBO Foundry ontologies (Figure 3), as described in [13,20]. Relations in the RO are formally defined to support automated reasoning [19]. In addition, in constructing DC-CL we employed accepted principles of ontology development outlined by the OBO Foundry [13], including the use of genus-differentia definitions [21].


An improved ontological representation of dendritic cells as a paradigm for all cell types.

Masci AM, Arighi CN, Diehl AD, Lieberman AE, Mungall C, Scheuermann RH, Smith B, Cowell LG - BMC Bioinformatics (2009)

The ontologies and relations referred to in the Dendritic Cell Ontology (DC-CL). The rectangles and ovals represent ontologies, and the arrows represent relations joining terms in the ontologies. Abbreviations for the ontology names are shown in normal font, and the relations used to link DC-CL to each ontology are shown in italics. The black arrow indicates relations used to join DC-CL terms to other DC-CL terms, while the blue arrows indicate trans-ontological relations. Ontologies and relations shown in rectangles are used to define DC-CL types, while the ontologies and relations shown in ovals are used to make non-classificatory assertions about DC-CL types. Abbreviations used in the figure are: GO CC, Gene Ontology Cellular Component Ontology; PRO, Protein Ontology; IO, Immunology Ontology; FMA, Foundational Model of Anatomy; and GO BP, Gene Ontology Biological Process Ontology.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2662812&req=5

Figure 3: The ontologies and relations referred to in the Dendritic Cell Ontology (DC-CL). The rectangles and ovals represent ontologies, and the arrows represent relations joining terms in the ontologies. Abbreviations for the ontology names are shown in normal font, and the relations used to link DC-CL to each ontology are shown in italics. The black arrow indicates relations used to join DC-CL terms to other DC-CL terms, while the blue arrows indicate trans-ontological relations. Ontologies and relations shown in rectangles are used to define DC-CL types, while the ontologies and relations shown in ovals are used to make non-classificatory assertions about DC-CL types. Abbreviations used in the figure are: GO CC, Gene Ontology Cellular Component Ontology; PRO, Protein Ontology; IO, Immunology Ontology; FMA, Foundational Model of Anatomy; and GO BP, Gene Ontology Biological Process Ontology.
Mentions: We next identified a set of ontology development principles designed to maximize both an ontology's utility for computational analysis and reasoning and its interoperability with existing resources. The basis of this approach is use of relations from the OBO Foundry Relation Ontology (RO) [19] to link terms both within DC-CL and also from DC-CL to other OBO Foundry ontologies (Figure 3), as described in [13,20]. Relations in the RO are formally defined to support automated reasoning [19]. In addition, in constructing DC-CL we employed accepted principles of ontology development outlined by the OBO Foundry [13], including the use of genus-differentia definitions [21].

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu

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.

Show MeSH
Related in: MedlinePlus