Limits...
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.

Show MeSH

Related in: MedlinePlus

The representation of Langerhans cells in the Cell Ontology. A portion of the Cell Ontology is shown with ovals corresponding to cell types defined in the ontology and arrows corresponding to relations between those cell types. Langerhans cell is represented by a yellow oval; blue arrows correspond to is_a relations, and orange arrows correspond to develops_from relations. Only a subset of Langerhans cell parent types are included in the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2662812&req=5

Figure 1: The representation of Langerhans cells in the Cell Ontology. A portion of the Cell Ontology is shown with ovals corresponding to cell types defined in the ontology and arrows corresponding to relations between those cell types. Langerhans cell is represented by a yellow oval; blue arrows correspond to is_a relations, and orange arrows correspond to develops_from relations. Only a subset of Langerhans cell parent types are included in the figure.

Mentions: An example of the CL's use of multiple hierarchies is classification of Langerhans cell within the cell by organism and cell by nuclear number hierarchies as a subtype of animal cell and single nucleate cell, respectively (Figure 1). Langerhans cell is further classified within the functional hierarchy (Figure 1) as a subtype of:


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 representation of Langerhans cells in the Cell Ontology. A portion of the Cell Ontology is shown with ovals corresponding to cell types defined in the ontology and arrows corresponding to relations between those cell types. Langerhans cell is represented by a yellow oval; blue arrows correspond to is_a relations, and orange arrows correspond to develops_from relations. Only a subset of Langerhans cell parent types are included in the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The representation of Langerhans cells in the Cell Ontology. A portion of the Cell Ontology is shown with ovals corresponding to cell types defined in the ontology and arrows corresponding to relations between those cell types. Langerhans cell is represented by a yellow oval; blue arrows correspond to is_a relations, and orange arrows correspond to develops_from relations. Only a subset of Langerhans cell parent types are included in the figure.
Mentions: An example of the CL's use of multiple hierarchies is classification of Langerhans cell within the cell by organism and cell by nuclear number hierarchies as a subtype of animal cell and single nucleate cell, respectively (Figure 1). Langerhans cell is further classified within the functional hierarchy (Figure 1) as a subtype of:

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