TY - JOUR
T1 - An improved ontological representation of dendritic cells as a paradigm for all cell types
AU - Masci, Anna Maria
AU - Arighi, Cecilia N.
AU - Diehl, Alexander D.
AU - Lieberman, Anne E.
AU - Mungall, Chris
AU - Scheuermann, Richard H.
AU - Smith, Barry
AU - Cowell, Lindsay G.
N1 - Funding Information:
LGC's contributions were supported by a Career Award from the Burroughs-Wellcome Fund, NIAID grant R01 AI077706, and NIAID grant R01 AI068804. AMM's contributions were supported by NIAID grant AI50019. CNA's contributions were supported by NIH grant 1 R01 GM080646-01. ADD's contributions were supported by NHGRI grant HG002273. CJM's contributions were supported by NHGRI grant HG002273 and NIH grant HG004028-01. RHS's contributions were supported by the NIAID through the Bioinformatics Integration Support Contract (N01 AI40076). BS's contributions were funded in part through the NIH Roadmap for Medical Research grant to the National Center for Biomedical Ontology (1 U 54 HG004028). We would like to thank Luigi Racioppi and Bali Pulendran for helpful discussion of flow cytometry and dendritic cell biology. We would
PY - 2009/2/25
Y1 - 2009/2/25
N2 - 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.
AB - 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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U2 - 10.1186/1471-2105-10-70
DO - 10.1186/1471-2105-10-70
M3 - Article
C2 - 19243617
AN - SCOPUS:63749103597
SN - 1471-2105
VL - 10
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 70
ER -