Research article

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

Anna Maria Masci^1,2,3 , Cecilia N Arighi⁴ , Alexander D Diehl⁵ , Anne E Lieberman¹ , Chris Mungall⁶ , Richard H Scheuermann⁷ , Barry Smith^8* and Lindsay G Cowell^1*

¹  Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA

²  Department of Cellular and Molecular Biology and Pathology, University of Naples, Naples, Italy

³  Laboratory of Immunobiology of Cardiovascular Diseases, Department of Medical Science and Rehabilitation, IRCCS San Raffaele Pisana, Roma, Italy

⁴  Protein Information Resource, Georgetown University Medical Center, Washington, DC, USA

⁵  Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA

⁶  Lawrence Berkeley National Laboratory, Berkeley CA, USA

⁷  Department of Pathology, Division of Biomedical Informatics, UT Southwestern Medical Center, Dallas, TX, USA

⁸  Department of Philosophy and Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2009, 10:70doi:10.1186/1471-2105-10-70

Published:	25 February 2009

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 webcite.