Comparison, alignment, and synchronization of cell line information between CLO and EFO

BMC Bioinformatics

Research

Yongqun He¹  Edison Ong²  Sirarat Sarntivijai³  Helen Parkinson³  Simon Jupp³ 

[1] Center of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;Unit of Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA;Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK;Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK;

关键词: Cell line;    Experimental factor ontology;    Cell line ontology;    Data integration;    Data mapping;   

DOI  :  10.1186/s12859-017-1979-z

来源: Springer

【 摘 要 】

BackgroundThe Experimental Factor Ontology (EFO) is an application ontology driven by experimental variables including cell lines to organize and describe the diverse experimental variables and data resided in the EMBL-EBI resources. The Cell Line Ontology (CLO) is an OBO community-based ontology that contains information of immortalized cell lines and relevant experimental components. EFO integrates and extends ontologies from the bio-ontology community to drive a number of practical applications. It is desirable that the community shares design patterns and therefore that EFO reuses the cell line representation from the Cell Line Ontology (CLO). There are, however, challenges to be addressed when developing a common ontology design pattern for representing cell lines in both EFO and CLO.ResultsIn this study, we developed a strategy to compare and map cell line terms between EFO and CLO. We examined Cellosaurus resources for EFO-CLO cross-references. Text labels of cell lines from both ontologies were verified by biological information axiomatized in each source. The study resulted in the identification 873 EFO-CLO aligned and 344 EFO unique immortalized permanent cell lines. All of these cell lines were updated to CLO and the cell line related information was merged. A design pattern that integrates EFO and CLO was also developed.ConclusionOur study compared, aligned, and synchronized the cell line information between CLO and EFO. The final updated CLO will be examined as the candidate ontology to import and replace eligible EFO cell line classes thereby supporting the interoperability in the bio-ontology domain. Our mapping pipeline illustrates the use of ontology in aiding biological data standardization and integration through the biological and semantics content of cell lines.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

Fig. 1