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

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.

[1]  Andrew M. Jenkinson,et al.  The EBI RDF platform: linked open data for the life sciences , 2014, Bioinform..

[2]  Anna Zhukova,et al.  Modeling sample variables with an Experimental Factor Ontology , 2010, Bioinform..

[3]  Anuj R. Jaiswal,et al.  OMEN: A Probabilistic Ontology Mapping Tool , 2005, SEMWEB.

[4]  Amos Bairoch,et al.  The Cellosaurus, a Cell-Line Knowledge Resource. , 2018, Journal of biomolecular techniques : JBT.

[5]  J. Hasty,et al.  Dynamics of single-cell gene expression , 2006, Molecular systems biology.

[6]  Dino Di Carlo,et al.  Dynamic single-cell analysis for quantitative biology. , 2006, Analytical chemistry.

[7]  David J. States,et al.  A bioinformatics analysis of the cell line nomenclature , 2008, Bioinform..

[8]  Vassilios Ioannidis,et al.  ExPASy: SIB bioinformatics resource portal , 2012, Nucleic Acids Res..

[9]  Csongor Nyulas,et al.  BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications , 2011, Nucleic Acids Res..

[10]  Alan Ruttenberg,et al.  The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability , 2016, J. Biomed. Semant..

[11]  Robert Petryszak,et al.  ArrayExpress update—simplifying data submissions , 2014, Nucleic Acids Res..

[12]  Tudor Groza,et al.  The Human Phenotype Ontology in 2017 , 2016, Nucleic Acids Res..

[13]  Christopher J. Mungall,et al.  k-BOOM: A Bayesian approach to ontology structure inference, with applications in disease ontology construction , 2016, bioRxiv.

[14]  Roger A. Pedersen,et al.  Early Cell Fate Decisions of Human Embryonic Stem Cells and Mouse Epiblast Stem Cells Are Controlled by the Same Signalling Pathways , 2009, PloS one.

[15]  E. Shapiro,et al.  Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.

[16]  Antje Chang,et al.  BRENDA in 2017: new perspectives and new tools in BRENDA , 2016, Nucleic Acids Res..

[17]  S. Lewis,et al.  Uberon, an integrative multi-species anatomy ontology , 2012, Genome Biology.

[18]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[19]  Sean Bechhofer,et al.  The OWL API: A Java API for OWL ontologies , 2011, Semantic Web.

[20]  H. Drexler,et al.  Widespread intraspecies cross‐contamination of human tumor cell lines arising at source , 1999, International journal of cancer.

[21]  Solomon Eyal Shimony,et al.  Markov Network Based Ontology Matching , 2009, IJCAI.

[22]  Yue Liu,et al.  CLO: The cell line ontology , 2014, Journal of Biomedical Semantics.

[23]  S. O’Brien,et al.  Origin of the HIV-susceptible human CD4+ cell line H9. , 1989, AIDS research and human retroviruses.

[24]  Simon Jupp,et al.  A new Ontology Lookup Service at EMBL-EBI , 2015, SWAT4LS.

[25]  Jayanta Debnath,et al.  Morphogenesis and oncogenesis of MCF-10A mammary epithelial acini grown in three-dimensional basement membrane cultures. , 2003, Methods.

[26]  Bin Zhao,et al.  Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration , 2016, Nucleic Acids Res..