A bioinformatics analysis of the cell line nomenclature

Sarntivijai, Sirarat; Ade, Alexander S.; Athey, Brian D.; States, David J.

doi:10.1093/bioinformatics/btn502

Cited by 27 publications

(20 citation statements)

References 18 publications

(10 reference statements)

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“…Such duplicated cell line labels could lead to confusion when modeling and reporting experimental results. The ambiguity of cell lines should be addressed to identify a differentia to be coded onto the ontology, or by a naming authority of consensus [ 14 , 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…For example, Cell line 17/14 (accessioned HB-8153 in ATCC catalog) is not the same cell line as 171-4 (accessioned HB-296 in ATCC catalog). Normalizing these two cell lines by removing punctuation marks will result in a false normalization [ 14 ]. After extracting annotations and related information from the three resources, the mapping pipeline was divided into three consecutive steps.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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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.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1979-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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“…Both ontologies were downloaded on 19 May 2017. There exist no widely accepted rules on how to name cell lines, thus resulting in a non-standardised vocabulary of cell lines as described in [ 8 ]. A number of ambiguous cell line names are still in use until today.…”

Section: Methodsmentioning

confidence: 99%

Usage of cell nomenclature in biomedical literature

2017

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BackgroundCell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell nomenclature in publications at a large scale for understanding the level of uptake of cell nomenclature in literature by scientists. In this study, we analyse the usage of cell nomenclature, both in Vivo, and in Vitro in biomedical literature by using text mining methods and present our results.ResultsWe identified 59% of the cell type classes in the Cell Ontology and 13% of the cell line classes in the Cell Line Ontology in the literature. Our analysis showed that cell line nomenclature is much more ambiguous compared to the cell type nomenclature. However, trends indicate that standardised nomenclature for cell lines and cell types are being increasingly used in publications by the scientists.ConclusionsOur findings provide an insight to understand how experimental cells are described in publications and may allow for an improved standardisation of cell type and cell line nomenclature as well as can be utilised to develop efficient text mining applications on cell types and cell lines. All data generated in this study is available at https://github.com/shenay/CellNomenclatureStudy.

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“…The Cell Ontology (CL; http://cellontology.org/) provides a structured and classified vocabulary for natural cell types (Bard et al 2005;Diehl et al 2016). The Cell Line Ontology (CLO; http://www.clo-ontology.org/) was created to categorize cell lines, defined as a "genetically stable and homogeneous population of cultured cells that share a common propagation history" (Sarntivijai et al 2008(Sarntivijai et al , 2014. The CL and CLO enable unambiguous identification of the sample source and are thus critical for data quality.…”

Section: Ontologies For Cells and Cell Linesmentioning

confidence: 99%

Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research

Emmerich¹,

Harris

2019

Good Research Practice in Non-Clinical Pharmacology and Biomedicine

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Insufficient description of experimental practices can contribute to difficulties in reproducing research findings. In response to this, "minimum information" guidelines have been developed for different disciplines. These standards help ensure that the complete experiment is described, including both experimental protocols and data processing methods, allowing a critical evaluation of the whole process and the potential recreation of the work. Selected examples of minimum information checklists with relevance for in vitro research are presented here and are collected by and registered at the MIBBI/FAIRsharing Information Resource portal. In addition, to support integrative research and to allow for comparisons and data sharing across studies, ontologies and vocabularies need to be defined and integrated across areas of in vitro research. As examples, this chapter addresses ontologies for cells and bioassays and discusses their importance for in vitro studies. Finally, specific quality requirements for important in vitro research tools (like chemical probes, antibodies, and cell lines) are suggested, and remaining issues are discussed.

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A bioinformatics analysis of the cell line nomenclature

Abstract: The web ontology file for this cell line collection can be downloaded at http://www.stateslab.org/data/celllineOntology/cellline.zip.

Cited by 27 publications

References 18 publications

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

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

Usage of cell nomenclature in biomedical literature

Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research

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