2016
DOI: 10.1186/s13326-016-0088-7
|View full text |Cite
|
Sign up to set email alerts
|

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability

Abstract: BackgroundThe Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies.Construction and contentRecent work on the CL has focused o… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
215
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
5
3
2

Relationship

2
8

Authors

Journals

citations
Cited by 239 publications
(227 citation statements)
references
References 62 publications
1
215
0
Order By: Relevance
“…Using the same quantification workflow ensured that molecular phenotype identifiers (genes, transcripts, exons and events) were consistent between individual studies. Furthermore, we harmonised sample metadata between studies and mapped all biological samples (cell types and tissues) to a common set of 24 distinct ontology terms from UBERON [20], Cell Ontology [21] and Experimental Factor Ontology [22]. This will allow users to easily find if the same cell types or tissues has been profiled in multiple studies ( Table 1).…”
Section: Data Analysis Workflowmentioning
confidence: 99%
“…Using the same quantification workflow ensured that molecular phenotype identifiers (genes, transcripts, exons and events) were consistent between individual studies. Furthermore, we harmonised sample metadata between studies and mapped all biological samples (cell types and tissues) to a common set of 24 distinct ontology terms from UBERON [20], Cell Ontology [21] and Experimental Factor Ontology [22]. This will allow users to easily find if the same cell types or tissues has been profiled in multiple studies ( Table 1).…”
Section: Data Analysis Workflowmentioning
confidence: 99%
“…Transcription Factor names as well as associated aliases were integrated primarily from [8] with a large number of transcription factor aliases also provided by [9]. Cell types and associated aliases were gathered primarily from Cell Ontology [10], with all terms descending from either the T cell ( CL_0000084 ) or NK cell ( CL_0000623 ) terms. For all entities, a list of aliases not present in the corresponding ontologies was manually curated after identifying the need for them individually, and all aliases/names were used to identify entity spans based on exact, case-insensitive token sequence matches.…”
Section: Named Entity Recognitionmentioning
confidence: 99%
“…In this regard, PRO terms have been supplied for precise entity annotation as requested by other ontology developers (e.g. the family level term ‘CD59-like glycoprotein’ [http://purl.obolibrary.org/obo/PR_000001809] requested by the Cell Ontology [CL] (17)), and to other members of the GO Consortium (for example, the hyperoxidized form of Tpxi1 [http://purl.obolibrary.org/obo/PR_000028935] requested by PomBase (18)). GO itself uses PRO terms to define certain terms.…”
Section: Enhancements Of Data and Coveragementioning
confidence: 99%