COBrA: a bio-ontology editor

Aitken, Stuart; Korf, Roman; Webber, Bonnie; Bard, Jonathan

doi:10.1093/bioinformatics/bti097

Cited by 15 publications

(10 citation statements)

References 2 publications

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“…and CoBrA 4 (Aitken et al, 2005) browsers (the former for complex ontologies, the latter for simple ones). Terms also carry appropriate dbxrefs from the Drosophila , VAO, zebrafish, Uberon, and human developmental anatomy ontologies.…”

Section: Methodsmentioning

confidence: 99%

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

Bard¹

2012

Front. Gene.

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This paper describes the AEO, an ontology of anatomical entities that expands the common anatomy reference ontology (CARO) and whose major novel feature is a type hierarchy of ~160 anatomical terms. The breadth of the AEO is wider than CARO as it includes both developmental and gender-specific classes, while the granularity of the AEO terms is at a level adequate to classify simple-tissues (~70 classes) characterized by their containing a predominantly single cell-type. For convenience and to facilitate interoperability, the AEO contains an abbreviated version of the ontology of cell-types (~100 classes) that is linked to these simple-tissue types. The AEO was initially based on an analysis of a broad range of animal anatomy ontologies and then upgraded as it was used to classify the ~2500 concepts in a new version of the ontology of human developmental anatomy (), a process that led to significant improvements in its structure and content, albeit with a possible focus on mammalian embryos. The AEO is intended to provide the formal classification expected in contemporary ontologies as well as capturing knowledge about anatomical structures not currently included in anatomical ontologies. The AEO may thus be useful in increasing the amount of tissue and cell-type knowledge in other anatomy ontologies, facilitating annotation of tissues that share common features, and enabling interoperability across anatomy ontologies. The AEO can be downloaded from .

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Section: Methodsmentioning

confidence: 99%

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

Bard¹

2012

Front. Gene.

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“…Several potentially useful ontology building and mapping applications were identified. Of those, the following tools were chosen for further evaluation: DAG/OBO-Edit (11), Protégé-OWL (12) and COBrA (13). The specific versions used for the initial evaluation of ontology editing tools were: DAG-Edit 1.418, OBO-Edit 1.001, COBrA 1.0 and Protégé 3.1 beta (build 185), with the Prompt plug-in (for Protégé).…”

Section: Evaluation Of Toolsmentioning

confidence: 99%

The mouse-human anatomy ontology mapping project

et al. 2012

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The overall objective of the Mouse–Human Anatomy Project (MHAP) was to facilitate the mapping and harmonization of anatomical terms used for mouse and human models by Mouse Genome Informatics (MGI) and the National Cancer Institute (NCI). The anatomy resources designated for this study were the Adult Mouse Anatomy (MA) ontology and the set of anatomy concepts contained in the NCI Thesaurus (NCIt). Several methods and software tools were identified and evaluated, then used to conduct an in-depth comparative analysis of the anatomy ontologies. Matches between mouse and human anatomy terms were determined and validated, resulting in a highly curated set of mappings between the two ontologies that has been used by other resources. These mappings will enable linking of data from mouse and human. As the anatomy ontologies have been expanded and refined, the mappings have been updated accordingly. Insights are presented into the overall process of comparing and mapping between ontologies, which may prove useful for further comparative analyses and ontology mapping efforts, especially those involving anatomy ontologies. Finally, issues concerning further development of the ontologies, updates to the mapping files, and possible additional applications and significance were considered.Database URL: http://obofoundry.org/cgi-bin/detail.cgi?id=ma2ncit

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“…The currently available biological ontologies have been established by different research groups and stored in individual databases, often complemented by ontology developing and editing programs such as COBrA [92], Protégé [93], Ontolingua Server [94] and Chimaera Ontology Environment [95]. It is feasible to speculate that the integration of the existing ontologies into a single semantic network system would be very useful for efficient queries in multiple biological knowledge domains.…”

Section: Biological Ontologymentioning

confidence: 99%

Integration of Biological Data with Semantic Networks

Hsing¹,

Cherkasov²

2006

CBIO

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In recent years, the broad utilization of high-throughput experimental techniques resulted in a vast amount of expression and interaction data, accompanied by information on metabolic, cell signaling and gene regulatory pathways accumulated in the literature and databases. Thus, one of the major goals of modern bioinformatics is to process and integrate heterogeneous biological data to provide an insight into the inner workings of a cell governed by complex interaction networks. The paper reviews the current development of semantic network (SN) technologies and their applications to the integration of genomic and proteomic data. We also elaborate on our own work that applies a semantic network approach to modeling complex cell signaling pathways and simulating the cause-effect of molecular interactions in human macrophages. The review is concluded with a discussion of the prospective use of semantic networks in bioinformatics practice as an efficient and general language for data integration, knowledge representation and inference.

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COBrA: a bio-ontology editor

Cited by 15 publications

References 2 publications

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

The mouse-human anatomy ontology mapping project

Integration of Biological Data with Semantic Networks

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