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OnEX: Exploring changes in life science ontologies.

Hartung M, Kirsten T, Gross A, Rahm E - BMC Bioinformatics (2009)

Bottom Line: The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application.Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology.OnEX provides a user-friendly web interface to explore information about changes in current life science ontologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de

ABSTRACT

Background: Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.

Results: We present OnEX (Ontology Evolution EXplorer) a system for exploring ontology changes. Currently, OnEX provides access to about 560 versions of 16 well-known life science ontologies. The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application. Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology.

Conclusion: OnEX provides a user-friendly web interface to explore information about changes in current life science ontologies. It is available at http://www.izbi.de/onex.

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Quantitative analysis workflow. Parts of the quantitative analysis workflow.
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Figure 3: Quantitative analysis workflow. Parts of the quantitative analysis workflow.

Mentions: An application of the first workflow is illustrated in the example scenario of Figure 3 focusing on changes in the GO sub-ontology biological processes (BP). The overview panel (Comparative Overview) shows basic statistics of all available ontologies. For instance, it is indicated that GO BP consists of approx. 16,500 concepts interconnected by 33,000 relationships in the March 2009 version as opposed to only 7,000 concepts and no relationships in the first available version of December 2002. The Trend Chart for GO BP illustrates a steady increase in the number of both concepts and relationships. It can be observed that relationships have been introduced in April 2003 and that a significant increase occurred between July 2006 and December 2006. Users can then navigate to Evolution Details to see average evolution statistics and quantitative changes between the captured ontology versions. For instance, the GO BP sub-ontology experienced approx. 130 concept additions per month while on average about 12 concepts are modified per month, i.e., have become obsolete or were deleted. The exact number of added, deleted, fused and obsolete marked concepts is displayed in a table that can be sorted according to different criteria such as the number of affected concepts. One can see that most additions occurred between the versions of September and November 2006 (971 concepts). If a user is interested in a specific version change, she may navigate to a further panel displaying the Affected Concepts per Change Type. As an example, in the July 2008 version of GO BP five concepts have become obsolete, e.g., GO:0034262 (autophagy in response to cellular starvation) and GO:0042477 (odontogenesis of calcareous or chitinous tooth). As a next step the user may navigate to details about the affected concepts by clicking on the accession number (see workflow in the following section).


OnEX: Exploring changes in life science ontologies.

Hartung M, Kirsten T, Gross A, Rahm E - BMC Bioinformatics (2009)

Quantitative analysis workflow. Parts of the quantitative analysis workflow.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2746816&req=5

Figure 3: Quantitative analysis workflow. Parts of the quantitative analysis workflow.
Mentions: An application of the first workflow is illustrated in the example scenario of Figure 3 focusing on changes in the GO sub-ontology biological processes (BP). The overview panel (Comparative Overview) shows basic statistics of all available ontologies. For instance, it is indicated that GO BP consists of approx. 16,500 concepts interconnected by 33,000 relationships in the March 2009 version as opposed to only 7,000 concepts and no relationships in the first available version of December 2002. The Trend Chart for GO BP illustrates a steady increase in the number of both concepts and relationships. It can be observed that relationships have been introduced in April 2003 and that a significant increase occurred between July 2006 and December 2006. Users can then navigate to Evolution Details to see average evolution statistics and quantitative changes between the captured ontology versions. For instance, the GO BP sub-ontology experienced approx. 130 concept additions per month while on average about 12 concepts are modified per month, i.e., have become obsolete or were deleted. The exact number of added, deleted, fused and obsolete marked concepts is displayed in a table that can be sorted according to different criteria such as the number of affected concepts. One can see that most additions occurred between the versions of September and November 2006 (971 concepts). If a user is interested in a specific version change, she may navigate to a further panel displaying the Affected Concepts per Change Type. As an example, in the July 2008 version of GO BP five concepts have become obsolete, e.g., GO:0034262 (autophagy in response to cellular starvation) and GO:0042477 (odontogenesis of calcareous or chitinous tooth). As a next step the user may navigate to details about the affected concepts by clicking on the accession number (see workflow in the following section).

Bottom Line: The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application.Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology.OnEX provides a user-friendly web interface to explore information about changes in current life science ontologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de

ABSTRACT

Background: Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.

Results: We present OnEX (Ontology Evolution EXplorer) a system for exploring ontology changes. Currently, OnEX provides access to about 560 versions of 16 well-known life science ontologies. The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application. Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology.

Conclusion: OnEX provides a user-friendly web interface to explore information about changes in current life science ontologies. It is available at http://www.izbi.de/onex.

Show MeSH