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GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.

Kirsten T, Gross A, Hartung M, Rahm E - J Biomed Semantics (2011)

Bottom Line: Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.We introduce the component-based infrastructure and show analysis results for selected components and life science applications.GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany. tkirsten@izbi.uni-leipzig.de.

ABSTRACT

Background: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA.

Conclusions: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

No MeSH data available.


Stable and unstable ontology regions in GO Molecular Functions using the Region Analyzer. The figure shows the region stability of GO Molecular functions concepts between 2009-09 and 2011-03 (monthly versions). Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). (a) Region stability of slim terms on the first level of GO Molecular function. (b) Region stability of the detected significant result concepts and their parents (from our application scenario in Figure 4).
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Figure 9: Stable and unstable ontology regions in GO Molecular Functions using the Region Analyzer. The figure shows the region stability of GO Molecular functions concepts between 2009-09 and 2011-03 (monthly versions). Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). (a) Region stability of slim terms on the first level of GO Molecular function. (b) Region stability of the detected significant result concepts and their parents (from our application scenario in Figure 4).

Mentions: Figure 9(a) exemplarily shows the region stability for slim terms (see http://www.geneontology.org/GO.slims.shtml) on the first level of GO-MF. Some of the top level slim terms remained completely stable, e.g., "nutrient reservoir activity" (GO:0045735) while others changed substantially, e.g. "translation regulator activity" (GO:0045182). Moreover, Figure 9(b) shows the region stability for the significant categories in our example scenario in the period 2009-09 to 2011-03 (monthly versions). Two concepts are completely stable (green), three show intermediate stability and four concepts are unstable. Especially the region of the newly introduced concept GO:0001071 and its child GO:0003700 are unstable. Interestingly, concepts "water binding" (GO:0050824) and "ice binding" (GO:0050825) remained stable and still appear only in the 2011-03 result set. This could be indirectly caused, e.g., the number of the annotated genes (one important input of the used hypergeometric test) decreased due to information reducing operations, such as setting concepts to obsolete or concept merges.


GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.

Kirsten T, Gross A, Hartung M, Rahm E - J Biomed Semantics (2011)

Stable and unstable ontology regions in GO Molecular Functions using the Region Analyzer. The figure shows the region stability of GO Molecular functions concepts between 2009-09 and 2011-03 (monthly versions). Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). (a) Region stability of slim terms on the first level of GO Molecular function. (b) Region stability of the detected significant result concepts and their parents (from our application scenario in Figure 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3198872&req=5

Figure 9: Stable and unstable ontology regions in GO Molecular Functions using the Region Analyzer. The figure shows the region stability of GO Molecular functions concepts between 2009-09 and 2011-03 (monthly versions). Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). (a) Region stability of slim terms on the first level of GO Molecular function. (b) Region stability of the detected significant result concepts and their parents (from our application scenario in Figure 4).
Mentions: Figure 9(a) exemplarily shows the region stability for slim terms (see http://www.geneontology.org/GO.slims.shtml) on the first level of GO-MF. Some of the top level slim terms remained completely stable, e.g., "nutrient reservoir activity" (GO:0045735) while others changed substantially, e.g. "translation regulator activity" (GO:0045182). Moreover, Figure 9(b) shows the region stability for the significant categories in our example scenario in the period 2009-09 to 2011-03 (monthly versions). Two concepts are completely stable (green), three show intermediate stability and four concepts are unstable. Especially the region of the newly introduced concept GO:0001071 and its child GO:0003700 are unstable. Interestingly, concepts "water binding" (GO:0050824) and "ice binding" (GO:0050825) remained stable and still appear only in the 2011-03 result set. This could be indirectly caused, e.g., the number of the annotated genes (one important input of the used hypergeometric test) decreased due to information reducing operations, such as setting concepts to obsolete or concept merges.

Bottom Line: Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.We introduce the component-based infrastructure and show analysis results for selected components and life science applications.GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany. tkirsten@izbi.uni-leipzig.de.

ABSTRACT

Background: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA.

Conclusions: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

No MeSH data available.