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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.


Change history of GO:0003700 in OnEX. Detailed change history for concept GO:0003700 ("sequence-specific DNA binding transcription factor activity") using the concept-based analysis module of OnEX.
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Figure 6: Change history of GO:0003700 in OnEX. Detailed change history for concept GO:0003700 ("sequence-specific DNA binding transcription factor activity") using the concept-based analysis module of OnEX.

Mentions: OnEX is useful to find out which changes affected our application scenario. When inspecting the change history of GO:0003700 (Figure 6), we observe several attribute value changes of the concept name and description. Initially the concept was named "transcription factor", later "transcription factor activity" and currently it is denoted as "sequence-specific DNA binding transcription factor activity". Furthermore, its is_a relationships to parent concepts were revised. So in 2010-08, GO:0003700 was temporarily moved from parents GO:0030528 ("transcription regulator activity") and GO:0003677 ("DNA binding") to the MF root GO:0003674. In 2010-10, it was moved again to its new parent node GO:0001071 ("nucleic acid binding transcription factor activity"). The results in Figure 4(a) and 4(b) show that the former parent concept GO:0030528 was significant in the 2009-09 evaluation but no longer in 2011-03 due to the significant structural changes in this region. Figure 4(c) also reveals a reduced number of indirect (propagated) annotations for GO:0030528 in 2011-03 due to the lack of the incoming edges from the moved GO:0003700 concept (reducing the number of indirect annotations from ~1150 to only ~440). This underlines that term enrichment algorithms depend much on indirectly propagated annotations and therefore on structural ontology changes.


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)

Change history of GO:0003700 in OnEX. Detailed change history for concept GO:0003700 ("sequence-specific DNA binding transcription factor activity") using the concept-based analysis module of OnEX.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Change history of GO:0003700 in OnEX. Detailed change history for concept GO:0003700 ("sequence-specific DNA binding transcription factor activity") using the concept-based analysis module of OnEX.
Mentions: OnEX is useful to find out which changes affected our application scenario. When inspecting the change history of GO:0003700 (Figure 6), we observe several attribute value changes of the concept name and description. Initially the concept was named "transcription factor", later "transcription factor activity" and currently it is denoted as "sequence-specific DNA binding transcription factor activity". Furthermore, its is_a relationships to parent concepts were revised. So in 2010-08, GO:0003700 was temporarily moved from parents GO:0030528 ("transcription regulator activity") and GO:0003677 ("DNA binding") to the MF root GO:0003674. In 2010-10, it was moved again to its new parent node GO:0001071 ("nucleic acid binding transcription factor activity"). The results in Figure 4(a) and 4(b) show that the former parent concept GO:0030528 was significant in the 2009-09 evaluation but no longer in 2011-03 due to the significant structural changes in this region. Figure 4(c) also reveals a reduced number of indirect (propagated) annotations for GO:0030528 in 2011-03 due to the lack of the incoming edges from the moved GO:0003700 concept (reducing the number of indirect annotations from ~1150 to only ~440). This underlines that term enrichment algorithms depend much on indirectly propagated annotations and therefore on structural ontology changes.

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.