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Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis

BACKGROUND: Semantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resour...

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Autores principales: Taboada, María, Martínez, Diego, Pilo, Belén, Jiménez-Escrig, Adriano, Robinson, Peter N, Sobrido, María J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444309/
https://www.ncbi.nlm.nih.gov/pubmed/22849591
http://dx.doi.org/10.1186/1472-6947-12-78
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author Taboada, María
Martínez, Diego
Pilo, Belén
Jiménez-Escrig, Adriano
Robinson, Peter N
Sobrido, María J
author_facet Taboada, María
Martínez, Diego
Pilo, Belén
Jiménez-Escrig, Adriano
Robinson, Peter N
Sobrido, María J
author_sort Taboada, María
collection PubMed
description BACKGROUND: Semantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical practice. This is especially true for the construction of mutation databases. This paper presents a way of harmonizing abstract phenotype descriptions with patient data from clinical practice, and querying this dataset about relationships between phenotypes and genetic variants, at different levels of abstraction. METHODS: Due to the current availability of ontological and terminological resources that have already reached some consensus in biomedicine, a reuse-based ontology engineering approach was followed. The proposed approach uses the Ontology Web Language (OWL) to represent the phenotype ontology and the patient model, the Semantic Web Rule Language (SWRL) to bridge the gap between phenotype descriptions and clinical data, and the Semantic Query Web Rule Language (SQWRL) to query relevant phenotype-genotype bidirectional relationships. The work tests the use of semantic web technology in the biomedical research domain named cerebrotendinous xanthomatosis (CTX), using a real dataset and ontologies. RESULTS: A framework to query relevant phenotype-genotype bidirectional relationships is provided. Phenotype descriptions and patient data were harmonized by defining 28 Horn-like rules in terms of the OWL concepts. In total, 24 patterns of SWQRL queries were designed following the initial list of competency questions. As the approach is based on OWL, the semantic of the framework adapts the standard logical model of an open world assumption. CONCLUSIONS: This work demonstrates how semantic web technologies can be used to support flexible representation and computational inference mechanisms required to query patient datasets at different levels of abstraction. The open world assumption is especially good for describing only partially known phenotype-genotype relationships, in a way that is easily extensible. In future, this type of approach could offer researchers a valuable resource to infer new data from patient data for statistical analysis in translational research. In conclusion, phenotype description formalization and mapping to clinical data are two key elements for interchanging knowledge between basic and clinical research.
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spelling pubmed-34443092012-09-18 Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis Taboada, María Martínez, Diego Pilo, Belén Jiménez-Escrig, Adriano Robinson, Peter N Sobrido, María J BMC Med Inform Decis Mak Research Article BACKGROUND: Semantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical practice. This is especially true for the construction of mutation databases. This paper presents a way of harmonizing abstract phenotype descriptions with patient data from clinical practice, and querying this dataset about relationships between phenotypes and genetic variants, at different levels of abstraction. METHODS: Due to the current availability of ontological and terminological resources that have already reached some consensus in biomedicine, a reuse-based ontology engineering approach was followed. The proposed approach uses the Ontology Web Language (OWL) to represent the phenotype ontology and the patient model, the Semantic Web Rule Language (SWRL) to bridge the gap between phenotype descriptions and clinical data, and the Semantic Query Web Rule Language (SQWRL) to query relevant phenotype-genotype bidirectional relationships. The work tests the use of semantic web technology in the biomedical research domain named cerebrotendinous xanthomatosis (CTX), using a real dataset and ontologies. RESULTS: A framework to query relevant phenotype-genotype bidirectional relationships is provided. Phenotype descriptions and patient data were harmonized by defining 28 Horn-like rules in terms of the OWL concepts. In total, 24 patterns of SWQRL queries were designed following the initial list of competency questions. As the approach is based on OWL, the semantic of the framework adapts the standard logical model of an open world assumption. CONCLUSIONS: This work demonstrates how semantic web technologies can be used to support flexible representation and computational inference mechanisms required to query patient datasets at different levels of abstraction. The open world assumption is especially good for describing only partially known phenotype-genotype relationships, in a way that is easily extensible. In future, this type of approach could offer researchers a valuable resource to infer new data from patient data for statistical analysis in translational research. In conclusion, phenotype description formalization and mapping to clinical data are two key elements for interchanging knowledge between basic and clinical research. BioMed Central 2012-07-31 /pmc/articles/PMC3444309/ /pubmed/22849591 http://dx.doi.org/10.1186/1472-6947-12-78 Text en Copyright ©2012 Taboada et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Taboada, María
Martínez, Diego
Pilo, Belén
Jiménez-Escrig, Adriano
Robinson, Peter N
Sobrido, María J
Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title_full Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title_fullStr Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title_full_unstemmed Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title_short Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
title_sort querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444309/
https://www.ncbi.nlm.nih.gov/pubmed/22849591
http://dx.doi.org/10.1186/1472-6947-12-78
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