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PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

Rodríguez-López R, Reyes-Palomares A, Sánchez-Jiménez F, Medina MÁ - BMC Bioinformatics (2014)

Bottom Line: Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations.One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions.PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. rorodriguez@uma.es.

ABSTRACT

Background: Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.

Results: PhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.

Conclusions: This framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

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Related in: MedlinePlus

PhenUMA knowledge base. A: Schematic representation of the PhenUMA knowledge base contents. Three types of relationships are included in the knowledge base: i) “known relationships” (solid lines) were extracted from the databases OMIM, Orphanet and STRING and also include the metabolic interactions from Veeramani and Bader [15]; ii) “inferred relationships” (dashed lines) were taken from the OMIM and Orphanet known relationships; and iii) “semantic similarity relationships” (dotted lines). For the semantic similarity relationships, scores were calculated using the HPO and GO. Genes (red triangles), OMIM diseases (yellow circles) and Orphanet diseases (blue octagons) are the components of these relationships. This schematic describes how inferred relationships were determined from known relationships; that is, how the dashed lines were deduced from the solid lines. B: Illustrative example of integration between phenotypic and functional gene-gene relationships as retrieved in PhenUMA for ornithine transcarbamylase (OTC; MIM# 300461) at a medium confidence level.
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Fig1: PhenUMA knowledge base. A: Schematic representation of the PhenUMA knowledge base contents. Three types of relationships are included in the knowledge base: i) “known relationships” (solid lines) were extracted from the databases OMIM, Orphanet and STRING and also include the metabolic interactions from Veeramani and Bader [15]; ii) “inferred relationships” (dashed lines) were taken from the OMIM and Orphanet known relationships; and iii) “semantic similarity relationships” (dotted lines). For the semantic similarity relationships, scores were calculated using the HPO and GO. Genes (red triangles), OMIM diseases (yellow circles) and Orphanet diseases (blue octagons) are the components of these relationships. This schematic describes how inferred relationships were determined from known relationships; that is, how the dashed lines were deduced from the solid lines. B: Illustrative example of integration between phenotypic and functional gene-gene relationships as retrieved in PhenUMA for ornithine transcarbamylase (OTC; MIM# 300461) at a medium confidence level.

Mentions: The initial stages of the development of PhenUMA were focused on building a consistent knowledge base, and subsequent efforts were dedicated to design a user-friendly web application. The knowledge base contains all of the information necessary to create the output networks, and the source data were retrieved from consolidated databases or from inferred relationships determined using different data processing methods (Figure 1A, schematic representation of the knowledge base). The web interface was implemented to make the query execution easier and to allow the visualization of outcome networks according to the Cytoscape Web 1.0.3 utility [16]. The tool was developed in Java, and the database was built using MySQL 5.0.45. PhenUMA and other resources such as tutorials and downloadable processed data are available on the web (http://www.phenuma.uma.es/). An illustrative example of all of the types of gene-gene relationships is shown in Figure 1B.Figure 1


PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

Rodríguez-López R, Reyes-Palomares A, Sánchez-Jiménez F, Medina MÁ - BMC Bioinformatics (2014)

PhenUMA knowledge base. A: Schematic representation of the PhenUMA knowledge base contents. Three types of relationships are included in the knowledge base: i) “known relationships” (solid lines) were extracted from the databases OMIM, Orphanet and STRING and also include the metabolic interactions from Veeramani and Bader [15]; ii) “inferred relationships” (dashed lines) were taken from the OMIM and Orphanet known relationships; and iii) “semantic similarity relationships” (dotted lines). For the semantic similarity relationships, scores were calculated using the HPO and GO. Genes (red triangles), OMIM diseases (yellow circles) and Orphanet diseases (blue octagons) are the components of these relationships. This schematic describes how inferred relationships were determined from known relationships; that is, how the dashed lines were deduced from the solid lines. B: Illustrative example of integration between phenotypic and functional gene-gene relationships as retrieved in PhenUMA for ornithine transcarbamylase (OTC; MIM# 300461) at a medium confidence level.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4260198&req=5

Fig1: PhenUMA knowledge base. A: Schematic representation of the PhenUMA knowledge base contents. Three types of relationships are included in the knowledge base: i) “known relationships” (solid lines) were extracted from the databases OMIM, Orphanet and STRING and also include the metabolic interactions from Veeramani and Bader [15]; ii) “inferred relationships” (dashed lines) were taken from the OMIM and Orphanet known relationships; and iii) “semantic similarity relationships” (dotted lines). For the semantic similarity relationships, scores were calculated using the HPO and GO. Genes (red triangles), OMIM diseases (yellow circles) and Orphanet diseases (blue octagons) are the components of these relationships. This schematic describes how inferred relationships were determined from known relationships; that is, how the dashed lines were deduced from the solid lines. B: Illustrative example of integration between phenotypic and functional gene-gene relationships as retrieved in PhenUMA for ornithine transcarbamylase (OTC; MIM# 300461) at a medium confidence level.
Mentions: The initial stages of the development of PhenUMA were focused on building a consistent knowledge base, and subsequent efforts were dedicated to design a user-friendly web application. The knowledge base contains all of the information necessary to create the output networks, and the source data were retrieved from consolidated databases or from inferred relationships determined using different data processing methods (Figure 1A, schematic representation of the knowledge base). The web interface was implemented to make the query execution easier and to allow the visualization of outcome networks according to the Cytoscape Web 1.0.3 utility [16]. The tool was developed in Java, and the database was built using MySQL 5.0.45. PhenUMA and other resources such as tutorials and downloadable processed data are available on the web (http://www.phenuma.uma.es/). An illustrative example of all of the types of gene-gene relationships is shown in Figure 1B.Figure 1

Bottom Line: Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations.One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions.PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. rorodriguez@uma.es.

ABSTRACT

Background: Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.

Results: PhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.

Conclusions: This framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

Show MeSH
Related in: MedlinePlus