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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

Building network process. A: Input provided by the user of PhenUMA. Gene-Gene network building allows a set of genes or diseases as input (OMIM or Orphan diseases). In case of providing a disease list, genes associated with each disease (OMIM or Orphanet associations) are use to create the gene-gene network in the building network stage. Disease-Disease network can relate OMIM diseases or Orphan diseases and in both cases the input type are similar: a list of diseases or a set of genes. Phenotype query network building require of a set of phenotypes (HPO) as input, which is taken as a phenotype profile. B: Building network stage is divided in two parts: the seed network building that contains the relationships between de input set (genes, diseases or phenotypes) and the rest of elements included in the database and the network enrichment that consist in the addition of the rest of relationships included in the knowledge base (see Figure 1) between the elements related in each network.
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Fig3: Building network process. A: Input provided by the user of PhenUMA. Gene-Gene network building allows a set of genes or diseases as input (OMIM or Orphan diseases). In case of providing a disease list, genes associated with each disease (OMIM or Orphanet associations) are use to create the gene-gene network in the building network stage. Disease-Disease network can relate OMIM diseases or Orphan diseases and in both cases the input type are similar: a list of diseases or a set of genes. Phenotype query network building require of a set of phenotypes (HPO) as input, which is taken as a phenotype profile. B: Building network stage is divided in two parts: the seed network building that contains the relationships between de input set (genes, diseases or phenotypes) and the rest of elements included in the database and the network enrichment that consist in the addition of the rest of relationships included in the knowledge base (see Figure 1) between the elements related in each network.

Mentions: PhenUMA allows the retrieval of information related with a set of genes, diseases or phenotypes of interest. Figure 3 shows the building network stages for each type of input and output. When a query is executed, firstly a seed network is created from the input reported by the user; subsequently, this network is populated with the relationships included in the database for the type of data related (Figure 3B). For example, if a phenotypic similarity network is requested for one gene or one list of genes, the resulting network is populated with the functional, protein-protein interaction, metabolic and inferred relationships (see an example for ornithine transcarbamylase in Figure 1B). PhenUMA allows users to select among three different levels of confidence, termed low, medium and high, for both phenotypic similarities (the 98th, 99th and 99.5th percentiles, respectively) and functional similarities (the 99.5th, 99.8th and 99.9th percentiles, respectively).Figure 3


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)

Building network process. A: Input provided by the user of PhenUMA. Gene-Gene network building allows a set of genes or diseases as input (OMIM or Orphan diseases). In case of providing a disease list, genes associated with each disease (OMIM or Orphanet associations) are use to create the gene-gene network in the building network stage. Disease-Disease network can relate OMIM diseases or Orphan diseases and in both cases the input type are similar: a list of diseases or a set of genes. Phenotype query network building require of a set of phenotypes (HPO) as input, which is taken as a phenotype profile. B: Building network stage is divided in two parts: the seed network building that contains the relationships between de input set (genes, diseases or phenotypes) and the rest of elements included in the database and the network enrichment that consist in the addition of the rest of relationships included in the knowledge base (see Figure 1) between the elements related in each network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Building network process. A: Input provided by the user of PhenUMA. Gene-Gene network building allows a set of genes or diseases as input (OMIM or Orphan diseases). In case of providing a disease list, genes associated with each disease (OMIM or Orphanet associations) are use to create the gene-gene network in the building network stage. Disease-Disease network can relate OMIM diseases or Orphan diseases and in both cases the input type are similar: a list of diseases or a set of genes. Phenotype query network building require of a set of phenotypes (HPO) as input, which is taken as a phenotype profile. B: Building network stage is divided in two parts: the seed network building that contains the relationships between de input set (genes, diseases or phenotypes) and the rest of elements included in the database and the network enrichment that consist in the addition of the rest of relationships included in the knowledge base (see Figure 1) between the elements related in each network.
Mentions: PhenUMA allows the retrieval of information related with a set of genes, diseases or phenotypes of interest. Figure 3 shows the building network stages for each type of input and output. When a query is executed, firstly a seed network is created from the input reported by the user; subsequently, this network is populated with the relationships included in the database for the type of data related (Figure 3B). For example, if a phenotypic similarity network is requested for one gene or one list of genes, the resulting network is populated with the functional, protein-protein interaction, metabolic and inferred relationships (see an example for ornithine transcarbamylase in Figure 1B). PhenUMA allows users to select among three different levels of confidence, termed low, medium and high, for both phenotypic similarities (the 98th, 99th and 99.5th percentiles, respectively) and functional similarities (the 99.5th, 99.8th and 99.9th percentiles, respectively).Figure 3

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