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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

Subsets of inferred and phenotypically similar gene pairs. Venn diagram showing the distribution of gene pairs between a dataset of inferred relationships (from the union of OMIM and Orphanet) and the phenotypic similarity gene network at a low level of confidence corresponding to the 98th percentile.
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Fig4: Subsets of inferred and phenotypically similar gene pairs. Venn diagram showing the distribution of gene pairs between a dataset of inferred relationships (from the union of OMIM and Orphanet) and the phenotypic similarity gene network at a low level of confidence corresponding to the 98th percentile.

Mentions: The gene-gene network obtained using semantic similarity methods and the gene-gene inference network from known interactions (both OMIM and Orphanet) were compared to study their mutual coverage. Three distinct subsets were distinguished (Figure 4): inferred pairs of genes that are not included in phenotypic similarity gene network (Inferred OUT), inferred pairs of genes that are in the phenotypic similarity gene network (Inferred IN) and novel pairs of genes that are exclusively in the phenotypic similarity gene network. These latter genes represent more than 90% of all computed phenotypic similarities (22,833 of 24,902 gene pairs). They are considered novel because the involved genes are not co-associated with the same genetic disease based on the current information in OMIM and Orphanet. Notably, 1606 genes in OMIM and 792 genes Orphanet are associated with only one monogenic disease so they would appear as unconnected in inferred networks. Nevertheless, more than 49% and 61% of these genes, respectively, are linked to other genes with phenotypic similarity in PhenUMA.Figure 4


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)

Subsets of inferred and phenotypically similar gene pairs. Venn diagram showing the distribution of gene pairs between a dataset of inferred relationships (from the union of OMIM and Orphanet) and the phenotypic similarity gene network at a low level of confidence corresponding to the 98th percentile.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Subsets of inferred and phenotypically similar gene pairs. Venn diagram showing the distribution of gene pairs between a dataset of inferred relationships (from the union of OMIM and Orphanet) and the phenotypic similarity gene network at a low level of confidence corresponding to the 98th percentile.
Mentions: The gene-gene network obtained using semantic similarity methods and the gene-gene inference network from known interactions (both OMIM and Orphanet) were compared to study their mutual coverage. Three distinct subsets were distinguished (Figure 4): inferred pairs of genes that are not included in phenotypic similarity gene network (Inferred OUT), inferred pairs of genes that are in the phenotypic similarity gene network (Inferred IN) and novel pairs of genes that are exclusively in the phenotypic similarity gene network. These latter genes represent more than 90% of all computed phenotypic similarities (22,833 of 24,902 gene pairs). They are considered novel because the involved genes are not co-associated with the same genetic disease based on the current information in OMIM and Orphanet. Notably, 1606 genes in OMIM and 792 genes Orphanet are associated with only one monogenic disease so they would appear as unconnected in inferred networks. Nevertheless, more than 49% and 61% of these genes, respectively, are linked to other genes with phenotypic similarity in PhenUMA.Figure 4

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