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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

Phenotypically similar disorders associated with SSADH deficiency at different confidence levels. PhenUMA results of the query for SSADH deficiency (MIM# 271980) at different levels of confidence A: Low, B: Medium and C: High. All panels are screenshots of the PhenUMA results that were edited to highlight the main clinical features associated with each OMIM disease cluster.
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Fig5: Phenotypically similar disorders associated with SSADH deficiency at different confidence levels. PhenUMA results of the query for SSADH deficiency (MIM# 271980) at different levels of confidence A: Low, B: Medium and C: High. All panels are screenshots of the PhenUMA results that were edited to highlight the main clinical features associated with each OMIM disease cluster.

Mentions: PhenUMA allows users to obtain coherent disease and gene clusters related to a particular disease, gene or set of phenotypes for research purposes. As an example, we will examine succinic semialdehyde dehydrogenase deficiency (SSADHD; MIM# 271980), also known as 4-Hydroxybutyric aciduria, a rare inborn error of metabolism associated with mutations in Locus ALDH5A1 (ALDH5A1; MIM# 610045). We used PhenUMA to search for all of the phenotypic similarities to SSADH deficiency at each of the confidence levels of low, medium and high. These results show how different clusters of diseases are generated and belong to distinguishable groups according to their phenotypic similarity score (Figure 5). For instance, a low cutoff for phenotypic similarity gives four large overlapped and densely interconnected clusters of disorders associated with epilepsy, seizures, neurodegenerative processes, neurophysiological abnormalities and behavioral problems (Figure 5A). SSADH deficiency has a higher frequency of connections to the disorders that involve convulsions, epilepsy or changes in behavior, and the connection becomes more evident when we increase the similarity score to the medium level of significance (Figure 5B). In this case, the established clusters have a more clearly defined structure and relationships to SSADH deficiency. Indeed, three non-overlapped clusters are apparent in Figure 5B. However, although the phenotypic coherence increased, the interconnections between clusters (OMIM diseases) remained abundant in the resulting network (Figure 5B). Therefore, we constrained the query to the most significant phenotypic similarities for SSADH deficiency by selecting the “high confidence” option in PhenUMA.Figure 5


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)

Phenotypically similar disorders associated with SSADH deficiency at different confidence levels. PhenUMA results of the query for SSADH deficiency (MIM# 271980) at different levels of confidence A: Low, B: Medium and C: High. All panels are screenshots of the PhenUMA results that were edited to highlight the main clinical features associated with each OMIM disease cluster.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Phenotypically similar disorders associated with SSADH deficiency at different confidence levels. PhenUMA results of the query for SSADH deficiency (MIM# 271980) at different levels of confidence A: Low, B: Medium and C: High. All panels are screenshots of the PhenUMA results that were edited to highlight the main clinical features associated with each OMIM disease cluster.
Mentions: PhenUMA allows users to obtain coherent disease and gene clusters related to a particular disease, gene or set of phenotypes for research purposes. As an example, we will examine succinic semialdehyde dehydrogenase deficiency (SSADHD; MIM# 271980), also known as 4-Hydroxybutyric aciduria, a rare inborn error of metabolism associated with mutations in Locus ALDH5A1 (ALDH5A1; MIM# 610045). We used PhenUMA to search for all of the phenotypic similarities to SSADH deficiency at each of the confidence levels of low, medium and high. These results show how different clusters of diseases are generated and belong to distinguishable groups according to their phenotypic similarity score (Figure 5). For instance, a low cutoff for phenotypic similarity gives four large overlapped and densely interconnected clusters of disorders associated with epilepsy, seizures, neurodegenerative processes, neurophysiological abnormalities and behavioral problems (Figure 5A). SSADH deficiency has a higher frequency of connections to the disorders that involve convulsions, epilepsy or changes in behavior, and the connection becomes more evident when we increase the similarity score to the medium level of significance (Figure 5B). In this case, the established clusters have a more clearly defined structure and relationships to SSADH deficiency. Indeed, three non-overlapped clusters are apparent in Figure 5B. However, although the phenotypic coherence increased, the interconnections between clusters (OMIM diseases) remained abundant in the resulting network (Figure 5B). Therefore, we constrained the query to the most significant phenotypic similarities for SSADH deficiency by selecting the “high confidence” option in PhenUMA.Figure 5

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