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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

Effects of phenotypic similarity cutoff variations on the number of elements and Jaccard coefficients. Computed phenotypic similarities for gene pairs (blue squares), OMIM disease pairs (red circles) and Orphanet disease pairs (green triangles) were filtered at the 95th percentile, and different cutoff scores corresponding to the 95th, 98th, 99th and 99.5th percentiles were used. The Resnik and Robinson measurements are shown as solid and dashed lines, respectively. A: Variations in the number of genes and diseases that are involved in phenotypic similarities at increasing values of the similarity score. B: Variations of the Jaccard’s similarity coefficients calculated from the resulting intersection between the phenotypic similarity-based networks and their respective inferred networks is represented as the distinct similarity scores.
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Fig2: Effects of phenotypic similarity cutoff variations on the number of elements and Jaccard coefficients. Computed phenotypic similarities for gene pairs (blue squares), OMIM disease pairs (red circles) and Orphanet disease pairs (green triangles) were filtered at the 95th percentile, and different cutoff scores corresponding to the 95th, 98th, 99th and 99.5th percentiles were used. The Resnik and Robinson measurements are shown as solid and dashed lines, respectively. A: Variations in the number of genes and diseases that are involved in phenotypic similarities at increasing values of the similarity score. B: Variations of the Jaccard’s similarity coefficients calculated from the resulting intersection between the phenotypic similarity-based networks and their respective inferred networks is represented as the distinct similarity scores.

Mentions: Initially, we built a binary classifier system that compares all of the computed scores between semantically similar genes or disease pairs with their respective reference datasets. However, the estimated thresholds in each ROC curve were meaningful (Additional file 1), but they are impractical as optimal cutoffs because of the large size of the resulting networks. Therefore, we analyzed cutoff variations in the phenotypic similarity datasets using a similar approach as in one of our recent studies [13]. First, we removed all pairs of genes or diseases that had a similarity score below the 95th percentile. Next, we studied both the influence of cutoff variations on the number of gene or disease entries and the resulting Jaccard’s similarity coefficients when comparing the semantic similarity networks to their respective reference datasets network (Figure 2). More specifically, the Jaccard’s similarity coefficient represents the number of intersected pairs of gene or disease entries divided by the number of pairs of entries in the union.Figure 2


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)

Effects of phenotypic similarity cutoff variations on the number of elements and Jaccard coefficients. Computed phenotypic similarities for gene pairs (blue squares), OMIM disease pairs (red circles) and Orphanet disease pairs (green triangles) were filtered at the 95th percentile, and different cutoff scores corresponding to the 95th, 98th, 99th and 99.5th percentiles were used. The Resnik and Robinson measurements are shown as solid and dashed lines, respectively. A: Variations in the number of genes and diseases that are involved in phenotypic similarities at increasing values of the similarity score. B: Variations of the Jaccard’s similarity coefficients calculated from the resulting intersection between the phenotypic similarity-based networks and their respective inferred networks is represented as the distinct similarity scores.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Effects of phenotypic similarity cutoff variations on the number of elements and Jaccard coefficients. Computed phenotypic similarities for gene pairs (blue squares), OMIM disease pairs (red circles) and Orphanet disease pairs (green triangles) were filtered at the 95th percentile, and different cutoff scores corresponding to the 95th, 98th, 99th and 99.5th percentiles were used. The Resnik and Robinson measurements are shown as solid and dashed lines, respectively. A: Variations in the number of genes and diseases that are involved in phenotypic similarities at increasing values of the similarity score. B: Variations of the Jaccard’s similarity coefficients calculated from the resulting intersection between the phenotypic similarity-based networks and their respective inferred networks is represented as the distinct similarity scores.
Mentions: Initially, we built a binary classifier system that compares all of the computed scores between semantically similar genes or disease pairs with their respective reference datasets. However, the estimated thresholds in each ROC curve were meaningful (Additional file 1), but they are impractical as optimal cutoffs because of the large size of the resulting networks. Therefore, we analyzed cutoff variations in the phenotypic similarity datasets using a similar approach as in one of our recent studies [13]. First, we removed all pairs of genes or diseases that had a similarity score below the 95th percentile. Next, we studied both the influence of cutoff variations on the number of gene or disease entries and the resulting Jaccard’s similarity coefficients when comparing the semantic similarity networks to their respective reference datasets network (Figure 2). More specifically, the Jaccard’s similarity coefficient represents the number of intersected pairs of gene or disease entries divided by the number of pairs of entries in the union.Figure 2

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