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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

ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. A: ROC curves for phenotypic similarities between OMIM diseases. For all the cases we used the same reference dataset. This dataset are all inferred OMIM disease pairs that are those diseases associated with the same gene/s. It is noteworthy that the results from Robinson and Resnik are equivalent to those in Additional file 1: Figure S1A and S1B, respectively, B: FDR for increasing values of phenotypic similarity scores.
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Fig6: ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. A: ROC curves for phenotypic similarities between OMIM diseases. For all the cases we used the same reference dataset. This dataset are all inferred OMIM disease pairs that are those diseases associated with the same gene/s. It is noteworthy that the results from Robinson and Resnik are equivalent to those in Additional file 1: Figure S1A and S1B, respectively, B: FDR for increasing values of phenotypic similarity scores.

Mentions: PhenomeNET and Phenomizer are the most comparable to PhenUMA. Therefore, a more systematic comparison was performed between the results of PhenUMA and PhenomeNET. To do so, we downloaded the file “borderflow-0.1”, which contains relationships and similarity scores between the phenotypes of several species, such as worm, fly, rat, mouse, zebra fish and human, from the PhenomeNET website. Given this cross-species phenotype network, we selected only OMIM disease pairs. A ROC curve was built using the same reference set of inferred relationships between OMIM diseases that share one or several genes. The resulting ROC curves from Resnik’s and Robinson’s measures give better results than those provided by PhenomeNET (Figure 6A). We analyzed the fraction of expected false discoveries by calculating the false discovery rate for each system (Figure 6B). In this case, we observed a lower false discovery rate for PhenUMA, which uses the Robinson’s measure, compared to the similarity score computed using PhenomeNET (Figure 6B). However, PhenomeNET gives a lower fraction of expected false positives than the classical Resnik’s measure.Figure 6


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)

ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. A: ROC curves for phenotypic similarities between OMIM diseases. For all the cases we used the same reference dataset. This dataset are all inferred OMIM disease pairs that are those diseases associated with the same gene/s. It is noteworthy that the results from Robinson and Resnik are equivalent to those in Additional file 1: Figure S1A and S1B, respectively, B: FDR for increasing values of phenotypic similarity scores.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. A: ROC curves for phenotypic similarities between OMIM diseases. For all the cases we used the same reference dataset. This dataset are all inferred OMIM disease pairs that are those diseases associated with the same gene/s. It is noteworthy that the results from Robinson and Resnik are equivalent to those in Additional file 1: Figure S1A and S1B, respectively, B: FDR for increasing values of phenotypic similarity scores.
Mentions: PhenomeNET and Phenomizer are the most comparable to PhenUMA. Therefore, a more systematic comparison was performed between the results of PhenUMA and PhenomeNET. To do so, we downloaded the file “borderflow-0.1”, which contains relationships and similarity scores between the phenotypes of several species, such as worm, fly, rat, mouse, zebra fish and human, from the PhenomeNET website. Given this cross-species phenotype network, we selected only OMIM disease pairs. A ROC curve was built using the same reference set of inferred relationships between OMIM diseases that share one or several genes. The resulting ROC curves from Resnik’s and Robinson’s measures give better results than those provided by PhenomeNET (Figure 6A). We analyzed the fraction of expected false discoveries by calculating the false discovery rate for each system (Figure 6B). In this case, we observed a lower false discovery rate for PhenUMA, which uses the Robinson’s measure, compared to the similarity score computed using PhenomeNET (Figure 6B). However, PhenomeNET gives a lower fraction of expected false positives than the classical Resnik’s measure.Figure 6

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