PhenUMA: a tool for integrating the biomedical relationships among genes and diseases

López, Rocío Rodríguez; Reyes-Palomares, Armando; Sánchez‐Jiménez, Francisca; Medina, Miguel Ángel

doi:10.1186/s12859-014-0375-1

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…One of the useful databases is PhenUMA (www.phenuma.uma.es). 18 PhenUMA is a great tool to identify pathological relationships based on functional and phenotypic. With this PhenUMA database, a list of DDAs with their OMIM ids were obtained and used for evaluating our DDAs.…”

Section: Methodsmentioning

confidence: 99%

DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

Suratanee

Plaimas

2015

Bioinform Biol Insights

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Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease–disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein–protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease–disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

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Section: Methodsmentioning

confidence: 99%

DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

Suratanee

Plaimas

2015

Bioinform Biol Insights

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“…To help with the characterization of molecular relationships between different phenotypes and microvariants, we aimed to apply principles of network medicine [ 14 – 18 ] to find the consequences of variants and their association with diseases. To this end, we focused on the development of a computational approach via tripartite networks made of three types of nodes: variants (CNVs), patients, and phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases

et al. 2018

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Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype–phenotype relationships. We aimed to improve the identification of statistically consistent genotype–phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype–patient–genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.

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“…Goh et al used a bipartite graph consisting of diseases and genes, using it to connect diseases that share common genetic components [12]. Further studies have built on this work, and it is clear that similar diseases in terms of pathophysiology tend to group together in such networks [13,14]. As well as diseases, there has been increasing interest in their underlying phenotypes and the connections between them.…”

Section: Introductionmentioning

confidence: 99%

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

et al. 2020

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Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.

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PhenUMA: a tool for integrating the biomedical relationships among genes and diseases

Cited by 9 publications

References 26 publications

DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

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