Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level

Mannil, Deepthi; Vogt, Ingo; Prinz, Jeanette; Campillos, Mónica

doi:10.1093/nar/gku948

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…We only kept therapeutic indications reported by more than one source. The data of Adverse Drug Reaction (ADRs) was retrieved from 3 sources: Offsides ( Tatonetti et al, 2012 ), AEOLUS, and ORGANDB ( Mannil et al, 2015 ) (all files were downloaded on September, 2017). As we did with drug therapeutic indication data, we used UMLS CUIs to harmonize phenotypes and DrugBank identifiers to represent drugs.…”

Section: Methodsmentioning

confidence: 99%

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

et al. 2018

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Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

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Section: Methodsmentioning

confidence: 99%

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

et al. 2018

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“…DisGeNET platform has been used to study a variety of biomedical problems, which include investigating the molecular basis of specific diseases (33–36), annotating lists of genes produced by different types of omics and sequencing protocols (37–39), validating disease genes prediction methods (40–42), understanding disease mechanisms in the context of protein networks (43,44), gaining insight into drug action (45) and drug adverse reactions mechanisms (46), drug repurposing (47), exploring the molecular basis of disease comorbidities (48,49), assessing the performance of text-mining algorithms (50) and as part of other resources (51–53). …”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Piñero¹,

Bravo²,

et al. 2016

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The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

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“…‘eye’). Tools such as OMIM ( 10 ), Organ System Heterogeneity DB ( 9 ) and BRITE ( 11 ) do offer some direct links between genes and organs, but include only a limited number of organs and systems (33 in OMIM, 26 in Organ System Heterogeneity DB, 12 in BRITE), and lack platforms to efficiently and statistically analyze these data.…”

Section: Introductionmentioning

confidence: 99%

Gene ORGANizer: linking genes to the organs they affect

Gokhman

Kelman

Amartely

et al. 2017

Nucleic Acids Research

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One of the biggest challenges in studying how genes work is understanding their effect on the physiology and anatomy of the body. Existing tools try to address this using indirect features, such as expression levels and biochemical pathways. Here, we present Gene ORGANizer (geneorganizer.huji.ac.il), a phenotype-based tool that directly links human genes to the body parts they affect. It is built upon an exhaustive curated database that links >7000 genes to ∼150 anatomical parts using >150 000 gene-organ associations. The tool offers user-friendly platforms to analyze the anatomical effects of individual genes, and identify trends within groups of genes. We demonstrate how Gene ORGANizer can be used to make new discoveries, showing that chromosome X is enriched with genes affecting facial features, that positive selection targets genes with more constrained phenotypic effects, and more. We expect Gene ORGANizer to be useful in a variety of evolutionary, medical and molecular studies aimed at understanding the phenotypic effects of genes.

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Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level

Cited by 9 publications

References 31 publications

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Gene ORGANizer: linking genes to the organs they affect

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