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Phenomics is the comprehensive study of phenotypes at every level of biology: from metabolites to organisms. With high throughput technologies increasing the scope of biological discoveries, the field of phenomics has been developing rapid and precise methods to collect, catalog, and analyze phenotypes. Such methods have allowed phenotypic data to be widely used in medical applications, from assisting clinical diagnoses to prioritizing genomic diagnoses. To channel the benefits of phenomics into the field of inborn errors of metabolism (IEM), we have recently launched IEMbase, an expert-curated knowledgebase of IEM and their disease-characterizing phenotypes. While our efforts with IEMbase have realized benefits, taking full advantage of phenomics requires a comprehensive curation of IEM phenotypes in core phenomics projects, which is dependent upon contributions from the IEM clinical and research community. Here, we assess the inclusion of IEM biochemical phenotypes in a core phenomics project, the Human Phenotype Ontology. We then demonstrate the utility of biochemical phenotypes using a text-based phenomics method to predict gene-disease relationships, showing that the prediction of IEM genes is significantly better using biochemical rather than clinical profiles. The findings herein provide a motivating goal for the IEM community to expand the computationally accessible descriptions of biochemical phenotypes associated with IEM in phenomics resources.
Pubmed ID: 29340838 RIS Download
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A collaborative care and research initiative with a focus on prevention and treatment of Intellectual disability (ID) that is due to inborn errors of metabolism (IEM), which can be treated with diet or drugs. Health care policy and institutional culture is still operating under the old premise that all ID is incurable and thus, many children born with treatable ID are at risk of not being treated. To acknowledge the multidisciplinary scope and the ways in which health care professionals and researchers will collaborate, the goals of the TIDE BC project are demonstrated within a framework of 7 Work Packages: * Implementation of a new Protocol for diagnostic evaluation of ID, focusing of treatable conditions; * Development of infrastructure to facilitate implementation, evaluation and sustainability of the Protocol; * Investments into next generation genomic technologies; * Improving evidence of and access to treatments; * Evaluation and health economy; * Knowledge dissemination; * Education and Mentoring. The objectives addressed in all Work Packages reflect a highly integrated cluster combining clinical care, research, evaluation, and knowledge dissemination.
View all literature mentionsThe Disease Ontology group has developed a set of standard representations of phenotypes associated with diseases useful in bioinformatics applications. These are formalized into an ontological structure and are encoded in OWL. Neurodegenerative diseases have a wide and complex range of biological and clinical symptoms. While neurodegenerative diseases share many pathological features in common, they also contain unique signatures. Animal models of these disorders are key to translational research. However, animal models typically replicate only a subset of disease features or display features that are only indirectly related to a given disorder, whose relationship to the human condition may be across several diseases. Matching animal models to human diseases is therefore a significant informatics challenge. We have been working to develop ontologies that capture essential features of neurodegenerative diseases and associated animal models in a way that allows more flexible matching of animal models to human disorders and in a way that makes explicit commonalities and differences among animal models and human neurodegenerative disease. Creating ontologies for diseases and disorders is a very challenging task (Gupta et al., 2003) because of the complexity of the disorders and because of the limitations of current ontology formalisms. In order to simplify the approach and make it practical for use in information systems, we have focused on formal descriptions of phenotypes associated with diseases and animal models rather than on a formal model of the disease process itself. We employ the modular ontologies developed as part of the Neuroscience Information Framework (NIF: http://nif.nih.gov) and the Phenotype and Trait Ontology (PATO), an ontology of qualities associated with biological phenotypes, to create a flexible template for creating phenotypic statements at the class and instance levels. We show how these phenotypes can be used to look for commonalities across multiple neurodegenerative conditions and animal models.
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