Text-based phenotypic profiles incorporating biochemical phenotypes of inborn errors of metabolism improve phenomics-based diagnosis
| Autor: | Clara D.M. van Karnebeek, Michael Gottlieb, Wyeth W. Wasserman, Nenad Blau, Jessica J. Y. Lee, Steven J.M. Jones, Jake Lever |
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| Přispěvatelé: | Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Paediatric Metabolic Diseases, University of Zurich, Wasserman, Wyeth W |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
2716 Genetics (clinical) Databases Factual 610 Medicine & health Computational biology Inborn errors of metabolism Biology Pattern Recognition Automated Diagnosis Differential 03 medical and health sciences 0302 clinical medicine Phenomics 1311 Genetics Biochemical phenotypes Research community Human Phenotype Ontology Genetics Humans Data mining Genetics (clinical) Metabolic phenotypes Text-based phenomics Computational Biology Decision Support Systems Clinical Phenotype Clinical informatics Human genetics 3. Good health 030104 developmental biology 10036 Medical Clinic 030217 neurology & neurosurgery Algorithms Biomarkers Metabolism Inborn Errors |
| Zdroj: | Journal of Inherited Metabolic Disease Journal of inherited metabolic disease, 41(3), 555-562. Springer Netherlands |
| ISSN: | 1573-2665 0141-8955 |
| Popis: | 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. Electronic supplementary material The online version of this article (10.1007/s10545-017-0125-4) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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