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J Inherit Metab Dis. 2018 May;41(3):555-562. doi: 10.1007/s10545-017-0125-4. Epub 2018 Jan 16.

Text-based phenotypic profiles incorporating biochemical phenotypes of inborn errors of metabolism improve phenomics-based diagnosis.

Author information

1
Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Room 3109, 950 West 28th Avenue, Vancouver, BC, V5Z 4H4, Canada.
2
Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada.
3
Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
4
Dietmar-Hopp Metabolic Center, Department of General Pediatrics, University Hospital, Heidelberg, Germany.
5
Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
6
Departments of Pediatrics and Clinical Genetics, Emma Children's Hospital, Academic Medical Centre, Amsterdam, The Netherlands.
7
Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Room 3109, 950 West 28th Avenue, Vancouver, BC, V5Z 4H4, Canada. wyeth@cmmt.ubc.ca.
8
Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada. wyeth@cmmt.ubc.ca.

Abstract

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.

KEYWORDS:

Biochemical phenotypes; Clinical informatics; Data mining; Inborn errors of metabolism; Metabolic phenotypes; Text-based phenomics

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