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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05

AUTHORS

Jessica J. Y. Lee, Michael M. Gottlieb, Jake Lever, Steven J. M. Jones, Nenad Blau, Clara D. M. van Karnebeek, Wyeth W. Wasserman

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. More... »

PAGES

555-562

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10545-017-0125-4

DOI

http://dx.doi.org/10.1007/s10545-017-0125-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100424605

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29340838


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