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2018 ; 41
(3
): 555-562
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Text-based phenotypic profiles incorporating biochemical phenotypes of inborn
errors of metabolism improve phenomics-based diagnosis
#MMPMID29340838
Lee JJY
; Gottlieb MM
; Lever J
; Jones SJM
; Blau N
; van Karnebeek CDM
; Wasserman WW
J Inherit Metab Dis
2018[May]; 41
(3
): 555-562
PMID29340838
show ga
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.