Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery.

While abnormalities related to carbohydrates (glycans) are frequent for
patients with rare and undiagnosed diseases as well as in many common
diseases, these glycan-related phenotypes (glycophenotypes) are not well
represented in knowledge bases (KBs). If glycan-related diseases were more
robustly represented and curated with glycophenotypes, these could be used
for molecular phenotyping to help to realize the goals of precision
medicine. Diagnosis of rare diseases by computational cross-species
comparison of genotype-phenotype data has been facilitated by leveraging
ontological representations of clinical phenotypes, using Human Phenotype
Ontology (HPO), and model organism ontologies such as Mammalian Phenotype
Ontology (MP) in the context of the Monarch Initiative. In this article, we
discuss the importance and complexity of glycobiology and review the
structure of glycan-related content from existing KBs and biological
ontologies. We show how semantically structuring knowledge about the
annotation of glycophenotypes could enhance disease diagnosis, and propose
a solution to integrate glycophenotypes and related diseases into the
Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We
encourage the community to practice good identifier hygiene for glycans in
support of semantic analysis, and clinicians to add glycomics to their
diagnostic analyses of rare diseases.