Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery

Jean Philippe F. Gourdine, Matthew H. Brush, Nicole A. Vasilevsky, Kent Shefchek, Sebastian Köhler, Nicolas Matentzoglu, Monica C. Munoz-Torres, Julie A. McMurry, Xingmin Aaron Zhang, Peter N. Robinson, Melissa A. Haendel

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalDatabase : the journal of biological databases and curation
Volume2019
DOIs
StatePublished - Jan 1 2019

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ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Gourdine, J. P. F., Brush, M. H., Vasilevsky, N. A., Shefchek, K., Köhler, S., Matentzoglu, N., Munoz-Torres, M. C., McMurry, J. A., Zhang, X. A., Robinson, P. N., & Haendel, M. A. (2019). Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery. Database : the journal of biological databases and curation, 2019. https://doi.org/10.1093/database/baz114