Ontology-based validation and identification of regulatory phenotypes
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
KAUST Grant NumberURF/1/3454-01-01
MetadataShow full item record
AbstractMotivation: Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations Results: We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.
CitationKulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2018) Ontology-based validation and identification of regulatory phenotypes. Available: http://dx.doi.org/10.1101/256529.
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and FCC/1/1976-08-01.
PublisherCold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.