RT Journal Article
SR Electronic
T1 Ontology-based validation and identification of regulatory phenotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 256529
DO 10.1101/256529
A1 Maxat Kulmanov
A1 Paul N Schofield
A1 Georgios V Gkoutos
A1 Robert Hoehndorf
YR 2018
UL http://biorxiv.org/content/early/2018/01/30/256529.abstract
AB Motivation 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.Availability https://github.com/bio-ontology-research-group/phenogoconContact robert.hoehndorf{at}kaust.edu.sa