Advances in Gene Ontology Utilization Improve Statistical Power of Annotation Enrichment

Abstract

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in these gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through the ontology through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission lest erroneous term mappings occur. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods.\n\nWe demonstrate that GOcats unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value=1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcats path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.