Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Abstract

AbstractBackgroundIdentification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet-lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. This is because PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes for anatomical entities. We developed an integrative framework to predict candidate genes for anatomical entities by combining existing experimental knowledge about gene-anatomy relationships with PPI networks using anatomy ontology annotations. We expected this integration to improve the quality of the PPI networks and be better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomy entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These ‘anatomy-based gene networks’ are semantic networks, as they are constructed based on the Uberon anatomy ontology annotations that are obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database, and we compared the performance of their network-based candidate gene predictions.ResultsAccording to candidate gene prediction performance evaluations tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks showed better receiver operating characteristic (ROC) and precision-recall curve performances than PPI networks for both zebrafish and mouse.ConclusionIntegration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improves the network quality, which makes them better optimized for predicting candidate genes for anatomical entities.