ZFIN ID: ZDB-PUB-201009-2
Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities
Fernando, P.C., Mabee, P.M., Zeng, E.
Date: 2020
Source: BMC Bioinformatics   21: 442 (Journal)
Registered Authors: Mabee, Paula M.
Keywords: Anatomy ontology, Big data, Candidate gene prediction, Data integration, Data quality, Phenotype, Protein–protein interaction networks, Semantic similarity, Uberon
MeSH Terms:
  • Animals
  • Area Under Curve
  • Databases, Protein
  • Gene Regulatory Networks
  • Mice
  • Phenotype
  • Protein Interaction Mapping/methods*
  • Protein Interaction Maps*
  • ROC Curve
  • User-Computer Interface
  • Zebrafish/metabolism
PubMed: 33028186 Full text @ BMC Bioinformatics
Identification 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. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were 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 compared the performance of their network-based candidate gene predictions.
According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse.
Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.