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

BMC Bioinformatics

Pasan C. Fernando¹  Paula M. Mabee²  Erliang Zeng³ 

[1] Department of Biology, University of South Dakota, Vermillion, SD, USA;Department of Biology, University of South Dakota, Vermillion, SD, USA;National Ecological Observatory Network, Battelle Memorial Institute, 1685 38th St., Suite 100, 80301, Boulder, CO, USA;Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, USA;Department of Preventive and Community Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, USA;Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA;Department of Biomedical Engineering, College of Engineering, University of Iowa, Iowa City, IA, USA;

关键词: Anatomy ontology;    Uberon;    Phenotype;    Protein–protein interaction networks;    Big data;    Data integration;    Candidate gene prediction;    Data quality;    Semantic similarity;   

DOI  :  10.1186/s12859-020-03773-2

来源: Springer

【 摘 要 】

BackgroundIdentification 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.ResultsAccording 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.ConclusionIntegration 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.

【 授权许可】

CC BY