mGDisAI: N7-methylguanosine (mG) sites and diseases associations inference based on heterogeneous network.

BACKGROUND

Recent studies have confirmed that N7-methylguanosine (mG) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated mG sites. To date, tens of thousands of mG sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated mG sites using a computational perspective. Thus, computational methods for mG-disease association prediction are urgently needed, but none are currently available at present.

RESULTS

To fill this gap, we collected association information between mG sites and diseases, genomic information of mG sites, and phenotypic information of diseases from different databases to build an mG-disease association dataset. To infer potential disease-associated mG sites, we then proposed a heterogeneous network-based model, mG Sites and Diseases Associations Inference (mGDisAI) model. mGDisAI predicts the potential disease-associated mG sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of mG sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and mGDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model's accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related mG sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of mG sites and GO terms. The results showed that mGDisAI identified disease-associated mG sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases.

CONCLUSION

The mGDisAI web server can be accessed at http://180.208.58.66/m7GDisAI/ , which provides a user-friendly interface to query disease associated mG. The list of top 20 mG sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific mG sites and diseases are also shown.