Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

• Published in: Briefings in bioinformatics Vol. 22; no. 4
• Main Authors: Wei, Leyi, He, Wenjia, Malik, Adeel, Su, Ran, Cui, Lizhen, Manavalan, Balachandran
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.07.2021; Oxford Publishing Limited (England)
• Subjects: Algorithms; Cell culture; Computer applications; Deoxyribonucleic acid; DNA; DNA biosynthesis; Eukaryotes; Learning algorithms; Machine learning; Nucleic acids; Physicochemical properties; Prediction models; Replication; Replication origins; Training; model interpretability; eXtreme Gradient Boosting; feature extraction; stacking strategy; origin of replication site
• DOI: 10.1093/bib/bbaa275
• ISSN: 1467-5463; 1477-4054

Abstract Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs’ distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.