BeRBP: binding estimation for human RNA-binding proteins. Issue 5 (27th December 2018)

• Record Type: Journal Article
• Authors: Yu, Hui; Wang, Jing; Sheng, Quanhu; Liu, Qi; Shyr, Yu
• Is Part Of: Nucleic acids research. Volume 47:Issue 5(2019)
• Page Start: e26
• Page End: e26
• Publication Date: 2018-12-27
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky1294
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Identifying binding targets of RNA-binding proteins (RBPs) can greatly facilitate our understanding of their functional mechanisms. Most computational methods employ machine learning to train classifiers on either RBP-specific targets or pooled RBP–RNA interactions. The former strategy is more powerful, but it only applies to a few RBPs with a large number of known targets; conversely, the latter strategy sacrifices prediction accuracy for a wider application, since specific interaction features are inevitably obscured through pooling heterogeneous datasets. Here, we present beRBP, a dual approach to predict human RBP–RNA interaction given PWM of a RBP and one RNA sequence. Based on Random Forests, beRBP not only builds a specific model for each RBP with a decent number of known targets, but also develops a general model for RBPs with limited or null known targets. The specific and general models both compared well with existing methods on three benchmark datasets. Notably, the general model achieved a better performance than existing methods on most novel RBPs. Overall, as a composite solution overarching the RBP-specific and RBP-General strategies, beRBP is a promising tool for human RBP binding estimation with good prediction accuracy and a broad application scope.