BeRBP: Binding estimation for human RNA-binding proteins

Hui Yu, Jing Wang, Quanhu Sheng, Qi Liu, Yu Shyr

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Article numbere26
JournalNucleic acids research
Issue number5
Publication statusPublished - 2019 Mar 18

All Science Journal Classification (ASJC) codes

  • Genetics


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