ncRNAP: De novo prediction of RNA-protein interactions from sequence information

  • Last updated: Sept. 14, 2012


Welcome to our website! This is the supplementary materials webpage for our paper "De novo prediction of non-coding RNA-protein interactions".

In this paper, we combined bioinformatics and experimental approach to de novo inferring RNA-protein interactions, especially ncRNA-protein, and further conducted biological experiments to validate its effectiveness. We predicted protein-RNA interactions by an extended naïve-Bayes-classifier. Our predictor achieved high prediction accuracy. In particular, we demonstrate that the proposed method performs superior to the other machine-learning-based models for predicting protein-RNA interaction. We also conducted ncRNA pull-down experiments and identified interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrate the effectiveness of our proposed method.

Our method is particular suitable for studying function roles for long non-coding RNA (lncRNAs), a novel class of RNA molecules, that exhibit rising interest within the research community in recent years. You can find the supporting materials or other resources which referred in the paper. Pay attention to the users: You can use and redistribute the data and code if you accept GNU General Public License (GPL).

Any questions, please contact Dr. Zhi-Ping Liu by


  • Ying Wang, Xiaowei Chen, Zhi-Ping Liu, Qiang Huang, Yong Wang, Xiang-Sun Zhang, Runsheng Chen, and Luonan Chen: De novo prediction of RNA-protein interactions from sequence information. 9(1):133-142, 2013.

Supplementary Webpage:#

Category: Supplementary

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