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Nucleic Acids Res. 2016 Feb 29;44(4):e32. doi: 10.1093/nar/gkv1025. Epub 2015 Oct 13.

A deep learning framework for modeling structural features of RNA-binding protein targets.

Author information

1
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
2
Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China.
3
School of Life Sciences, Tsinghua University, Beijing 100084, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China.
4
Department of Genetics, Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA chao.cheng@dartmouth.edu.
5
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China zengjy321@tsinghua.edu.cn.

Abstract

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp.

PMID:
26467480
PMCID:
PMC4770198
DOI:
10.1093/nar/gkv1025
[Indexed for MEDLINE]
Free PMC Article

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