Dataset Information

Identification of RNA-binding protein targets with HyperTRIBE.

ABSTRACT: RNA-binding proteins (RBPs) accompany RNA from birth to death, affecting RNA biogenesis and functions. Identifying RBP-RNA interactions is essential to understanding their complex roles in different cellular processes. However, detecting in vivo RNA targets of RBPs, especially in a small number of discrete cells, has been a technically challenging task. We previously developed a novel technique called TRIBE (targets of RNA-binding proteins identified by editing) to overcome this problem. TRIBE expresses a fusion protein consisting of a queried RBP and the catalytic domain of the RNA-editing enzyme ADAR (adenosine deaminase acting on RNA) (ADARcd), which marks target RNA transcripts by converting adenosine to inosine near the RBP binding sites. These marks can be subsequently identified via high-throughput sequencing. In spite of its usefulness, TRIBE is constrained by a low editing efficiency and editing-sequence bias from the ADARcd. Therefore, we developed HyperTRIBE by incorporating a previously characterized hyperactive mutation, E488Q, into the ADARcd. This strategy increases the editing efficiency and reduces sequence bias, which markedly increases the sensitivity of this technique without sacrificing specificity. HyperTRIBE provides a more powerful strategy for identifying RNA targets of RBPs with an easy experimental and computational protocol at low cost, that can be performed not only in flies, but also in mammals. The HyperTRIBE experimental protocol described below can be carried out in cultured Drosophila S2 cells in 1 week, using tools available in a common molecular biology laboratory; the computational analysis requires 3 more days.

SUBMITTER: Rahman R

PROVIDER: S-EPMC6349038 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of RNA-binding protein targets with HyperTRIBE.

Rahman Reazur R Xu Weijin W Jin Hua H Rosbash Michael M

Nature protocols 20180801 8

RNA-binding proteins (RBPs) accompany RNA from birth to death, affecting RNA biogenesis and functions. Identifying RBP-RNA interactions is essential to understanding their complex roles in different cellular processes. However, detecting in vivo RNA targets of RBPs, especially in a small number of discrete cells, has been a technically challenging task. We previously developed a novel technique called TRIBE (targets of RNA-binding proteins identified by editing) to overcome this problem. TRIBE e ...[more]

PMID: 30013039

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Project description: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.

Dataset Information

Identification of RNA-binding protein targets with HyperTRIBE.

Publications

Identification of RNA-binding protein targets with HyperTRIBE.

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