Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.
Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.
复制标题
使用深度卷积和循环神经网络预测 RNA-蛋白质序列和结构结合偏好
DOI：
10.1186/s12864-018-4889-1
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发表时间：
2018-07-03
期刊：
影响因子：
4.4
通讯作者：
Shen HB
中科院分区：
文献类型：
--
作者：
Pan X;Rijnbeek P;Yan J;Shen HB
背景RNA的调控主要依赖于它的结合蛋白，即RNA结合蛋白（RBP）。不幸的是，大多数限制性商业惯例的结合偏好仍然没有得到很好的表征。序列和二级结构特异性之间的相互依赖性是具有挑战性的预测RBP结合位点和准确的序列和结构基序detection.ResultsIn这项研究中，我们提出了一种基于深度学习的方法，iDeepS，同时识别结合序列和结构基序从RNA序列使用卷积神经网络（CNN）和双向长短期记忆网络（BLSTM）。我们首先对序列和预测的二级结构进行独热编码，以实现后续的卷积操作。为了从观察到的序列中揭示隐藏的绑定知识，CNN被应用于学习抽象特征。考虑到序列和预测结构之间的密切关系，我们使用BLSTM来捕获由CNN识别的结合序列和结构基序之间可能的长程依赖关系。最后，将学习的加权表示馈送到分类层中以预测RBP结合位点。我们在来自大规模代表性CLIP-seq数据集的经验证的RBP结合位点上评估了iDeepS。结果表明，iDeepS可以可靠地预测RNA上的RBP结合位点，并且优于最先进的方法。与其他方法相比，iDeepS的一个重要优势是可以自动提取结合序列和结构基序，这将提高我们对RBP结合特异性机制的理解。ConclusionOur study表明，iDeepS方法可以识别序列和结构基序，从而准确预测RBP结合位点。iDeepS可在 https://github.com/xypan1232/iDeepS .
BackgroundRNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection.ResultsIn this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs.ConclusionOur study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS .
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影响因子：
12.3
作者：
Fukunaga T;Ozaki H;Terai G;Asai K;Iwasaki W;Kiryu H
通讯作者：
Kiryu H
DOI：
10.1093/bioinformatics/btt495
发表时间：
2013-11-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Agostini F;Zanzoni A;Klus P;Marchese D;Cirillo D;Tartaglia GG
通讯作者：
Tartaglia GG
影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者：
Tuschl T
模因套件：用于发现和搜索的工具。
影响因子：
14.9
作者：
Bailey TL;Boden M;Buske FA;Frith M;Grant CE;Clementi L;Ren J;Li WW;Noble WS
通讯作者：
Noble WS
使用RNA二级结构指导序列基序发现对单链区域。
影响因子：
14.9
作者：
Hiller M;Pudimat R;Busch A;Backofen R
通讯作者：
Backofen R