2017;118-119:7381. Convolution Layer and Max Pooling Layer are designed to automatically process the feature. Arrows show the dependencies between inputs, modes, and outputs. Zhou, J. 2016;32(10):152735. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. Yang, Y., Zhan, J., Zhao, H. & Zhou, Y. As shown in Fig. It also accepts batch input with multiple RNA sequences. did part of computation and data analysis. It may lose the RBPs binding to the non-coding RNA. For example, truepera radiovictrix (proteome id: UP000000379) only contained one reviewed sequence. For general model, RBPsuite will predict binding scores of all available RBPs for the segments of the input sequence, as shown in Fig. 2) We select regions overlapped with reference gene by intersectBed of bedtools [21]. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features. These phenomena imply Deep-RBPPred is not overfitting. Abstract. Zhang, X. Sharan, M., Forstner, K. U., Eulalio, A. Unlike RBPPred, we only employ physicochemical features including hydrophobicity, polarity, normalized van der Waals volume, polarizability, side chains charge and polarity. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. The result shows that the RBPs rate in the bacteria is smaller than the eukaryote proteome. All the activation functions in neurons are ReLU40. Then all the sequences are discarded from testing set if they are in the same cluster with the training sequences. BMC Genomics. CRIP first encodes the sequence into one-hot encoded matrix using a stacked codon-based encoding scheme, then the encoded matrix is fed into a hybrid deep learning architecture with a CNN and a biLSTM to predict RBP binding sites on circRNAs. Methods. 1. Long short-term memory. For linear RNAs, iDeepS in RBPsuite yields an average AUC of 0.781, precision of 0.673, sensitivity of 0.802 and specificity of 0.591 across 154 RBPs on the independent test set. It should be noted that some studies [20] used the intersection of the bed files to obtain a set of most probably peaks. Scientific Reports Wiley Interdiscip Rev RNA. Thanks to the availability of large scale data for RBP binding motifs and in vivo binding sites results in the form of eCLIP experiments, it is now possible to computationally predict RBP binding sites across the whole genome. At last, a 148-dimensional vector is encoded to represent each protein sequence including the properties of hydrophobicity, normalized van der Waals volume, polarity and polarizability, charge and polarity of side chain. In iDeepS, we can extract binding motifs from the learned parameters of the kernels of CNNs. Based on this consideration, we employ the hand-designed features which are proved effective to represent RBPs in RBPPred. arXiv preprint arXiv. Pan X, Rijnbeek P, Yan J, Shen HB. CLIP-seq; RBP binding site prediction; deep learning; eCLIP; recurrent neural networks; visualization. UniProt, C. UniProt: a hub for protein information. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Polishchuk M, Paz I, Yakhini Z, Mandel-Gutfreund Y. SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data. This result implies RBPs may function in more complex cellular processes in eukaryotes. Nature. Understanding the interactions between circRNAs and proteins is helpful for revealing the biological functions of circRNAs ( Du et al., 2017; Zang et al., 2020 ). Mitchell, S. F., Jain, S., She, M. & Parker, R. Global analysis of yeast mRNPs. The current report is on the development of a medium-term multimodal data fusion . Search genes: Advanced Predict Binding Sites from PWMs Scan your sequence: Threshold (between 0 and 1): The scan will return matches that are greater than X% of the maximum score for that PWM. Accessibility (a) Results for the first benchmark set contain 23 CLIP-seq data sets from 20 different RBPs and various CLIP-seq protocols. A deep learning framework for modeling structural features of RNA-binding protein targets. Unauthorized use of these marks is strictly prohibited. BMC Genomics Bioinformatics 22, 16581659, https://doi.org/10.1093/bioinformatics/btl158 (2006). Pan X, Shen HB. Thus, we propose a deep learning based method CRIP for specially predicting RBP-binding sites on circRNAs [14] from sequences alone. RBPsuite first breaks a full-length input sequence into multiple segments of 101 nucleotides without overlap, then outputs the scores between the segments and the chosen RBP. From the biological point of view, the local structure . Also, many computational methods have been proposed to predict interaction of protein with RNA15,16,17,18 and RBPs19,20,21,22,23,24,25. Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning. RNA-binding proteins (RBPs) take over 5-10% of the eukaryotic proteome and play key roles in many biological processes, e.g. The protein feature vector is reshaped to a tensor with shape 820 in order to apply the 2D-convolution function. 2014;158(6):143143. Deep-RBPPred is written in the python, availability as an open source tool at http://www.rnabinding.com/Deep_RBPPred/Deep-RBPPred.html. Users can click the RBP of interest to see the predicted RBP binding sites of this RBP on the input sequence (Fig. which uses only sequence information of circRNAs to predict circRNA-RBP binding sites. and transmitted securely. Literature, The 10-fold cross-validation results for RNAProt models trained with additional features. https://doi.org/10.1038/s41598-018-33654-x, DOI: https://doi.org/10.1038/s41598-018-33654-x. Steffen P, Voss B, Rehmsmeier M, Reeder J, Giegerich R. RNAshapes: an integrated RNA analysis package based on abstract shapes. & Bengio, Y. The Cardiomyocyte RNA-Binding Proteome: Links to Intermediary Metabolism and Heart Disease. According to the potential score estimated by WebCircRNA [28], users can choose the RNA type. Springer: New York; 2014:155. In Table2, Deep-RBPPred-balance achieves MCC values of 0.82 for H. sapiens, 0.69 for S. cerevisiae, 0.80 for A. thaliana, which are both much higher than the balance and imbalance model of SVM. DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning. In total, we obtain verified motifs for 43 RBPs, which are further scanned against the sequence segments using FIMO in MEME suite [23] with p-value <0.01. For non-RBPs, Deep-RBPPred-balance achieves an average SP of 0.86 ((0.87+0.71+1.0)/3), which is lower than average SP of 0.90 ((0.93+0.85+0.92)/3) of Deep-RBPPred-imbalance. We can find that RBPPred and Deep-RBPPred have different performances in S. cerevisiae and A. thaliana proteomes. Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). Results: 3) The gene overlapped regions are extended to 101 nts in upstream and downstream centering at the read peaks, and we got the positive regions of RBPs. See this image and copyright information in PMC. Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A, Drewe P, et al. . Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Castello, A. et al. The WR score of each putative binding site is compared to the mean WR score of its pre-defined genomic region. For eukaryote species, the amount is set to 1/10 of S. cerevisiae. This may lead to a bias result caused by the redundance between training and testing set. Recent research has shown that circRNAs can interact with RNA-binding proteins (RBPs), which is a critical aspect for understanding circRNA functions. Here we just simply describe the generating process. 4b, only two segments are wrongly predicted as AGO2 binding sites, one has a low predicted score below 0.6. In order to train our deep learning model, we employed the training set used in the RBPPred. The prediction performance on the independent test set and a case study both demonstrate the effectiveness of RBPsuite. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. CAS An official website of the United States government. We downloaded peaks of 154 RBPs of K526 and HepG2 through eCLIP-seq from ENCODE corresponding to human genome hg19 version. The RBPsuite accepts RNA sequence as the input and gives the scores of 101nt segments broken from the input RNA sequence. The mRNA-bound proteome of the early fly embryo. (a) Results for the first benchmark set contain 23 CLIP-seq data sets from 20 different RBPs and various CLIP-seq protocols. Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. In this study, we model the prediction of binding sites on RNAs as a sequence labeling problem, and propose a new model called circSLNN to identify the specific location of RBP-binding sites on . Here we use RBPsuite to predict RBP binding sites on full-length RNAs. Bioinformatics 32, i121i127, https://doi.org/10.1093/bioinformatics/btw255 (2016). 7, 8mer MTSs of miRNA families conserved across the vertebrates were collected 47. So, the reviewed proteomes are used in the prediction. Technical note on transcription factor motif discovery from importance scores (TF-MoDISco) version 0.5.1.1; 2018. arXiv preprint , arXiv:1810.04805. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. The result of SPOT-Seq-RNA is not shown here because it has been compared with RBPPred22. Maticzka D, Lange SJ, Costa F, Backofen R. GraphProt: modeling binding preferences of RNA-binding proteins. CAS RNA binding protein (RBP) plays an important role in cellular processes. Nucleic Acids Res 39, 30173025, https://doi.org/10.1093/nar/gkq1266 (2011). Bailey TL, Johnson J, Grant CE, Noble WS. Genome Res 26, 990999, https://doi.org/10.1101/gr.200535.115 (2016). Google Scholar. The performance of the imbalance model of Deep-RBPPred is almost as good as the balance model. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. To validate the effectiveness of the method, we compared its performance on 37 CircRNA-RBP datasets with existing methods. Thus, iDeepS trains a hybrid network with two CNNs and a long-short temporary memory (LSTM) network [9] to infer binding sequences and structure preferences of RBPs [10]. Genome Res 26, 10001009, https://doi.org/10.1101/gr.200386.115 (2016). Thus, it is imperative to develop an easy-to-use webserver to integrate the state-of-the-art prediction methods for predicting RBP binding sites on RNAs and cover as many RBPs as possible. The solvent accessibility is also discarded. iDeepS and CRIP in RBPsuite are implemented under the TensorFlow framework in Python. The loss is defined as the sum of L2 regularization loss and the cross entropy (see text). Computational prediction of associations between long non-coding RNAs and proteins. The 10-fold cross-validation results for GraphProt, DeepCLIP, and RNAProt. CAS The sequence is included for reference, using only sequence information for training. In this architecture, the number of trainable variable is 480,930. (b) Results for the second benchmark set contain 30 eCLIP data sets from 30 different RBPs. The first solution is to pad all the sequences to fixed length sequences, and then one-hot encoding is used to encode the sequences. Then the feature tensor is flatted to a 640-dimensional vector. Genes-Basel. RBPPred correctly predicts 24 of 130 RBPs, while Deep-RBPPred-imbalance and Deep-RBPPred-balance correctly predict 63 and 92 RBPs respectively. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Identifying RBP Targets with RIP-seq Methods Mol Biol. The . Kumar, M., Gromiha, M. M. & Raghava, G. P. SVM based prediction of RNA-binding proteins using binding residues and evolutionary information. statement and As shown, the SVM-imbalance model achieves MCC values of 0.50, 0.60 and 0.47 for S. cerevisiae, H. sapiens and A. thaliana. Cite this article. For testing our deep learning model, we used the testing set from RBPPred22. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Thus, we add structure context into the motif identification to develop an updated iDeepS using an extended alphabet as used in pysster [11]. Here RNAshapes [24] is used to predict the abstract secondary structures from RNA sequences. The yellow boxes mark necessary framework inputs, the, The 10-fold cross-validation results for, The 10-fold cross-validation results for GraphProt, DeepCLIP, and RNAProt. Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For longer sequences, iDeepS for linear RNAs takes longer time than CRIP for circRNAs since it first needs run the structure prediction. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. Proteins 80, 20802088, https://doi.org/10.1002/prot.24100 (2012). Google Scholar. 2004;306(5696):63640. LeCun, Y., Bottou, L., Bengio, Y. DeepCLIP applies a similar network architecture consisting of a hybrid CNN and LSTM to predict RBP binding sites on RNAs [12] and the network architecture is similar to iDeepS. The frontend of RBPsuite webserver uses JQuery framework of JavaScript and Ajax technology to implement asynchronous loading. Training times are in minutes (averaged over 3 runs) for training a single model with 10,000 instances (81nt) for GraphProt, RNAProt, and DeepCLIP. Article omiXcore [15] and SMARTIV [16, 17]. A disadvantage of Deepnet-rbp is that it requires complicated steps to estimate the binding preference [ ]. RNA-binding proteins (RBPs) are involved in many biological processes, their binding sites on RNAs can give insights into mechanisms behind diseases involving RBPs [1]. To improve the recognition rate of RBP binding sites and reduce the experimental time and cost, many calculation methods based on domain knowledge to predict RBP binding sites have emerged. . For balance set, the highest average MCC is 0.74 and the highest standard error is 0.15. Model training time comparison. In order to get the best prediction performance, the balance model of 390th epoch and imbalance model of 242th epoch are selected as final models. RBPsuite first breaks the full-length sequence into segments of 101 nucleotides. In general, deep learning methods are applied in a large-scale data. Given a full-length RNA sequence, it will break the sequence into multiple segments of 101 nts (used by iONMF [27] and our previous iDeep) without overlap, if the input sequence or the remaining sequence is shorter than 101nt, we pad it to a length of 101 using N as another 101nt-long segment. The backend predictor of the above webservers are non-deep learning-based methods, which are proved to be inferior to deep learning-based methods for predicting RBP binding sites [18]. Comparing with RBPPred, Deep-RBPPred predicts RBPs without using blast to generate PSSM matrix which is a time-consuming step. J Mol Recognit 24, 303313, https://doi.org/10.1002/jmr.1061 (2011). To overcome this shortcoming, we present Deep-RBPPred which is based on deep learning. All this makes RNAProt a valuable tool to apply in future RBP binding site research. This indicates that our models is not overfitting. 6. One protein (Uniprotid: P0DOC6) cant be calculated by RBPPred for that no protein sequences can be found by Blast. Van Nostrand, E. L. et al. We expect to update RBPsuite to be able to locate the exact binding nucleotides on RNAs. Bioinformatics. Abadi, M. et al. Get the most important science stories of the day, free in your inbox. Privacy S1). 2016; 64(2):28293. SMARTIV accepts a set of RNA sequences in BED format file as the input, and applies Hidden Markov Model (HMM) to find the enriched combined sequence and structure motifs from in vivo binding data. For the linear RNAs, the server predicts the RBP binding scores using our updated iDeepS, which is retrained on binding RNA targets of 154 RBPs derived from ENCODE. SONAR discovers RNA-binding proteins from analysis of large-scale protein-protein interactomes. BMC Bioinformatics 18, 136, https://doi.org/10.1186/s12859-017-1561-8 (2017). The prediction of circRNA-RBP binding sites is a fundamental step to the further understanding of the interaction mechanism between them. Consortium EP. This work has been supported by the Fundamental Research Funds for the Central Universities [2016YXMS017] and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No. As shown in Table2, Deep-RBPPred-balance achieves an average SN of 0.95 ((0.96+0.94+0.94)/3). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Xiaoyong Pan or Hong-Bin Shen. All authors approved this manuscript. & Hinton, G. E. In Advances in neural information processing systems. The negative sequences are collected from PDB, by using PISCCES36 with sequence identity cutoff 25%, sequence length between 50 and 10,000 and resolution of X-ray better than 3.0. RNA-binding protein recognition based on multi-view deep feature and multi-label learning. For bacteria, the number is set to 1/10 of E. coli. The sensitivity of balance model is about 7% higher than the state-of-the-art method. In addition, L2 regularization and dropout layer43 are added to avoid overfitting in the architecture of our deep learning. Zeng, H., Edwards, M. D., Liu, G. & Gifford, D. K. Convolutional neural network architectures for predicting DNA-protein binding. Experimental results show that the average AUC performance of our method is 93.85%, which is better than the current state-of-the-art . eCollection 2022. RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. More importantly, Deep-RBPPred needs fewer features than RBPPred. Deep-RBPPred was then applied to estimate RBPs in 139 reviewed proteomes from the Uniprot dataset. The SVM-based models are constructed on the training sets with the libsvm-3.2241 and tested in the testing dataset. By using this website, you agree to our Here we do not list the computational time of RBPPred because it costs much more computational time. Yu H, Wang J, Sheng Q, Liu Q, Shyr Y. beRBP: binding estimation for human RNA-binding proteins. The results of prediction are shown in Fig. The results are shown in Table1. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. 4, the training and testing loss decrease with the epoch. 2021 May 20;22(3):bbaa174. PubMedGoogle Scholar. The process of 10-cross validation (Fig. Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, et al. Take the advantage of computational time, Deep-RBPPred can be used to estimate RBPs in proteome scale quickly.
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