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• W2991266812 abstract "Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%–70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem. Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%–70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem. Pseudouridine (Ψ) is the most prevalent post-transcriptional modification, and it has been widely found in a series of biological and cellular processes.1Hudson G.A. Bloomingdale R.J. Znosko B.M. Thermodynamic contribution and nearest-neighbor parameters of pseudouridine-adenosine base pairs in oligoribonucleotides.RNA. 2013; 19: 1474-1482Crossref PubMed Scopus (44) Google Scholar,2Sloan K.E. Warda A.S. Sharma S. Entian K.D. Lafontaine D.L.J. Bohnsack M.T. Tuning the ribosome: The influence of rRNA modification on eukaryotic ribosome biogenesis and function.RNA Biol. 2017; 14: 1138-1152Crossref PubMed Scopus (122) Google Scholar Recent studies have demonstrated that Ψ sites exist in many kinds of RNAs, such as small nuclear RNA (snRNA), rRNA, tRNA, mRNA, and small nucleolar RNA (snoRNA).3Ge J. Yu Y.T. RNA pseudouridylation: new insights into an old modification.Trends Biochem. Sci. 2013; 38: 210-218Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar, 4Han S. Liang Y. 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HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification.Bioinformatics. 2019; 35: 602-610Crossref PubMed Scopus (1) Google Scholar Thus, the Ψ site plays a crucial role in biological research and drug development. More specifically, Ψ is an isomer of uridine catalyzed by the Ψ synthase (PUS) that removes the uridine residue’s base from its sugar, followed by “rotating” it 180° along the N3-C6 axis, and subsequently reattaches the base’s 5-carbon to the 1’-carbon of the sugar.12Charette M. Gray M.W. Pseudouridine in RNA: what, where, how, and why.IUBMB Life. 2000; 49: 341-351Crossref PubMed Google Scholar Although there are several experimental methods based on the high-throughput techniques that have been developed to recognize the Ψ modifications, they are both costly and time consuming.13Carlile T.M. Rojas-Duran M.F. Zinshteyn B. Shin H. Bartoli K.M. Gilbert W.V. 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A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.BMC Bioinformatics. 2016; 17: 329Crossref PubMed Scopus (40) Google Scholar, 29Liao Z. Li D. Wang X. Li L. Zou Q. Cancer diagnosis from isomiR expression with machine learning method.Curr. Bioinform. 2018; 13: 57-63Crossref Scopus (15) Google Scholar, 30Xu A. Chen J. Peng H. Han G. Cai H. Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences.Front. Genet. 2019; 10: 236Crossref PubMed Scopus (1) Google Scholar Therefore, intelligent computational approaches are highly desirable to predict Ψ sites on RNA sequences. To the best of our knowledge, six predictors have been reported to identify Ψ sites. Specifically, Panwar and Raghava31Panwar B. Raghava G.P.S. Prediction of uridine modifications in tRNA sequences.BMC Bioinformatics. 2014; 15: 326Crossref PubMed Scopus (10) Google Scholar first proposed the tRNAmod model to predict Ψ sites in tRNA. Li et al.32Li Y.H. Zhang G. Cui Q. PPUS: a web server to predict PUS-specific pseudouridine sites.Bioinformatics. 2015; 31: 3362-3364Crossref PubMed Scopus (25) Google Scholar then developed the PPUS method based on the support vector machine (SVM) to identify PUS-specific Ψ sites. Later, Chen et al.33Chen W. Tang H. Ye J. Lin H. Chou K.C. iRNA-PseU: identifying RNA pseudouridine sites.Mol. Ther. Nucleic Acids. 2016; 5: e332Abstract Full Text Full Text PDF PubMed Google Scholar provided the iRNA-PseU predictor, and He et al.34He J. Fang T. Zhang Z. Huang B. Zhu X. Xiong Y. PseUI: pseudouridine sites identification based on RNA sequence information.BMC Bioinformatics. 2018; 19: 306Crossref PubMed Scopus (38) Google Scholar introduced the PseUI predictor, which are both based on the SVM classifier. In addition, Tahir et al.35Tahir M. Tayara H. Chong K.T. iPseU-CNN: identifying RNA pseudouridine sites using convolutional neural networks.Mol. Ther. Nucleic Acids. 2019; 16: 463-470Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar built the iPseU-CUU model based on the convolution neural network (CNN). Most recently, Chen et al.36Liu K. Chen W. Lin H. XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.Mol. Genet. Genomics. 2019; (Published online August 7, 2019)https://doi.org/10.1007/s00438-019-01600-9Crossref Scopus (8) Google Scholar proposed an eXtreme Gradient Boosting (xgboost)-based method (XG-PseU). It should be noted that the same datasets, built by Chen et al.,33Chen W. Tang H. Ye J. Lin H. Chou K.C. iRNA-PseU: identifying RNA pseudouridine sites.Mol. Ther. Nucleic Acids. 2016; 5: e332Abstract Full Text Full Text PDF PubMed Google Scholar were applied in the three studies (iRNA-PseU, PseUI, and iPseU-CUU) to build the predictors, including the benchmark training datasets (H_990, S_628, and M_944) and the independent testing datasets (H_200 and S_200). Here, H, S, and M represent the RNA samples for H. sapiens, S. cerevisiae, and M. musculus, while 990, 628, 944, and 200 indicate the corresponding sample numbers in each dataset. Thus, we used the datasets mentioned earlier in this article for convenient comparisons. The performances of the four predictors (iRNA-PseU, PseUI, iPseU-CUU, and XG-PseU) are listed in Table 1, where the XG-PseU results for independent datasets were obtained by the web server at http://www.bioml.cn. The jackknife test, 5-fold cross-validation, and 10-fold cross-validation are used for the iRNA-PseU, PseUI/iPseU-CUU, and XG-PseU models, respectively. It can be seen that their overall performances are gradually improved through the scientists’ efforts. Taking H_990 as an example, the accuracies have been improved by 6.28% from 60.40% (iRNA-PseU) to 61.24% (PseUI) and to 66.68% (iPseU-CUU). However, it must be noted that these predictive accuracies are still unsatisfactory.Table 1Results of the Proposed iRNA-PseU, PseUI, iPseU-CUU, and XG-PseU Predictors for Training Datasets H_990, S_628, and M_944 and Testing Datasets H_200 and S_200PredictorsTraining DatasetsAcc (%)MCCSn (%)Sp (%)Testing DatasetsAcc (%)MCCSn (%)Sp (%)iRNA-PseUaThe predictor developed by Chen et al.33H_99060.40.2161.0159.8H_20065.000.3060.0070.00PseUIbThe predictor proposed by He et al.3464.240.2864.8563.6465.500.3163.0068.00iPseU-CUUcThe predictor constructed by Tahir et al.3566.680.3465.0068.7869.000.4077.7260.81XG-PseUdThe predictor constructed by Liu et al.3665.440.3163.6467.2467.000.3467.0067.00iRNA-PseUaThe predictor developed by Chen et al.33S_62864.490.2964.6564.33S_20073.000.4681.0065.00PseUIbThe predictor proposed by He et al.3466.560.3362.171.0268.500.3772.0065.00iPseU-CUUcThe predictor constructed by Tahir et al.3568.150.3766.3670.4573.500.4768.7677.82XG-PseUdThe predictor constructed by Liu et al.3668.150.3766.8469.4571.000.4275.0067.00iRNA-PseUaThe predictor developed by Chen et al.33M_94469.070.3873.3164.83PseUIbThe predictor proposed by He et al.3470.440.4174.5866.31iPseU-CUUcThe predictor constructed by Tahir et al.3571.810.4474.4969.11XG-PseUdThe predictor constructed by Liu et al.3672.030.4576.4867.57a The predictor developed by Chen et al.33Chen W. Tang H. Ye J. Lin H. Chou K.C. iRNA-PseU: identifying RNA pseudouridine sites.Mol. Ther. Nucleic Acids. 2016; 5: e332Abstract Full Text Full Text PDF PubMed Google Scholarb The predictor proposed by He et al.34He J. Fang T. Zhang Z. Huang B. Zhu X. Xiong Y. PseUI: pseudouridine sites identification based on RNA sequence information.BMC Bioinformatics. 2018; 19: 306Crossref PubMed Scopus (38) Google Scholarc The predictor constructed by Tahir et al.35Tahir M. Tayara H. Chong K.T. iPseU-CNN: identifying RNA pseudouridine sites using convolutional neural networks.Mol. Ther. Nucleic Acids. 2019; 16: 463-470Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholard The predictor constructed by Liu et al.36Liu K. Chen W. Lin H. XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.Mol. Genet. Genomics. 2019; (Published online August 7, 2019)https://doi.org/10.1007/s00438-019-01600-9Crossref Scopus (8) Google Scholar Open table in a new tab As a crucial step toward building a machine-learning-based predictor, feature extraction becomes a particularly important process. Several sequence representation methods have been used in previous works to obtain feature vectors. For example, a hybrid approach of the binary profile of patterns (BPP) and structural information is applied in the tRNAmod.31Panwar B. Raghava G.P.S. Prediction of uridine modifications in tRNA sequences.BMC Bioinformatics. 2014; 15: 326Crossref PubMed Scopus (10) Google Scholar In addition, the PPUS model uses the nucleotides around Ψ as the features to identify.32Li Y.H. Zhang G. Cui Q. PPUS: a web server to predict PUS-specific pseudouridine sites.Bioinformatics. 2015; 31: 3362-3364Crossref PubMed Scopus (25) Google Scholar For the successful iRNA-PseU method, dinucleotide chemical properties (DCP) and nucleotide density (ND) are incorporated for identification.33Chen W. Tang H. Ye J. Lin H. Chou K.C. iRNA-PseU: identifying RNA pseudouridine sites.Mol. Ther. Nucleic Acids. 2016; 5: e332Abstract Full Text Full Text PDF PubMed Google Scholar For the PseUI, the effective features are selected from five different feature extraction techniques using the sequential forward-feature-selection method, including nucleotide composition (NC), dinucleotide composition (DNC), pseudo-dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP).37Chen C.Y. Chuang T.J. Comment on “A comprehensive overview and evaluation of circular RNA detection tools”.PLoS Comput. 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Bioinform. 2019; 20: 896-917Crossref PubMed Scopus (19) Google Scholar in which the recently reported overall accuracies are basically above 90%. In particular, the SVM-based iRNA(m6A)-PseDNC model demonstrates an Acc of 91.24% of 10-fold cross-validation for m6A identification for S. cerevisiae.43Chen W. Ding H. Zhou X. Lin H. Chou K.C. iRNA(m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition.Anal. Biochem. 2018; 561-562: 59-65Crossref PubMed Scopus (67) Google Scholar For the m5C site, the recently developed iRNA-m5C predictor by the Random Forest (RF) algorithm shows a jackknife test Acc up to 92.9% for H. sapiens.52Lv H. Zhang Z.M. Li S.H. Tan J.X. Chen W. Lin H. Evaluation of different computational methods on 5-methylcytosine sites identification.Brief. Bioinform. 2019; : bbz048PubMed Google Scholar For m1A, the SVM-based iRNA-3typeA method obtains a jackknife validation Acc of 99.13% on H. sapiens and 98.73% for M. musculus.56Chen W. Feng P. Yang H. Ding H. Lin H. Chou K.C. iRNA-3typeA: identifying three types of modification at RNA’s adenosine sites.Mol. Ther. Nucleic Acids. 2018; 11: 468-474Abstract Full Text Full Text PDF PubMed Scopus (112) Google Scholar However, as mentioned earlier, the evaluated accuracies of Ψ site identification of different models are basically only 60%–70%, where there is still a large amount of improvement possible. We noticed that a predictor called “KELMPSP” reported a better performance, where the accuracies for the H_990, S_628, M_949, H_200, and S_200 datasets are up to 74.55%, 85.53%, 79.45%, 72.5%, and 76.00%, respectively.58Li Y.Z. FY X. FY X. KELMPSP: pseudouridine sites identification based on kernel extreme learning machine.Chin. J. Biochem. Mol. Biol. 2018; 34: 785-793Google Scholar In this method, the kernel extreme learning machine (KELM) algorithm is applied, where the final features are obtained by combining NCP, nucleotide concentrations, and position-specific mononucleotide, dinucleotide, and trinucleotide propensity characteristics. However, the related web server at http://39.105.77.161:8890/KELMPSP is no longer available. In this paper, we first applied the bi-profile Bayes method (BPB)59Shao J. Xu D. Tsai S.-N. Wang Y. Ngai S.-M. Computational identification of protein methylation sites through bi-profile Bayes feature extraction.PLoS ONE. 2009; 4: e4920Crossref PubMed Scopus (126) Google Scholar to extract the RNA sequence features to identify the Ψ sites. Two algorithms, RF and SVM, were both used to construct the models, where the performances were evaluated by 5-fold cross-validation and independent tests. Then, we incorporated three different features with BPB to show their comprehensive performance, including basic Kmer (Kmer),60Wei L. Liao M. Gao Y. Ji R. He Z. Zou Q. Improved and promising identification of human microRNAs by incorporating a high-quality negative set.IEEE/ACM Trans. Comput. Biol. Bioinformatics. 2014; 11: 192-201Crossref PubMed Scopus (123) Google Scholar general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General) generated from the web server Pse-in-One,61Liu B. Liu F. Wang X. Chen J. Fang L. Chou K.C. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.Nucleic Acids Res. 2015; 43: W65-W71Crossref PubMed Google Scholar and NCP with ND (NCP+ND). Also, high-quality features were selected using the MRMD62Zou Q. Zeng J.C. Cao L.J. Ji R.R. A novel features ranking metric with application to scalable visual and bioinformatics data classification.Neurocomputing. 2016; 173: 346-354Crossref Scopus (0) Google Scholar method to predict the Ψ sites. First, we extracted the RNA features using the BPB method fo" @default.
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• W2991266812 date "2020-03-01" @default.
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• W2991266812 title "Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?" @default.
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