RNAm5Cfinder: A Web-server for Predicting RNA 5-methylcytosine (m5C) Sites Based on Random Forest

Li, Jianwei; Huang, Yan; Yang, Xiaoyue; Zhou, Yiran; Zhou, Yuan

doi:10.1038/s41598-018-35502-4

Cited by 51 publications

(56 citation statements)

References 17 publications

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“…thaliana, SVM again gave the highest AUC value at 0.782, which is higher than those of iRNA-m5C [26], iRNAm5C-PseDNC [20] and RNAm5CPred [23]. Two methods are available for predicting m5C sites of M. musculus, namely RNAm5Cfinder [25] and iRNA-m5C [26]. Two methods are available for detecting m5C sites of A. thaliana, namely PEA-m5C [24] and iRNA-m5C.…”

Section: Comparison With Other Classifiersmentioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

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Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at http://zhulab.ahu.edu.cn/m5CPred-SVM.

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Section: Comparison With Other Classifiersmentioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

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“…iRNA-m5C [26], iRNAm5C-PseDNC [20] and RNAm5CPred [23]. Two methods are available for predicting m5C sites of M. musculus, namely RNAm5Cfinder [25] and iRNA-m5C [26].…”

Section: Comparison With Other Classifiersmentioning

confidence: 99%

“…Recently, Fang et al [24] mainly focused in predicting m5C sites in A. thaliana. Li et al [25] had collected data from GEO database and developed a Web server RNAm5Cfinder based on random forest algorithm, which can be used to predict m5C sites in eight kinds of cells or tissues of mouse and human. More recently, Lv et al [26] developed a server called iRNA-m5C to predict m5C sites of four types of species.…”

Section: Introductionmentioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

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Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functionssuch as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA,researcherscanbetter understandthe exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost.However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVMoffered substantially higher prediction accuracy thanpreviously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species.The result shows that our model outperformed the existing state-of-art models.Our model is available for users through a web serverat http://zhulab.ahu.edu.cn/m5CPred-SVM.

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“…Classification algorithms were used to separate the positive samples from the negative ones in each of the four datasets {D(Sc), D(Sp), D(Kl), D(Pp)}. This study utilized five classification algorithms to evaluate their capabilities of predicting DNA replication origins, i.e., support vector machine (SVM) (Weston, et al, 2001), random forest (RF) (Jang, et al, 2018;Li, et al, 2018), multinomial naïve Bayes (MNB) classifier (Pan, et al, 2018), gradient boosting decision tree (GBDT) (Liang, et al, 2018;Wang, et al, 2019), and back propagation neural network (BPNN) (Rumelhart, et al, 1986).…”

Section: Classification Of Dna Replication Originsmentioning

confidence: 99%

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

et al. 2019

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Motivation Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions. Results This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study. Availability and implementation The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page. Supplementary information Supplementary data are available at Bioinformatics online.

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RNAm5Cfinder: A Web-server for Predicting RNA 5-methylcytosine (m5C) Sites Based on Random Forest

Cited by 51 publications

References 17 publications

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

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