PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Li, Fuyi; Fan, Cunshuo; Marquez‐Lago, Tatiana T.; Leier, André; Revote, Jerico; Jia, Cangzhi; Zhu, Yan; Smith, A. Ian; Webb, Geoffrey I.; Liu, Quanzhong; Wei, Leyi; Li, Jian; Song, Jiangning

doi:10.1093/bib/bbz050

Cited by 38 publications

(26 citation statements)

References 58 publications

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“…It should be noted that we tried a large number of other types of features generated by iLearn [36] or Pse-in-One [37] toolkits when we designed the input features (data not shown). The sequence-based features generated by these two toolkits have been used widely for predicting both RNA post-transcriptional modification sites [38][39][40] and post-translational modification sites [41,42]. Our experimental results demonstrated that our proposed feature combination in this study yielded satisfactory performance, which cannot be significantly improved when they were combined with other features.…”

Section: Discussionmentioning

confidence: 89%

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: Discussionmentioning

confidence: 89%

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

Chen

Xiong

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…2) It combined various meta-predictors via the SVM-based Collaborative Learning. It can be anticipated that the proposed Collaborative Learning framework would be applied to solve many important problems in bioinformatics, such as protein disordered protein prediction [64], DNA replication origin prediction, protein post-translational modification sites [65], etc.…”

Section: Resultsmentioning

confidence: 99%

iRBP-Motif-PSSM: Identification of RNA-Binding Proteins Based on Collaborative Learning

et al. 2019

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RNAs and RNA-binding proteins (RBPs) in cells can bind with each other to form a nuclear ribonucleoprotein (RNP) complex, playing important roles in life processes, and gene regulation. How to accurately predict the RNA-binding proteins is a big challenge and hot research task. Here, we proposed a new computational predictor called iRBP-Motif-PSSM for identifying RNA-binding proteins by combining the motif information and the evolutionary information extracted from the Position Specific Scoring Matrixes. Collaborative Learning was employed to address the instability problem of the predictor. The experimental results showed that iRBP-Motif-PSSM showed better performance than other existing methods for identifying NA-binding proteins, indicating that iRBP-Motif-PSSM is a useful tool for biological analysis.

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“…It should be noted that we tried a large number of other types of features generated by iLearn [ 36 ] or Pse-in-One [ 37 ] toolkits when we designed the input features (data not shown). The sequence-based features generated by these two toolkits have been used widely for predicting both RNA post-transcriptional modification sites [ 38 – 40 ] and post-translational modification sites [ 41 , 42 ]. Our experimental results demonstrated that our proposed feature combination in this study yielded satisfactory performance, which cannot be significantly improved when they were combined with other features.…”

Section: Discussionmentioning

confidence: 99%

m5CPred-SVM: a novel method for predicting m5C sites of RNA

et al. 2020

View full text Add to dashboard Cite

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 https://zhulab.ahu.edu.cn/m5CPred-SVM.

show abstract

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Cited by 38 publications

References 58 publications

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

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

iRBP-Motif-PSSM: Identification of RNA-Binding Proteins Based on Collaborative Learning

m5CPred-SVM: a novel method for predicting m5C sites of RNA

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