DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites

Liu, Quanzhong; Chen, Jinxiang; Wang, Yanze; Li, Shuqin; Jia, Cangzhi; Song, Jiangning; Li, Fuyi

doi:10.1093/bib/bbaa124

Cited by 94 publications

(58 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

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

show abstract

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

show abstract

“…Eventually, they identified 41 bp as the optimal length for consistently obtaining the best performance, regardless of species. Surprisingly, the same optimal sequence length has been used in later studies 69 , 70 of other species. 24 , 25 Ultimately, a set of 3,108, 3,538, 3,956, 776, 1,812, and 1138 samples for C .…”

Section: Methodsmentioning

confidence: 99%

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

Manavalan

Hasan

Basith

et al. 2020

Molecular Therapy - Nucleic Acids

View full text Add to dashboard Cite

DNA N 4 -methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN ( Escherichia coli ), DNA4mC-LIP ( Arabidopsis thaliana ), iDNA-MS ( Fragaria vesca ), DNA4mC-LIP and 4mCCNN ( Drosophila melanogaster ), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus , Geobacter pickeringii , and Mus musculus , thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

show abstract

“…Recently, various 4mC sites identifiers have been suggested for several species entailing A. thaliana, C. elegans, D. melanogaster, G. pickeringii, E. coli and G. subterraneus [23,24]. Further a first computational tool for 4mC identification in Rosaceae genomes is recently introduced which is, i4mC-ROSE [25].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, neural networks have acquired great importance due to their high performance in different fields like medical imaging [29][30][31], agriculture [32][33][34][35][36][37], image quality assessment and others. Moreover, neural networks have exhibited great performance in the identification of 6mA sites [38,39], m6A sites [40,41], 4mC sites [23,24,42], functional piRNAs [43], N4-acetylcytidine sites [44], promoters classification [45] and others. Inspired from the high performance given by neural network tools for modification identification in different sites, we have proposed a neural network based tool for 4mC identification in Rosaceae genomes.…”

Section: Introductionmentioning

confidence: 99%

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

2021

View full text Add to dashboard Cite

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community.

show abstract

DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites

Cited by 94 publications

References 48 publications

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

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

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

Contact Info

Product

Resources

About