MethyRNA: a web server for identification of N<sup>6</sup>-methyladenosine sites

Chen, Wei; Tang, Hua; Lin, Hao

doi:10.1080/07391102.2016.1157761

Cited by 119 publications

(96 citation statements)

References 34 publications

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“…Inspired by Chen et al .’s works1011, Zhou and his co-workers also proposed a mammalian m 6 A site predictor named SRAMP12. Subsequently, a webserver called MethyRNA was proposed to identify m 6 A sites in H. sapiens and M. musculus 13. Although the performances of existing methods are satisfactory for identifying m 6 A site in mammalian transcriptomes13, they fails to accurately identify m 6 A site in yeast12.…”

mentioning

confidence: 99%

“…Subsequently, a webserver called MethyRNA was proposed to identify m 6 A sites in H. sapiens and M. musculus 13. Although the performances of existing methods are satisfactory for identifying m 6 A site in mammalian transcriptomes13, they fails to accurately identify m 6 A site in yeast12. This may be due to the fact that the information around the yeast m 6 A site has not been fully characterized12.…”

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confidence: 99%

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Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Chen

Xing

Zou

2017

Sci Rep

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110

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As one of the most abundant RNA post-transcriptional modifications, N6-methyladenosine (m6A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m6A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m6A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m6A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m6A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/.

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confidence: 99%

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confidence: 99%

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Chen

Xing

Zou

2017

Sci Rep

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110

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“…Fourth, to reduce redundancy and bias, none of the included RNA segments had pairwise sequence identity with any other in a same subset. By strictly following the above procedures, we obtained an array of benchmark datasets with different

ξ

values and hence different lengths of RNA samples

(2 ξ + 1)

as well (see Equation 1), as illustrated below

{double-struckS}_{ξ} (normal⊛) \subset {\begin{matrix} 23 nucleotides, when ξ = 11 \\ 25 nucleotides, when ξ = 12 \\ 27 nucleotides, when ξ = 13 \\ ⋮ \\ 39 nucleotides, when ξ = 19 \\ 41 nucleotides, when ξ = 20 \\ 43 nucleotides, when ξ = 21 \end{matrix},

where the symbol

\subset

means “formed by.” It was observed via preliminary tests as well as many reports19, 43, 66 that when

ξ = 20

(i.e., the RNA samples formed by 41 nucleotides [nt]), the corresponding results were most promising. Accordingly, hereafter we only consider the 41-nt RNA sequences.…”

Section: Methodsmentioning

confidence: 99%

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Feng

Ding

Yang

et al. 2017

Molecular Therapy - Nucleic Acids

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There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called “iRNA-PseColl” has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform’s web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.

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“…This problem is naturally a supervised learning task, which aims to train predictive models for each type by using labeled positive and negative modification sites. There is a large collection of algorithms for predicting m 6 A sites from mRNA sequences [4][5][6][7][8][9][10], most notably SRAMP. However, such predictive algorithms for other modifications are still scarce because training robust models for these modification sites face several challenges [11,12].…”

Section: Introductionmentioning

confidence: 99%

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks

Salekin

Mostavi

Chiu³

et al. 2020

Preprint

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Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms.To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m 6 A, m 1 A, m 1 G, m 2 G, m 5 C, m 5 U, 2′-O-Me, Pseudouridine (Ψ) and Dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m 6 A site prediction algorithm and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.Keywords: N6-methyladenosine (m 6 A), epitranscriptome, RNA modification site prediction, generative adversarial networks (GAN), unsupervised representation learning, methylated RNA immunoprecipitation sequencing (MeRIP-Seq).

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MethyRNA: a web server for identification of N⁶-methyladenosine sites

Cited by 119 publications

References 34 publications

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks

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MethyRNA: a web server for identification of N6-methyladenosine sites

Cited by 119 publications

References 34 publications

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks

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MethyRNA: a web server for identification of N⁶-methyladenosine sites