m6AmPred: Identifying RNA N6, 2′-O-dimethyladenosine (m6Am) sites based on sequence-derived information

Jiang, Jie; Chen, Kunqi; Lu, Zhi-Liang; Rong, Rong; Zhong, Yu; Meng, Jia

doi:10.1016/j.ymeth.2021.01.007

Cited by 17 publications

(16 citation statements)

References 37 publications

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“…The first step of functional epitranscriptome prediction is to identify modification sites, either directly from high-throughput sequencing data or by using sequenced-based computational prediction tools. There exist a large number of sequencing technologies and software tools that can serve this purpose, including but not limited to those based on reverse transcription signature [55] , [56] , [57] , [58] , [59] , bisulfite treatment [39] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , antibody [11] , [12] and the primary sequences of RNA molecules [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] . In the following paragraph, we cover primarily two most widely used approaches, including site detection from MeRIP-Seq data and sequence-based in silico prediction methods.…”

Section: Identification Of Rna Modification Sitementioning

confidence: 99%

Recent advances in functional annotation and prediction of the epitranscriptome

Zhang

Zhang²,

Zhang³

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Identification Of Rna Modification Sitementioning

confidence: 99%

Recent advances in functional annotation and prediction of the epitranscriptome

Zhang

Zhang²,

Zhang³

et al. 2021

Computational and Structural Biotechnology Journal

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“…To date, a number of computational approaches have been proposed for in silico prediction of RNA modification sites from the primary RNA sequences, including: the iRNA toolkits [3][4][5][6][7][8][9][10][11] , SRAMP 12 , DeepPromise 13 , WHISTLE 14 , Gene2vec 15 , m6A-Atlas 16 , RMDisease 17 , PEA 18 , PPUS 19 , BERMP 20 , m5Upred 21 , and m6AmPred 22 . Special attention has also been paid to the prediction of RNA modifications in introns 23 , lncRNAs 24 as well as various tissues and cell lines [25][26][27] .…”

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confidence: 99%

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

et al. 2021

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Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

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“… Jiang et al (2022) presented the m6AmPred, the first web server, for in silico identification of m6Am sites from the primary sequences of RNA. m6AmPred was built upon the XgbDart (eXtreme Gradient Boosting with Dart algorithm) and EIIP-PseEIIP encoding scheme.…”

Section: Methods To Detect Rna Modificationsmentioning

confidence: 99%

Dynamic regulation and key roles of ribonucleic acid methylation

Zou

Liu

Tan

et al. 2022

Front. Cell. Neurosci.

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Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), 5-hydroxymethylcytosine (hm5C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

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m6AmPred: Identifying RNA N6, 2′-O-dimethyladenosine (m6Am) sites based on sequence-derived information

Cited by 17 publications

References 37 publications

Recent advances in functional annotation and prediction of the epitranscriptome

Recent advances in functional annotation and prediction of the epitranscriptome

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Dynamic regulation and key roles of ribonucleic acid methylation

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