Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors

Ahmed, Sajid; Zahid, Hossain Md.; Uddin, Mahtab; Taherzadeh, Ghazaleh; Sharma, Alok; Shatabda, Swakkhar; Dehzangi, Abdollah

doi:10.1016/j.csbj.2020.10.032

Cited by 12 publications

(9 citation statements)

References 63 publications

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“…It is worth noting that, to the best of our knowledge, there exist only two computational approaches iRNA5hmC (Liu et al , 2020) and iRNA5hmC-PS ( Ahmed et al , 2020 ) for predicting hm 5 C RNA modification from RNA sequences. Both methods were based on strongly supervised learning of the same dataset ( Delatte et al , 2016 ) as the one used in our study.…”

Section: Resultsmentioning

confidence: 99%

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Huang

Wei

et al. 2021

Bioinformatics

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Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Huang

Wei

et al. 2021

Bioinformatics

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“…Notable exceptions are studies on the functional consequences of RNA editing, which can draw on reproducible mapping data produced by multiple (and independent) laboratories [49][50][51][52]. Inaccurate mapping of any RNA modification will greatly affect all experimental conclusions, hypothesis building, and importantly, ongoing bioinformatics efforts to predict RNA modification patterns in silico [53][54][55][56][57][58][59][60][61]. A persistent question, therefore, is how to reliably map specific epitranscriptomes, not only in reproducible fashion but also sufficiently robust to technical variation.…”

Section: Once Is Never: How Reproducible Is Current Modification Mapping?mentioning

confidence: 99%

The Regulation of RNA Modification Systems: The Next Frontier in Epitranscriptomics?

Schaefer

2021

Genes

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RNA modifications, long considered to be molecular curiosities embellishing just abundant and non-coding RNAs, have now moved into the focus of both academic and applied research. Dedicated research efforts (epitranscriptomics) aim at deciphering the underlying principles by determining RNA modification landscapes and investigating the molecular mechanisms that establish, interpret and modulate the information potential of RNA beyond the combination of four canonical nucleotides. This has resulted in mapping various epitranscriptomes at high resolution and in cataloguing the effects caused by aberrant RNA modification circuitry. While the scope of the obtained insights has been complex and exciting, most of current epitranscriptomics appears to be stuck in the process of producing data, with very few efforts to disentangle cause from consequence when studying a specific RNA modification system. This article discusses various knowledge gaps in this field with the aim to raise one specific question: how are the enzymes regulated that dynamically install and modify RNA modifications? Furthermore, various technologies will be highlighted whose development and use might allow identifying specific and context-dependent regulators of epitranscriptomic mechanisms. Given the complexity of individual epitranscriptomes, determining their regulatory principles will become crucially important, especially when aiming at modifying specific aspects of an epitranscriptome both for experimental and, potentially, therapeutic purposes.

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“…In this method, an SVM classifier used k-mers as features, producing an accuracy of 65.48%. Similarly, another SVM-based predictor called iRNA5hmC-PS [77] was established. It proposed a new feature extraction method called Position-Specific Gapped k-mer and used Position-Specific k-mer to retain most of the characteristic information of RNA sequences.…”

Section: Machine Learning Approaches For Rna Modification Sites Predictionmentioning

confidence: 99%

Machine learning applications in RNA modification sites prediction

Allali¹,

Elhamraoui²,

Daoud³

2021

Computational and Structural Biotechnology Journal

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Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors

Abstract: Graphical abstract

Cited by 12 publications

References 63 publications

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

The Regulation of RNA Modification Systems: The Next Frontier in Epitranscriptomics?

Machine learning applications in RNA modification sites prediction

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