Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy

Yu, Lezheng; Zhang, Yonglin; Li, Xue; Liu, Fengjuan; Jing, Runyu; Luo, Jie

doi:10.3389/fmicb.2023.1175925

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…In terms of m 5 U RNA modification, Jiang et al ( 2020 ) proposed the first sequence-based human m 5 U prediction framework m5UPred, followed by iRNA-m5U targeting yeast transcriptome (Feng and Chen, 2022 ). The prediction performance of human m 5 U has been further improved by m5U-SVM (Ao et al, 2023 ) and m5U-autoBio (Yu et al, 2023 ). In addition, RNADSN was developed by learning the common features between tRNA m 5 U and mRNA m 5 U (Li et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

Xu,

Wang,

Meng

et al. 2023

Front. Microbiol.

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5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

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Section: Introductionmentioning

confidence: 99%

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

Xu,

Wang,

Meng

et al. 2023

Front. Microbiol.

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A robust deep learning approach for identification of RNA 5-methyluridine sites

Shaon,

Karim,

Ali

et al. 2024

Sci Rep

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RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-76148-9.

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Unveiling the Potential Pattern Representation of RNA 5-Methyluridine Modification Sites Through a Novel Feature Fusion Model Leveraging Convolutional Neural Network and Tetranucleotide Composition

Alam,

Tahir,

Hussain

et al. 2024

IEEE Access

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The 5-Methyluridine (m5U), predominantly present in RNA and especially enriched in transfer RNA (tRNA), significantly enhances translational accuracy and protein synthesis by ensuring precise genetic information decoding and optimal tRNA functionality within cellular mechanisms. The identification of m5U modification sites is crucial, as this modification has gained significant attention in diseases such as breast cancer, stress response, and viral infections, offering insights into its molecular mechanisms and regulatory functions in disease contexts. Nevertheless, due to the arduous nature, intricate procedures, reliance on sophisticated and expensive instrumentation, and the need for specialized expertise, conventional biochemical approaches for identifying m5U modification sites result in substantial resource expenditures and notable temporal investments. Consequently, the pressing need for a precise and efficient computational method highlights the urgency for alternative approaches in identifying m5U modification sites. In this study, we introduce a novel computational approach called "Deep-m5U," which combines the strengths of Convolutional Neural Networks (CNNs) and tetranucleotide composition to accurately identify methyluridine modification sites and improve overall performance. The developed Deep-m5U method leverages CNNs to accurately detect protein-coding regions and capture relevant motifs, while incorporating tetra-nucleotide composition to capture global compositional characteristics, resulting in a more robust model that significantly enhances performance. We evaluated the Deep-m5U model on two publicly available benchmark datasets: the full transcript and mature mRNA datasets. Our results showcase superior performance, achieving accuracies of 91.26% and 95.63% respectively, surpassing the current cutting-edge methods. Moreover, the open-source code for Deep-m5U is freely accessible at: https://github.com/waleed551/Deep-m5U.

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Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy

Cited by 4 publications

References 65 publications

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

A robust deep learning approach for identification of RNA 5-methyluridine sites

Unveiling the Potential Pattern Representation of RNA 5-Methyluridine Modification Sites Through a Novel Feature Fusion Model Leveraging Convolutional Neural Network and Tetranucleotide Composition

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