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Biology
Special Issues
Bioinformatics in RNA Modifications and Non-Coding RNAs
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Bioinformatics in RNA Modifications and Non-Coding RNAs

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 2088

Special Issue Editors

Dr. Yuan Zhou

E-Mail Website
Guest Editor
Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China
Interests: RNA bioinformatics; RNA modification; non-coding RNA; single-cell and spatial transcriptome; gene expression regulation; machine learning; database
Dr. Yongsheng Li

E-Mail Website
Guest Editor
College of Biomedical Information and Engineering, Hainan Medical University, Haikou 570100, China
Interests: biomedical informatics; immunoinformatics

Special Issue Information

Dear Colleagues,

RNA-mediated post-transcriptional regulation of gene expression has recently emerged as a cutting-edge research field, where bioinformatics analysis of RNA data provides crucial support for understanding the molecular mechanisms of physiological and disease processes related to the RNA network, and for developing novel disease prevention and treatment strategies that exploit or target RNAs. Epitranscriptomic RNA modification and non-coding RNA network constitute the main building blocks of RNA regulation, which could conventionally regulate thousands of targets in the transcriptomes. Clearly, the wide regulatory spectrum of these RNA modification and non-coding RNA signifies the continuous development of bioinformatics methods and their application scenario for better interpretation of their distribution, dynamics, function and disease associations.

This Special Issue in Biology aims to provide a communication platform for bioinformatics specialists and experimental biologists in the field of RNA modification and non-coding RNA regulation. While we focus on the development of advanced bioinformatics methods for the detection, prediction, functional analysis and therapeutic association of RNA modification and non-coding RNA, submissions on novel integrative applications of available bioinformatics methods used to highlight the significance of RNA regulation in biological and disease processes are also welcome.

Dr. Yuan Zhou
Dr. Yongsheng Li
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-coding RNA
  • RNA modification
  • bioinformatics analysis
  • statistical modeling
  • machine learning
  • post-transcriptional regulation
  • transcriptome

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Published Papers (2 papers)

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Research

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15 pages, 3934 KiB  
Article
GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection
by Guohua Huang, Runjuan Xiao, Weihong Chen and Qi Dai
Biology 2024, 13(10), 798; https://doi.org/10.3390/biology13100798 - 6 Oct 2024
Viewed by 834
Abstract
Phosphorylation, a reversible and widespread post-translational modification of proteins, is essential for numerous cellular processes. However, due to technical limitations, large-scale detection of phosphorylation sites, especially those infected by SARS-CoV-2, remains a challenging task. To address this gap, we propose a method called [...] Read more.
Phosphorylation, a reversible and widespread post-translational modification of proteins, is essential for numerous cellular processes. However, due to technical limitations, large-scale detection of phosphorylation sites, especially those infected by SARS-CoV-2, remains a challenging task. To address this gap, we propose a method called GBMPhos, a novel method that combines convolutional neural networks (CNNs) for extracting local features, gating mechanisms to selectively focus on relevant information, and a bi-directional gated recurrent unit (Bi-GRU) to capture long-range dependencies within protein sequences. GBMPhos leverages a comprehensive set of features, including sequence encoding, physicochemical properties, and structural information, to provide an in-depth analysis of phosphorylation sites. We conducted an extensive comparison of GBMPhos with traditional machine learning algorithms and state-of-the-art methods. Experimental results demonstrate the superiority of GBMPhos over existing methods. The visualization analysis further highlights its effectiveness and efficiency. Additionally, we have established a free web server platform to help researchers explore phosphorylation in SARS-CoV-2 infections. The source code of GBMPhos is publicly available on GitHub. Full article
(This article belongs to the Special Issue Bioinformatics in RNA Modifications and Non-Coding RNAs)
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Review

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24 pages, 4255 KiB  
Review
Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites
by Zhengtao Luo, Liyi Yu, Zhaochun Xu, Kening Liu and Lichuan Gu
Biology 2024, 13(10), 777; https://doi.org/10.3390/biology13100777 - 28 Sep 2024
Viewed by 948
Abstract
N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, [...] Read more.
N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future. Full article
(This article belongs to the Special Issue Bioinformatics in RNA Modifications and Non-Coding RNAs)
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