Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
3.5
Journals
Biology
Special Issues
Bioinformatics in RNA Modifications and Non-Coding RNAs
share announcement

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: closed (10 July 2025) | Viewed by 5026

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

13 pages, 2361 KB  
Article
Grazing-Induced Changes in circRNAs, miRNAs and mRNAs Expression in Tibetan Sheep Biceps Femoris
by Xiong Ma, Shaobin Li, Zhanzhao Chen, Zhaohua He, Jianming Ren, Shiyu Tao, Lan Zhang and Pengfei Zhao
Biology 2025, 14(9), 1143; https://doi.org/10.3390/biology14091143 - 29 Aug 2025
Viewed by 389
Abstract
The present study is aimed at investigating the effects of grazing on the meat quality of Tibetan sheep, as well as the associated molecular mechanisms. A total of ten Tibetan sheep were utilized and equally allocated into two groups: grazing and pen-feeding. To [...] Read more.
The present study is aimed at investigating the effects of grazing on the meat quality of Tibetan sheep, as well as the associated molecular mechanisms. A total of ten Tibetan sheep were utilized and equally allocated into two groups: grazing and pen-feeding. To assess the intramuscular fat (IMF) content, Soxhlet extraction was performed on the biceps femoris muscle. Additionally, transcriptome sequencing was carried out to evaluate the expression profiles of RNAs, facilitating the construction of a ceRNA regulatory network. The results demonstrated that the IMF content in the grazing group was significantly higher compared to the pen-feeding group, implying that grazing might foster the formation of Type I muscle fibers, thereby enhancing meat quality. Moreover, the expression levels of circRNAs, such as novel_circ_001331, novel_circ_012918, novel_circ_029843, and novel_circ_059962, were markedly up-regulated in the grazing group. These circRNAs may alleviate the inhibitory effects on genes like COL8A1, MYLK3, and NOX4 by interacting with miR-381-y, miR-7144-x, miR-16-z, miR-8159-x, novel-m0040-3p, novel-m0092-5p, and oar-miR-329a-3p. These circRNAs and miRNAs are predominantly involved in the MAPK, Wnt, and VEGF signaling pathways and could be implicated in biological processes such as muscle fiber type switching and energy metabolism. This research offers valuable insights for improving the meat quality of Tibetan sheep and provides a foundation for exploring the role of circRNA and miRNA in the regulation of meat quality under grazing conditions. Full article
(This article belongs to the Special Issue Bioinformatics in RNA Modifications and Non-Coding RNAs)
►▼ Show Figures

Figure 1

19 pages, 16639 KB  
Article
Nucleotide Metabolism and Immune Genes Can Predict the Prognostic Risk of Hepatocellular Carcinoma and the Immune Microenvironment
by Xiaofang Wang, Qinghua Cui and Yuan Zhou
Biology 2025, 14(8), 1079; https://doi.org/10.3390/biology14081079 - 18 Aug 2025
Viewed by 613
Abstract
The overall survival of hepatocellular carcinoma (HCC) remains poor, highlighting the need for better prognostic tools. Nucleotide metabolism fuels tumor progression, while the immune microenvironment dictates therapy response, but integrated models combining both features are lacking. Using TCGA-LIHC transcriptomic/clinical data, we identified nucleotide [...] Read more.
The overall survival of hepatocellular carcinoma (HCC) remains poor, highlighting the need for better prognostic tools. Nucleotide metabolism fuels tumor progression, while the immune microenvironment dictates therapy response, but integrated models combining both features are lacking. Using TCGA-LIHC transcriptomic/clinical data, we identified nucleotide metabolism and immune-related differentially expressed genes (NMIRGs), which stratified HCC patients into two subtypes via non-negative matrix factorization. A nine-gene prognostic risk signature was constructed through LASSO/Cox regression and validated using independent GEO datasets, and the NMIRG signature was further validated experimentally via RT-qPCR in HCC cell lines and independently using the HPA database for protein-level evidence. As evaluated by our risk signature, high-risk patients exhibited altered immune profiles (T cells increasing, neutrophils decreasing), elevated tumor mutation burden and microsatellite instability, and worse predicted immunotherapy response. Gene set enrichment analysis linked high-risk genes to immune pathways and low-risk genes to metabolic processes. Our risk signature predicted HCC prognosis independent of demographic features and outperformed existing signatures with superior C-index accuracy, effectively predicting immune microenvironment status and therapy benefits. Together, this integrated NMIRG signature offers enhanced prognostication and identifies promising biomarkers for personalized HCC management. Full article
(This article belongs to the Special Issue Bioinformatics in RNA Modifications and Non-Coding RNAs)
►▼ Show Figures

Figure 1

15 pages, 3934 KB  
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
Cited by 2 | Viewed by 1404
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

24 pages, 4255 KB  
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 1907
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop