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RNA Informatics 2.0

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2584

Special Issue Editor


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Guest Editor
Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Shinjuku-ku, Tokyo 169-8555, Japan
Interests: noncoding RNA (ncRNA); long noncoding RNA (lncRNA); bioinformatics; computational biology
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Special Issue Information

Dear Colleagues,

Recent studies have shown that there is a large number of noncoding RNAs (ncRNAs) that are not translated into proteins but play important roles in various cellular functions. For example, several studies have suggested more long noncoding RNAs (lncRNAs) exist than messenger RNAs in higher eukaryotes, including humans, and elucidating the functions of these lncRNAs is an urgent task in current molecular biology. On the other hand, various types of RNA-related omics data, such as transcriptome (e.g., RNA-seq), epi-transcriptome (e.g., m6A-seq, m1A-seq), structurome (e.g., DMS-seq, SHAPE-seq), and interactome (e.g., CLIP-seq), are accumulating in the world. Integrating these omics data with bioinformatic approaches has also become important in RNA biology. In addition to natural RNAs, artificial RNAs, such as RNA aptamers, are also attracting attention from researchers as a new generation of target molecules for drug discovery; computational design of RNA sequence (e.g., RNA aptamer design, RNA inverse folding) is an important problem in this field. This Special Issue covers all aspects of RNA informatics. We welcome papers on both fundamental computational methods for analyzing RNAs and omics data analysis on RNAs.

Prof. Dr. Michiaki Hamada
Guest Editor

Manuscript Submission Information

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Keywords

  • ncRNA (e.g., miRNA, piRNA, snoRNA)
  • lncRNA
  • omics data (e.g., RNA-seq, CLIP-seq, m6A-seq, ribo-seq)
  • RNA aptamer
  • RNA design (e.g., inverse folding)
  • RNA interactome (RNA–protein interaction, RNA–RNA interaction)
  • RNA modification (e.g., m6A, m1A, inosine)
  • RNA structure (e.g., secondary structure, tertiary structure, G4)
  • RNA alignment
  • RNA classification

Published Papers (1 paper)

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Review

25 pages, 958 KiB  
Review
Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments
by Yoojoong Kim and Minhyeok Lee
Int. J. Mol. Sci. 2023, 24(12), 10299; https://doi.org/10.3390/ijms241210299 - 18 Jun 2023
Cited by 5 | Viewed by 2332
Abstract
This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims [...] Read more.
This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies. Full article
(This article belongs to the Special Issue RNA Informatics 2.0)
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