Computational Methods for the Analysis of RNA Structures and Modifications

A special issue of BioTech (ISSN 2673-6284). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (1 October 2023) | Viewed by 3109

Special Issue Editors

Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
Interests: gene regulation; transcriptomics; lncRNAs; enhancer-promoter communication; epitranscriptomics; RNA editing; RNA secondary structure; computational genomics; statistical modelling; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From research in RNA modifications (epitranscriptomics), it has become clear that RNA is a dynamic molecule that can be modified and folded into secondary and tertiary structures. Surprisingly, protein-coding messenger RNAs (mRNAs) occupy less than a few percent of the mammalian transcriptome, leaving behind the majority of transcribed RNAs as non-protein-coding RNAs (ncRNAs). These ncRNAs include ribosomal RNAs (rRNAs) and transfer RNAs (tRNAs), as well as other regulatory ncRNAs, such as microRNAs (miRNAs) and long ncRNAs (lncRNAs). In particular, lncRNAs, which are defined as any ncRNA longer than 200 nucleotides, have gained momentum in recent years, due both to the acceleration in their discovery as well as their potential to explain many cellular activities and physiological phenomena, including disease initiation and progression, which protein-centric research has so far been unable to explain.

It is now accepted in the field that lncRNAs exert their actions by binding to other macromolecules (i.e., DNA, RNA, and proteins). Furthermore, since they are long RNAs, their binding to other macromolecules is closely regulated by their secondary and tertiary structures as well as their epitranscriptomic modifications. This Special Issue will highlight and discuss further developments in computational methods for analyzing RNA structures and modifications.

We are pleased to invite you to submit a manuscript for this Special Issue. Original research articles and reviews are welcome.

Dr. Sarah Rennie
Prof. Dr. Shizuka Uchida
Guest Editors

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Published Papers (1 paper)

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18 pages, 1283 KiB  
Benchmarking RNA Editing Detection Tools
by David Rodríguez Morales, Sarah Rennie and Shizuka Uchida
BioTech 2023, 12(3), 56; - 26 Aug 2023
Viewed by 2499
RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types [...] Read more.
RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies. Full article
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