Special Issue "Computational Analysis of RNA Structure and Function"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 April 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Jan Gorodkin

Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Special Issue Information

Dear Colleagues,

RNA, the matter of transcripts, is being intensively studied across all living organisms in numerous ways, ranging from analysis of its structure and folding properties to high-throughput sequencing (HTS) and its applications, including those targeting interactions and structure itself. Indeed, RNA often folds into complex structures central to its function by, which, for example, function through binding to other RNAs and proteins. Hence, the relevance of predicting both RNA structure and RNA interactions does not only concern structure determination of single sequences, but do also addresses analysis of large-scale data sets. Efficient algorithms and implementations are also essential to meet the demand of large-scale applications. Another challenge is that algorithms for RNA structure and interaction analysis are relatively computational expensive, for example when compared to their counterpart of sequence alignments. Furthermore, the vast majority of trait and disease related mutations in higher eukaryotes are located in non-coding regions of the genome and since most of the genome is transcribed into RNA, the mutations hold the potential impact structure and thereby function of the RNA molecules. This Special Issue includes computational strategies for analysis of RNA structure and function covering both algorithmic aspects, as well as bioinformatic analysis large-scale related data sets.

Prof. Dr. Jan Gorodkin
Guest Editor

Manuscript Submission Information

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Keywords

  • RNA structure (2D and 3D)
  • RNA folding and dynamics
  • RNA interactions
  • Comparative structure analysis
  • Analysis of large scale data sets related to RNA structure
  • RNA structure and mutations
  • RNA modification
  • RNA structure and expression

Published Papers (2 papers)

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Research

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Open AccessArticle Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures
Received: 27 April 2018 / Revised: 4 June 2018 / Accepted: 13 June 2018 / Published: 14 June 2018
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Abstract
High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly
[...] Read more.
High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA. We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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Review

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Open AccessReview Towards Long-Range RNA Structure Prediction in Eukaryotic Genes
Received: 27 April 2018 / Revised: 13 June 2018 / Accepted: 13 June 2018 / Published: 15 June 2018
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Abstract
The ability to form an intramolecular structure plays a fundamental role in eukaryotic RNA biogenesis. Proximate regions in the primary transcripts fold into a local secondary structure, which is then hierarchically assembled into a tertiary structure that is stabilized by RNA-binding proteins and
[...] Read more.
The ability to form an intramolecular structure plays a fundamental role in eukaryotic RNA biogenesis. Proximate regions in the primary transcripts fold into a local secondary structure, which is then hierarchically assembled into a tertiary structure that is stabilized by RNA-binding proteins and long-range intramolecular base pairings. While the local RNA structure can be predicted reasonably well for short sequences, long-range structure at the scale of eukaryotic genes remains problematic from the computational standpoint. The aim of this review is to list functional examples of long-range RNA structures, to summarize current comparative methods of structure prediction, and to highlight their advances and limitations in the context of long-range RNA structures. Most comparative methods implement the “first-align-then-fold” principle, i.e., they operate on multiple sequence alignments, while functional RNA structures often reside in non-conserved parts of the primary transcripts. The opposite “first-fold-then-align” approach is currently explored to a much lesser extent. Developing novel methods in both directions will improve the performance of comparative RNA structure analysis and help discover novel long-range structures, their higher-order organization, and RNA–RNA interactions across the transcriptome. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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