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 (7 papers)

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Research

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Open AccessArticle RNA Structure Elements Conserved between Mouse and 59 Other Vertebrates
Received: 15 May 2018 / Revised: 25 July 2018 / Accepted: 27 July 2018 / Published: 1 August 2018
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Abstract
In this work, we present a computational screen conducted for functional RNA structures, resulting in over 100,000 conserved RNA structure elements found in alignments of mouse (mm10) against 59 other vertebrates. We explicitly included masked repeat regions to explore the potential of transposable
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In this work, we present a computational screen conducted for functional RNA structures, resulting in over 100,000 conserved RNA structure elements found in alignments of mouse (mm10) against 59 other vertebrates. We explicitly included masked repeat regions to explore the potential of transposable elements and low-complexity regions to give rise to regulatory RNA elements. In our analysis pipeline, we implemented a four-step procedure: (i) we screened genome-wide alignments for potential structure elements using RNAz-2, (ii) realigned and refined candidate loci with LocARNA-P, (iii) scored candidates again with RNAz-2 in structure alignment mode, and (iv) searched for additional homologous loci in mouse genome that were not covered by genome alignments. The 3’-untranslated regions (3’-UTRs) of protein-coding genes and small noncoding RNAs are enriched for structures, while coding sequences are depleted. Repeat-associated loci make up about 95% of the homologous loci identified and are, as expected, predominantly found in intronic and intergenic regions. Nevertheless, we report the structure elements enriched in specific genome elements, such as 3’-UTRs and long noncoding RNAs (lncRNAs). We provide full access to our results via a custom UCSC genome browser trackhub freely available on our website (http://rna.tbi.univie.ac.at/trackhubs/#RNAz). Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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Open AccessArticle TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes
Received: 15 May 2018 / Revised: 17 July 2018 / Accepted: 18 July 2018 / Published: 26 July 2018
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Abstract
The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority
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The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority of the genomes of Saccharomycotina. This is a consequence of sequences that evolve rapidly at nucleotide level, are subject to large variations in size, and are highly plastic with respect to their secondary structures. Here, we report on a survey that was aimed at closing this gap in RNA annotation. Despite considerable efforts and the combination of a variety of different methods, it was only partially successful. While 27 new telomerase RNAs were identified, we had to restrict our efforts to the subgroup Saccharomycetacea because even this narrow subgroup was diverse enough to require different search models for different phylogenetic subgroups. More distant branches of the Saccharomycotina remain without annotated telomerase RNA. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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Open AccessFeature PaperArticle Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs
Received: 7 May 2018 / Revised: 26 June 2018 / Accepted: 26 June 2018 / Published: 25 July 2018
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Abstract
RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning.
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RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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Open AccessArticle An Evolutionary Mechanism for the Generation of Competing RNA Structures Associated with Mutually Exclusive Exons
Received: 10 May 2018 / Revised: 6 July 2018 / Accepted: 13 July 2018 / Published: 17 July 2018
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Abstract
Alternative splicing is a commonly-used mechanism of diversifying gene products. Mutually exclusive exons (MXE) represent a particular type of alternative splicing, in which one and only one exon from an array is included in the mature RNA. A number of genes with MXE
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Alternative splicing is a commonly-used mechanism of diversifying gene products. Mutually exclusive exons (MXE) represent a particular type of alternative splicing, in which one and only one exon from an array is included in the mature RNA. A number of genes with MXE do so by using a mechanism that depends on RNA structure. Transcripts of these genes contain multiple sites called selector sequences that are all complementary to a regulatory element called the docking site; only one of the competing base pairings can form at a time, which exposes one exon from the cluster to the spliceosome. MXE tend to have similar lengths and sequence content and are believed to originate through tandem genomic duplications. Here, we report that pre-mRNAs of this class of exons have an increased capacity to fold into competing secondary structures. We propose an evolutionary mechanism for the generation of such structures via duplications that affect not only exons, but also their adjacent introns with stem-loop structures. If one of the two arms of a stem-loop is duplicated, it will generate two selector sequences that compete for the same docking site, a pattern that is associated with MXE splicing. A similar partial duplication of two independent stem-loops produces a pattern that is consistent with the so-called bidirectional pairing model. These models explain why tandem exon duplications frequently result in mutually exclusive splicing. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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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
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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 Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes
Received: 14 May 2018 / Revised: 26 July 2018 / Accepted: 21 August 2018 / Published: 25 August 2018
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Abstract
RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of
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RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods. Full article
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
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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
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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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