Editorial

3 pages, 138 KiB

Open AccessEditorial

Special Issue: Computational Analysis of RNA Structure and Function

by Jan Gorodkin

Genes 2019, 10(1), 55; https://doi.org/10.3390/genes10010055 - 16 Jan 2019

Cited by 1 | Viewed by 2878

RNA structure often plays a key role in determining the function of non-coding and coding transcripts [...] Full article

(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)

Research

Jump to: Editorial, Review

17 pages, 1977 KiB

Open AccessArticle

Multiple Sequence Alignments Enhance Boundary Definition of RNA Structures

by Radhakrishnan Sabarinathan, Christian Anthon, Jan Gorodkin and Stefan E. Seemann

Genes 2018, 9(12), 604; https://doi.org/10.3390/genes9120604 - 04 Dec 2018

Cited by 2 | Viewed by 3728

Abstract

Self-contained structured domains of RNA sequences have often distinct molecular functions. Determining the boundaries of structured domains of a non-coding RNA (ncRNA) is needed for many ncRNA gene finder programs that predict RNA secondary structures in aligned genomes because these methods do not [...] Read more.

Self-contained structured domains of RNA sequences have often distinct molecular functions. Determining the boundaries of structured domains of a non-coding RNA (ncRNA) is needed for many ncRNA gene finder programs that predict RNA secondary structures in aligned genomes because these methods do not necessarily provide precise information about the boundaries or the location of the RNA structure inside the predicted ncRNA. Even without having a structure prediction, it is of interest to search for structured domains, such as for finding common RNA motifs in RNA-protein binding assays. The precise definition of the boundaries are essential for downstream analyses such as RNA structure modelling, e.g., through covariance models, and RNA structure clustering for the search of common motifs. Such efforts have so far been focused on single sequences, thus here we present a comparison for boundary definition between single sequence and multiple sequence alignments. We also present a novel approach, named RNAbound, for finding the boundaries that are based on probabilities of evolutionarily conserved base pairings. We tested the performance of two different methods on a limited number of Rfam families using the annotated structured RNA regions in the human genome and their multiple sequence alignments created from 14 species. The results show that multiple sequence alignments improve the boundary prediction for branched structures compared to single sequences independent of the chosen method. The actual performance of the two methods differs on single hairpin structures and branched structures. For the RNA families with branched structures, including transfer RNA (tRNA) and small nucleolar RNAs (snoRNAs), RNAbound improves the boundary predictions using multiple sequence alignments to median differences of −6 and −11.5 nucleotides (nts) for left and right boundary, respectively (window size of 200 nts). Full article

(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)

► Show Figures

Figure 1

11 pages, 600 KiB

Open AccessArticle

WebCircRNA: Classifying the Circular RNA Potential of Coding and Noncoding RNA

by Xiaoyong Pan, Kai Xiong, Christian Anthon, Poul Hyttel, Kristine K. Freude, Lars Juhl Jensen and Jan Gorodkin

Genes 2018, 9(11), 536; https://doi.org/10.3390/genes9110536 - 06 Nov 2018

Cited by 22 | Viewed by 4440

Abstract

Circular RNAs (circRNAs) are increasingly recognized to play crucial roles in post-transcriptional gene regulation including functioning as microRNA (miRNA) sponges or as wide-spread regulators, for example in stem cell differentiation. It is therefore highly relevant to identify if a transcript of interest can [...] Read more.

Circular RNAs (circRNAs) are increasingly recognized to play crucial roles in post-transcriptional gene regulation including functioning as microRNA (miRNA) sponges or as wide-spread regulators, for example in stem cell differentiation. It is therefore highly relevant to identify if a transcript of interest can also function as a circRNA. Here, we present a user-friendly web server that predicts if coding and noncoding RNAs have circRNA isoforms and whether circRNAs are expressed in stem cells. The predictions are made by random forest models using sequence-derived features as input. The output scores are converted to fractiles, which are used to assess the circRNA and stem cell potential. The performances of the three models are reported as the area under the receiver operating characteristic (ROC) curve and are 0.82 for coding genes, 0.89 for long noncoding RNAs (lncRNAs) and 0.72 for stem cell expression. We present WebCircRNA for quick evaluation of human genes and transcripts for their circRNA potential, which can be essential in several contexts. Full article

(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)

► Show Figures

Figure 1

19 pages, 683 KiB

Open AccessArticle

RNA Structure Elements Conserved between Mouse and 59 Other Vertebrates

by Bernhard C. Thiel, Roman Ochsenreiter, Veerendra P. Gadekar, Andrea Tanzer and Ivo L. Hofacker

Genes 2018, 9(8), 392; https://doi.org/10.3390/genes9080392 - 01 Aug 2018

Cited by 14 | Viewed by 5018

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 [...] Read more.

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)

► Show Figures

Figure 1

16 pages, 1637 KiB

Open AccessArticle

TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes

by Maria Waldl, Bernhard C. Thiel, Roman Ochsenreiter, Alexander Holzenleiter, João Victor De Araujo Oliveira, Maria Emília M. T. Walter, Michael T. Wolfinger and Peter F. Stadler

Genes 2018, 9(8), 372; https://doi.org/10.3390/genes9080372 - 26 Jul 2018

Cited by 12 | Viewed by 5256

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 [...] Read more.

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)

► Show Figures

Figure 1

21 pages, 7897 KiB

Open AccessFeature PaperArticle

Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs

by Swati Jain, Cigdem S. Bayrak, Louis Petingi and Tamar Schlick

Genes 2018, 9(8), 371; https://doi.org/10.3390/genes9080371 - 25 Jul 2018

Cited by 12 | Viewed by 4765

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. [...] Read more.

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)

► Show Figures

Figure 1

13 pages, 472 KiB

Open AccessArticle

An Evolutionary Mechanism for the Generation of Competing RNA Structures Associated with Mutually Exclusive Exons

by Timofei M. Ivanov and Dmitri D. Pervouchine

Genes 2018, 9(7), 356; https://doi.org/10.3390/genes9070356 - 17 Jul 2018

Cited by 12 | Viewed by 3233

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 [...] Read more.

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)

► Show Figures

Figure 1

24 pages, 1440 KiB

Open AccessArticle

Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures

by Pierce Radecki, Mirko Ledda and Sharon Aviran

Genes 2018, 9(6), 300; https://doi.org/10.3390/genes9060300 - 14 Jun 2018

Cited by 7 | Viewed by 4423

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)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

21 pages, 2383 KiB

Open AccessReview

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

by Chandran Nithin, Pritha Ghosh and Janusz M. Bujnicki

Genes 2018, 9(9), 432; https://doi.org/10.3390/genes9090432 - 25 Aug 2018

Cited by 31 | Viewed by 10948

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 [...] Read more.

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)

► Show Figures

Figure 1

9 pages, 224 KiB

Open AccessReview

Towards Long-Range RNA Structure Prediction in Eukaryotic Genes

by Dmitri D. Pervouchine

Genes 2018, 9(6), 302; https://doi.org/10.3390/genes9060302 - 15 Jun 2018

Cited by 19 | Viewed by 4428

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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Computational Analysis of RNA Structure and Function

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (10 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI