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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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2
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Wang H, Lu X, Zheng H, Wang W, Zhang G, Wang S, Lin P, Zhuang Y, Chen C, Chen Q, Qu J, Xu L. RNAsmc: A integrated tool for comparing RNA secondary structure and evaluating allosteric effects. Comput Struct Biotechnol J 2023; 21:965-973. [PMID: 36733704 PMCID: PMC9876829 DOI: 10.1016/j.csbj.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/06/2023] [Accepted: 01/07/2023] [Indexed: 01/11/2023] Open
Abstract
RNA structure plays a crucial role in gene regulation, in RNA stability and the essential biological processes. RNA secondary structure (RSS) motifs are the basic building blocks for investigating the biological mechanisms of structure. Here, we present a strategy for structural motif-based dynamic alignment, namely, RNA secondary-structural motif-comparing (RNAsmc), to identify structural motifs and quantitatively evaluate their underlying molecular functions. RNAsmc also has strong robustness to sequence length, folding protocol and RNA structural profile by chemical probing. Notably, it is also applicable to quantify structural variation in special RNA editing events (SNVs or SNPs, fragment insertion or deletion, etc.). The findings indicate that RNAsmc can uncover the heterogeneity of RNA secondary structure and score for similarities among components, which provides an impetus to cluster RNA families and evaluate allosteric effects. We find that RNAsmc exhibits remarkable detection efficiency for experimentally-derived RiboSNitches. Finally, the pipeline was assembled into an R software package to serve as an automated toolkit to explore, align, and cluster RSS. It is freely available for download at https://CRAN.R-project.org/package=RNAsmc.
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Affiliation(s)
- Hong Wang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Center of Optometry International Innovation of Wenzhou, Eye Valley, Wenzhou 325027, China
| | - Xiaoyan Lu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hewei Zheng
- Wekemo Tech Group Co., Ltd. Shenzhen 518000, China
| | - Wencan Wang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Wenzhou Realdata Medical Research Co., Ltd, Wenzhou 325027, China
| | - Guosi Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Siyu Wang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Peng Lin
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Youyuan Zhuang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Chong Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Qi Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Center of Optometry International Innovation of Wenzhou, Eye Valley, Wenzhou 325027, China
- Corresponding authors at: National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Liangde Xu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Center of Optometry International Innovation of Wenzhou, Eye Valley, Wenzhou 325027, China
- Corresponding authors at: National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
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3
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Zakh R, Churkin A, Totzeck F, Parr M, Tuller T, Etzion O, Dahari H, Roggendorf M, Frishman D, Barash D. A Mathematical Analysis of HDV Genotypes: From Molecules to Cells. MATHEMATICS (BASEL, SWITZERLAND) 2021; 9:2063. [PMID: 34540628 PMCID: PMC8445514 DOI: 10.3390/math9172063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.
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Affiliation(s)
- Rami Zakh
- Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel
| | - Alexander Churkin
- Department of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheva 8410501, Israel
| | - Franziska Totzeck
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| | - Marina Parr
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| | - Tamir Tuller
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel
| | - Ohad Etzion
- Soroka University Medical Center, Ben-Gurion University, Beer-Sheva 8410501, Israel
| | - Harel Dahari
- Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA
| | - Michael Roggendorf
- Institute of Virology, Technische Universität München, 81675 Munich, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| | - Danny Barash
- Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel
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4
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Walter Costa MB, Höner Zu Siederdissen C, Dunjić M, Stadler PF, Nowick K. SSS-test: a novel test for detecting positive selection on RNA secondary structure. BMC Bioinformatics 2019; 20:151. [PMID: 30898084 PMCID: PMC6429701 DOI: 10.1186/s12859-019-2711-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/03/2019] [Indexed: 12/23/2022] Open
Abstract
Background Long non-coding RNAs (lncRNAs) play an important role in regulating gene expression and are thus important for determining phenotypes. Most attempts to measure selection in lncRNAs have focused on the primary sequence. The majority of small RNAs and at least some parts of lncRNAs must fold into specific structures to perform their biological function. Comprehensive assessments of selection acting on RNAs therefore must also encompass structure. Selection pressures acting on the structure of non-coding genes can be detected within multiple sequence alignments. Approaches of this type, however, have so far focused on negative selection. Thus, a computational method for identifying ncRNAs under positive selection is needed. Results We introduce the SSS-test (test for Selection on Secondary Structure) to identify positive selection and thus adaptive evolution. Benchmarks with biological as well as synthetic controls yield coherent signals for both negative and positive selection, demonstrating the functionality of the test. A survey of a lncRNA collection comprising 15,443 families resulted in 110 candidates that appear to be under positive selection in human. In 26 lncRNAs that have been associated with psychiatric disorders we identified local structures that have signs of positive selection in the human lineage. Conclusions It is feasible to assay positive selection acting on RNA secondary structures on a genome-wide scale. The detection of human-specific positive selection in lncRNAs associated with cognitive disorder provides a set of candidate genes for further experimental testing and may provide insights into the evolution of cognitive abilities in humans. Availability The SSS-test and related software is available at: https://github.com/waltercostamb/SSS-test. The databases used in this work are available at: http://www.bioinf.uni-leipzig.de/Software/SSS-test/. Electronic supplementary material The online version of this article (10.1186/s12859-019-2711-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maria Beatriz Walter Costa
- Embrapa Agroenergia, Parque Estação Biológica (PqEB), Asa Norte, Brasília, DF, 70770-901, Brazil. .,Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany.
| | - Christian Höner Zu Siederdissen
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany
| | - Marko Dunjić
- Human Biology Group, Institute for Biology, Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Königin-Luise-Straße 1-3, Berlin, 14195, Germany.,Center for Human Molecular Genetics, Faculty of Biology, University of Belgrade, Studentski trg 16, PO box 43, Belgrade, 11000, Serbia
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig & Competence Center for Scalable Data Services and Solutions Dresden-Leipzig & Leipzig Research Center for Civilization Diseases, University Leipzig, Leipzig, 04107, Germany.,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, 04103, Germany.,Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, Vienna, A-1090, Austria.,Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870, Denmark.,Faculdad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Ciudad Universitaria, Bogotá, D.C., COL-111321, Colombia.,Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Königin-Luise-Straße 1-3, Berlin, 14195, Germany. .,TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany. .,Paul-Flechsig-Institute for Brain Research, University of Leipzig, Liebigstraße 19. Haus C, Leipzig, 04103, Germany. .,Bioinformatics, Faculty of Agricultural Sciences, Institute of Animal Science, University of Hohenheim, Garbenstraße 13, Stuttgart, 70593, Germany.
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5
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Desirò D, Hölzer M, Ibrahim B, Marz M. SilentMutations (SIM): A tool for analyzing long-range RNA-RNA interactions in viral genomes and structured RNAs. Virus Res 2019; 260:135-141. [PMID: 30439394 PMCID: PMC7172452 DOI: 10.1016/j.virusres.2018.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/30/2018] [Accepted: 11/09/2018] [Indexed: 01/28/2023]
Abstract
We developed a tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. Our tool simulates destructive and compensatory mutants of two key regions from a single-stranded RNA. The simulated mutants can be utilized for the combinatorial in vitro analysis of RNA–RNA interactions. We predicted potential mutants for in vitro validation experiments of influenza A virus and hepatitis C virus interactions.
A single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication. In recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions. Here, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA–RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments. SIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions. We applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments. The source code and documentation of SIM are freely available at github.com/desiro/silentMutations.
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Affiliation(s)
- Daniel Desirò
- European Virus Bioinformatics Center, Jena, Germany; RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University, Jena, Germany
| | - Martin Hölzer
- European Virus Bioinformatics Center, Jena, Germany; RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University, Jena, Germany
| | - Bashar Ibrahim
- European Virus Bioinformatics Center, Jena, Germany; Chair of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University, Jena, Germany
| | - Manja Marz
- European Virus Bioinformatics Center, Jena, Germany; RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University, Jena, Germany; Leibniz Institute for Age Research-Fritz Lipmann Institute, Jena, Germany.
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6
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Woods CT, Laederach A. Classification of RNA structure change by 'gazing' at experimental data. Bioinformatics 2018; 33:1647-1655. [PMID: 28130241 PMCID: PMC5447233 DOI: 10.1093/bioinformatics/btx041] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 01/20/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2' Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by 'gel gazing.' SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human 'gazing' by identifying features of the SHAPE profile that human experts agree 'looks' like a riboSNitch. Results We find strong quantitative agreement between experts when RNA scientists 'gaze' at SHAPE data and identify riboSNitches. We identify dynamic time warping and seven other features predictive of the human consensus. The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8. When we analyze 2019 mutant traces for 17 different RNAs, we find that features of the WT SHAPE reactivity allow us to improve thermodynamic structure predictions of riboSNitches. This is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine. Availability and Implementation The classSNitch R package is freely available at http://classsnitch.r-forge.r-project.org . Contact alain@email.unc.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chanin Tolson Woods
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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7
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Churkin A, Weinbrand L, Barash D. Free energy minimization to predict RNA secondary structures and computational RNA design. Methods Mol Biol 2015; 1269:3-16. [PMID: 25577369 DOI: 10.1007/978-1-4939-2291-8_1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.
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Affiliation(s)
- Alexander Churkin
- Department of Computer Science, Ben-Gurion University, 653, Beer-Sheva, 84105, Israel
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8
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Mehedi M, Hoenen T, Robertson S, Ricklefs S, Dolan MA, Taylor T, Falzarano D, Ebihara H, Porcella SF, Feldmann H. Ebola virus RNA editing depends on the primary editing site sequence and an upstream secondary structure. PLoS Pathog 2013; 9:e1003677. [PMID: 24146620 PMCID: PMC3798607 DOI: 10.1371/journal.ppat.1003677] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Accepted: 08/17/2013] [Indexed: 11/18/2022] Open
Abstract
Ebolavirus (EBOV), the causative agent of a severe hemorrhagic fever and a biosafety level 4 pathogen, increases its genome coding capacity by producing multiple transcripts encoding for structural and nonstructural glycoproteins from a single gene. This is achieved through RNA editing, during which non-template adenosine residues are incorporated into the EBOV mRNAs at an editing site encoding for 7 adenosine residues. However, the mechanism of EBOV RNA editing is currently not understood. In this study, we report for the first time that minigenomes containing the glycoprotein gene editing site can undergo RNA editing, thereby eliminating the requirement for a biosafety level 4 laboratory to study EBOV RNA editing. Using a newly developed dual-reporter minigenome, we have characterized the mechanism of EBOV RNA editing, and have identified cis-acting sequences that are required for editing, located between 9 nt upstream and 9 nt downstream of the editing site. Moreover, we show that a secondary structure in the upstream cis-acting sequence plays an important role in RNA editing. EBOV RNA editing is glycoprotein gene-specific, as a stretch encoding for 7 adenosine residues located in the viral polymerase gene did not serve as an editing site, most likely due to an absence of the necessary cis-acting sequences. Finally, the EBOV protein VP30 was identified as a trans-acting factor for RNA editing, constituting a novel function for this protein. Overall, our results provide novel insights into the RNA editing mechanism of EBOV, further understanding of which might result in novel intervention strategies against this viral pathogen.
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Affiliation(s)
- Masfique Mehedi
- Department of Medical Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Thomas Hoenen
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Shelly Robertson
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Stacy Ricklefs
- Research Technology Branch, Genomics Unit, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Michael A. Dolan
- Bioinformatics and Computational Bioscience Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Travis Taylor
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Darryl Falzarano
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Hideki Ebihara
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Stephen F. Porcella
- Research Technology Branch, Genomics Unit, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
| | - Heinz Feldmann
- Department of Medical Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada
- Laboratory of Virology, Rocky Mountain Laboratories, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America
- * E-mail:
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9
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Athanasiadis P, Malousi A, Kouidou S, Maglaveras N. GREMET: an integrative tool for the prediction of mutation effects on gene regulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:214-219. [PMID: 23648050 DOI: 10.1016/j.cmpb.2013.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2012] [Revised: 12/19/2012] [Accepted: 04/02/2013] [Indexed: 06/02/2023]
Abstract
The identification of thousands of mutations yearly has put new challenges to researchers who are interested in fast and effective annotation as well as the prediction of potential implications to the gene regulation mechanisms. This work presents an integrative tool, called GREMET, for the prediction of alterations in gene splicing regulation inferred by mutations of the human genome. GREMET supports the characterization of mutations either single-point or indels with respect to their effect on the splicing potential of the neighboring sequences and the binding strength of auxiliary cis-acting splicing enhancers. In addition, GREMET identifies possible consequences of mutations on the DNA methylation through the disruption or creation of CpG sequences. Besides locus-specific mutations, GREMET performs the analyses on newly identified mutations and provides an easy-to-use Web interface helping researchers to save time in routine mutation analyses. GREMET is freely accessible at: http://kedip.med.auth.gr/biotools/gremet/.
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Affiliation(s)
- Polykarpos Athanasiadis
- Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
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10
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Sabarinathan R, Tafer H, Seemann SE, Hofacker IL, Stadler PF, Gorodkin J. The RNAsnp web server: predicting SNP effects on local RNA secondary structure. Nucleic Acids Res 2013; 41:W475-9. [PMID: 23630321 PMCID: PMC3977658 DOI: 10.1093/nar/gkt291] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The function of many non-coding RNA genes and cis-regulatory elements of messenger RNA largely depends on the structure, which is in turn determined by their sequence. Single nucleotide polymorphisms (SNPs) and other mutations may disrupt the RNA structure, interfere with the molecular function and hence cause a phenotypic effect. RNAsnp is an efficient method to predict the effect of SNPs on local RNA secondary structure based on the RNA folding algorithms implemented in the Vienna RNA package. The SNP effects are quantified in terms of empirical P-values, which, for computational efficiency, are derived from extensive pre-computed tables of distributions of substitution effects as a function of gene length and GC content. Here, we present a web service that not only provides an interface for RNAsnp but also features a graphical output representation. In addition, the web server is connected to a local mirror of the UCSC genome browser database that enables the users to select the genomic sequences for analysis and visualize the results directly in the UCSC genome browser. The RNAsnp web server is freely available at: http://rth.dk/resources/rnasnp/.
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Affiliation(s)
- Radhakrishnan Sabarinathan
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg C, Denmark
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Efficient procedures for the numerical simulation of mid-size RNA kinetics. Algorithms Mol Biol 2012; 7:24. [PMID: 22958879 PMCID: PMC3463434 DOI: 10.1186/1748-7188-7-24] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2009] [Accepted: 08/22/2012] [Indexed: 01/02/2023] Open
Abstract
Motivation Methods for simulating the kinetic folding of RNAs by numerically solving the chemical master equation have been developed since the late 90's, notably the programs Kinfold and Treekin with Barriers that are available in the Vienna RNA package. Our goal is to formulate extensions to the algorithms used, starting from the Gillespie algorithm, that will allow numerical simulations of mid-size (~ 60–150 nt) RNA kinetics in some practical cases where numerous distributions of folding times are desired. These extensions can contribute to analyses and predictions of RNA folding in biologically significant problems. Results By describing in a particular way the reduction of numerical simulations of RNA folding kinetics into the Gillespie stochastic simulation algorithm for chemical reactions, it is possible to formulate extensions to the basic algorithm that will exploit memoization and parallelism for efficient computations. These can be used to advance forward from the small examples demonstrated to larger examples of biological interest. Software The implementation that is described and used for the Gillespie algorithm is freely available by contacting the authors, noting that the efficient procedures suggested may also be applicable along with Vienna's Kinfold.
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Ritz J, Martin JS, Laederach A. Evaluating our ability to predict the structural disruption of RNA by SNPs. BMC Genomics 2012; 13 Suppl 4:S6. [PMID: 22759654 PMCID: PMC3303743 DOI: 10.1186/1471-2164-13-s4-s6] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.
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Affiliation(s)
- Justin Ritz
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
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