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Backofen R, Gorodkin J, Hofacker IL, Stadler PF. Comparative RNA Genomics. Methods Mol Biol 2024; 2802:347-393. [PMID: 38819565 DOI: 10.1007/978-1-0716-3838-5_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.
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Affiliation(s)
- Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
| | - Jan Gorodkin
- Center for Non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Ivo L Hofacker
- Institute for Theoretical Chemistry, University of Vienna, Wien, Austria
- Bioinformatics and Computational Biology research group, University of Vienna, Vienna, Austria
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
- Universidad National de Colombia, Bogotá, Colombia.
- Institute for Theoretical Chemistry, University of Vienna, Wien, Austria.
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.
- Santa Fe Institute, Santa Fe, NM, USA.
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2
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Van Damme R, Li K, Zhang M, Bai J, Lee WH, Yesselman JD, Lu Z, Velema WA. Chemical reversible crosslinking enables measurement of RNA 3D distances and alternative conformations in cells. Nat Commun 2022; 13:911. [PMID: 35177610 PMCID: PMC8854666 DOI: 10.1038/s41467-022-28602-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Three-dimensional (3D) structures dictate the functions of RNA molecules in a wide variety of biological processes. However, direct determination of RNA 3D structures in vivo is difficult due to their large sizes, conformational heterogeneity, and dynamics. Here we present a method, Spatial 2'-Hydroxyl Acylation Reversible Crosslinking (SHARC), which uses chemical crosslinkers of defined lengths to measure distances between nucleotides in cellular RNA. Integrating crosslinking, exonuclease (exo) trimming, proximity ligation, and high throughput sequencing, SHARC enables transcriptome-wide tertiary structure contact maps at high accuracy and precision, revealing heterogeneous RNA structures and interactions. SHARC data provide constraints that improves Rosetta-based RNA 3D structure modeling at near-nanometer resolution. Integrating SHARC-exo with other crosslinking-based methods, we discover compact folding of the 7SK RNA, a critical regulator of transcriptional elongation. These results establish a strategy for measuring RNA 3D distances and alternative conformations in their native cellular context.
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Affiliation(s)
- Ryan Van Damme
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Kongpan Li
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Minjie Zhang
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jianhui Bai
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Wilson H Lee
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Joseph D Yesselman
- Department of Chemistry, University of Nebraska-Lincoln, 832A Hamilton Hall, Lincoln, NE, 68588, USA
| | - Zhipeng Lu
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA.
| | - Willem A Velema
- Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen, The Netherlands.
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3
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Becquey L, Angel E, Tahi F. BiORSEO: a bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules. Bioinformatics 2020; 36:2451-2457. [DOI: 10.1093/bioinformatics/btz962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 11/15/2019] [Accepted: 01/02/2020] [Indexed: 11/12/2022] Open
Abstract
Abstract
Motivation
RNA loops have been modelled and clustered from solved 3D structures into ordered collections of recurrent non-canonical interactions called ‘RNA modules’, available in databases. This work explores what information from such modules can be used to improve secondary structure prediction. We propose a bi-objective method for predicting RNA secondary structures by minimizing both an energy-based and a knowledge-based potential. The tool, called BiORSEO, outputs secondary structures corresponding to the optimal solutions from the Pareto set.
Results
We compare several approaches to predict secondary structures using inserted RNA modules information: two module data sources, Rna3Dmotif and the RNA 3D Motif Atlas, and different ways to score the module insertions: module size, module complexity or module probability according to models like JAR3D and BayesPairing. We benchmark them against a large set of known secondary structures, including some state-of-the-art tools, and comment on the usefulness of the half physics-based, half data-based approach.
Availability and implementation
The software is available for download on the EvryRNA website, as well as the datasets.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Louis Becquey
- Université Paris-Saclay, Univ Evry, IBISC, 91020, Evry, France
| | - Eric Angel
- Université Paris-Saclay, Univ Evry, IBISC, 91020, Evry, France
| | - Fariza Tahi
- Université Paris-Saclay, Univ Evry, IBISC, 91020, Evry, France
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Sarrazin-Gendron R, Reinharz V, Oliver CG, Moitessier N, Waldispühl J. Automated, customizable and efficient identification of 3D base pair modules with BayesPairing. Nucleic Acids Res 2019; 47:3321-3332. [PMID: 30828711 PMCID: PMC6468301 DOI: 10.1093/nar/gkz102] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 02/06/2019] [Accepted: 02/28/2019] [Indexed: 12/12/2022] Open
Abstract
RNA structures possess multiple levels of structural organization. A secondary structure, made of Watson–Crick helices connected by loops, forms a scaffold for the tertiary structure. The 3D structures adopted by these loops are therefore critical determinants shaping the global 3D architecture. Earlier studies showed that these local 3D structures can be described as conserved sets of ordered non-Watson–Crick base pairs called RNA structural modules. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in the module databases. We present BayesPairing, an automated, efficient and customizable tool for (i) building Bayesian networks representing RNA 3D modules and (ii) rapid identification of 3D modules in sequences. BayesPairing uses a flexible definition of RNA 3D modules that allows us to consider complex architectures such as multi-branched loops and features multiple algorithmic improvements. We benchmarked our methods using cross-validation techniques on 3409 RNA chains and show that BayesPairing achieves up to ∼70% identification accuracy on module positions and base pair interactions. BayesPairing can handle a broader range of motifs (versatility) and offers considerable running time improvements (efficiency), opening the door to a broad range of large-scale applications.
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Affiliation(s)
| | - Vladimir Reinharz
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, Republic of Korea
| | - Carlos G Oliver
- School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada
| | - Nicolas Moitessier
- Department of Chemistry, McGill University, Montreal, QC H3A 0B8, Canada
| | - Jérôme Waldispühl
- School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada
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Lim CS, Brown CM. Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs. Front Microbiol 2018; 8:2582. [PMID: 29354101 PMCID: PMC5758548 DOI: 10.3389/fmicb.2017.02582] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 12/11/2017] [Indexed: 12/14/2022] Open
Abstract
Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.
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Affiliation(s)
- Chun Shen Lim
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Chris M Brown
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
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Abstract
Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.
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Affiliation(s)
- Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.,Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Ivo L Hofacker
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.,Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria.,Bioinformatics and Computational Biology Research Group, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria
| | - Peter F Stadler
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark. .,Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. .,Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. .,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. .,Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany. .,Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
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Abstract
Biological functions of RNA molecules are dependent upon sustained specific three-dimensional (3D) structures of RNA, with or without the help of proteins. Understanding of RNA structure is frequently based on 2D structures, which describe only the Watson-Crick (WC) base pairs. Here, we hierarchically review the structural elements of RNA and how they contribute to RNA 3D structure. We focus our analysis on the non-WC base pairs and on RNA modules. Several computer programs have now been designed to predict RNA modules. We describe the RNA-Puzzles initiative, which is a community-wide, blind assessment of RNA 3D structure prediction programs to determine the capabilities and bottlenecks of current predictions. The assessment metrics used in RNA-Puzzles are briefly described. The detection of RNA 3D modules from sequence data and their automatic implementation belong to the current challenges in RNA 3D structure prediction.
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Affiliation(s)
- Zhichao Miao
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, 67000 Strasbourg, France; ,
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, 67000 Strasbourg, France; ,
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