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Ballarino M, Pepe G, Helmer-Citterich M, Palma A. Exploring the landscape of tools and resources for the analysis of long non-coding RNAs. Comput Struct Biotechnol J 2023; 21:4706-4716. [PMID: 37841333 PMCID: PMC10568309 DOI: 10.1016/j.csbj.2023.09.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.
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Affiliation(s)
- Monica Ballarino
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Manuela Helmer-Citterich
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
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2
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Sampedro Vallina N, McRae EKS, Geary C, Andersen ES. An RNA Paranemic Crossover Triangle as A 3D Module for Cotranscriptional Nanoassembly. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2204651. [PMID: 36526605 DOI: 10.1002/smll.202204651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/15/2022] [Indexed: 05/28/2023]
Abstract
RNA nanotechnology takes advantage of structural modularity to build self-assembling nano-architectures with applications in medicine and synthetic biology. The use of paranemic motifs, that form without unfolding existing secondary structure, allows for the creation of RNA nanostructures that are compatible with cotranscriptional folding in vitro and in vivo. In previous work, kissing-loop (KL) motifs have been widely used to design RNA nanostructures that fold cotranscriptionally. However, the paranemic crossover (PX) motif has not yet been explored for cotranscriptional RNA origami architectures and information about the structural geometry of the motif is unknown. Here, a six base pair-wide paranemic RNA interaction that arranges double helices in a perpendicular manner is introduced, allowing for the generation of a new and versatile building block: the paranemic-crossover triangle (PXT). The PXT is self-assembled by cotranscriptional folding and characterized by cryogenic electron microscopy, revealing for the first time an RNA PX interaction in high structural detail. The PXT is used as a building block for the construction of multimers that form filaments and rings and a duplicated PXT motif is used as a building block to self-assemble cubic structures, demonstrating the PXT as a rigid self-folding domain for the development of wireframe RNA origami architectures.
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Affiliation(s)
- Néstor Sampedro Vallina
- Interdisciplinary Nanoscience Center (iNANO); Gustav Wieds Vej 14, Aarhus University, Aarhus, DK-8000, Denmark
| | - Ewan K S McRae
- Interdisciplinary Nanoscience Center (iNANO); Gustav Wieds Vej 14, Aarhus University, Aarhus, DK-8000, Denmark
| | - Cody Geary
- Interdisciplinary Nanoscience Center (iNANO); Gustav Wieds Vej 14, Aarhus University, Aarhus, DK-8000, Denmark
| | - Ebbe Sloth Andersen
- Interdisciplinary Nanoscience Center (iNANO); Gustav Wieds Vej 14, Aarhus University, Aarhus, DK-8000, Denmark
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3
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Bruni F, Proctor-Kent Y, Lightowlers RN, Chrzanowska-Lightowlers ZM. Messenger RNA delivery to mitoribosomes - hints from a bacterial toxin. FEBS J 2020; 288:437-451. [PMID: 32329962 PMCID: PMC7891357 DOI: 10.1111/febs.15342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 04/06/2020] [Accepted: 04/21/2020] [Indexed: 11/28/2022]
Abstract
In mammalian mitochondria, messenger RNA is processed and matured from large primary transcripts in structures known as RNA granules. The identity of the factors and process transferring the matured mRNA to the mitoribosome for translation is unclear. Nascent mature transcripts are believed to associate initially with the small mitoribosomal subunit prior to recruitment of the large subunit to form the translationally active monosome. When the small subunit fails to assemble, however, the stability of mt‐mRNA is only marginally affected, and under these conditions, the LRPPRC/SLIRP RNA‐binding complex has been implicated in maintaining mt‐mRNA stability. Here, we exploit the activity of a bacterial ribotoxin, VapC20, to show that in the absence of the large mitoribosomal subunit, mt‐mRNA species are selectively lost. Further, if the small subunit is also depleted, the mt‐mRNA levels are recovered. As a consequence of these data, we suggest a natural pathway for loading processed mt‐mRNA onto the mitoribosome.
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Affiliation(s)
- Francesco Bruni
- The Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, UK.,Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari Aldo Moro, Italy
| | - Yasmin Proctor-Kent
- The Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, UK
| | - Robert N Lightowlers
- The Wellcome Centre for Mitochondrial Research, Institute for Cell and Molecular Biosciences, Newcastle University, UK
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Wright ES. RNAconTest: comparing tools for noncoding RNA multiple sequence alignment based on structural consistency. RNA (NEW YORK, N.Y.) 2020; 26:531-540. [PMID: 32005745 PMCID: PMC7161358 DOI: 10.1261/rna.073015.119] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/28/2020] [Indexed: 05/05/2023]
Abstract
The importance of noncoding RNA sequences has become increasingly clear over the past decade. New RNA families are often detected and analyzed using comparative methods based on multiple sequence alignments. Accordingly, a number of programs have been developed for aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for these tasks remain unclear because existing benchmarks contain too few sequences belonging to only a small number of RNA families. RNAconTest (RNA consistency test) is a new benchmarking approach relying on the observation that secondary structure is often conserved across highly divergent RNA sequences from the same family. RNAconTest scores multiple sequence alignments based on the level of consistency among known secondary structures belonging to reference sequences in their output alignment. Similarly, consensus secondary structure predictions are scored according to their agreement with one or more known structures in a family. Comparing the performance of 10 popular alignment programs using RNAconTest revealed that DAFS, DECIPHER, LocARNA, and MAFFT created the most structurally consistent alignments. The best consensus secondary structure predictions were generated by DAFS and LocARNA (via RNAalifold). Many of the methods specific to noncoding RNAs exhibited poor scalability as the number or length of input sequences increased, and several programs displayed substantial declines in score as more sequences were aligned. Overall, RNAconTest provides a means of testing and improving tools for comparative RNA analysis, as well as highlighting the best available approaches. RNAconTest is available from the DECIPHER website (http://DECIPHER.codes/Downloads.html).
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Affiliation(s)
- Erik S Wright
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15219, USA
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5
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Arslan AN, Anandan J, Fry E, Monschke K, Ganneboina N, Bowerman J. Efficient RNA structure comparison algorithms. J Bioinform Comput Biol 2017; 15:1740009. [DOI: 10.1142/s0219720017400091] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.
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Affiliation(s)
- Abdullah N. Arslan
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
| | - Jithendar Anandan
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
| | - Eric Fry
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
| | - Keith Monschke
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
| | - Nitin Ganneboina
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
| | - Jason Bowerman
- Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
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Li Y, Shi X, Liang Y, Xie J, Zhang Y, Ma Q. RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation. BMC Bioinformatics 2017; 18:51. [PMID: 28109252 PMCID: PMC5251234 DOI: 10.1186/s12859-017-1481-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 01/10/2017] [Indexed: 01/10/2023] Open
Abstract
Background RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. Results An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. Conclusion RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ying Li
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China
| | - Xiaohu Shi
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China
| | - Yanchun Liang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China.,Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, 519041, China
| | - Juan Xie
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, 57007, USA.,Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, 57007, USA.,BioSNTR, Brookings, SD, USA
| | - Yu Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China. .,Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China.
| | - Qin Ma
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, 57007, USA. .,Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, 57007, USA. .,BioSNTR, Brookings, SD, USA.
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7
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Nguyen MN, Sim AYL, Wan Y, Madhusudhan MS, Verma C. Topology independent comparison of RNA 3D structures using the CLICK algorithm. Nucleic Acids Res 2016; 45:e5. [PMID: 27634929 PMCID: PMC5741206 DOI: 10.1093/nar/gkw819] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 09/01/2016] [Accepted: 09/02/2016] [Indexed: 01/15/2023] Open
Abstract
RNA molecules are attractive therapeutic targets because non-coding RNA molecules have increasingly been found to play key regulatory roles in the cell. Comparing and classifying RNA 3D structures yields unique insights into RNA evolution and function. With the rapid increase in the number of atomic-resolution RNA structures, it is crucial to have effective tools to classify RNA structures and to investigate them for structural similarities at different resolutions. We previously developed the algorithm CLICK to superimpose a pair of protein 3D structures by clique matching and 3D least squares fitting. In this study, we extend and optimize the CLICK algorithm to superimpose pairs of RNA 3D structures and RNA-protein complexes, independent of the associated topologies. Benchmarking Rclick on four different datasets showed that it is either comparable to or better than other structural alignment methods in terms of the extent of structural overlaps. Rclick also recognizes conformational changes between RNA structures and produces complementary alignments to maximize the extent of detectable similarity. Applying Rclick to study Ribonuclease III protein correctly aligned the RNA binding sites of RNAse III with its substrate. Rclick can be further extended to identify ligand-binding pockets in RNA. A web server is developed at http://mspc.bii.a-star.edu.sg/minhn/rclick.html.
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Affiliation(s)
- Minh N Nguyen
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Adelene Y L Sim
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Yue Wan
- Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore 138672
| | - M S Madhusudhan
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671.,Indian Institute of Science Education and Research, Pune, India
| | - Chandra Verma
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671.,Department of Biological Sciences, National University of Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore
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