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Bai H, Ata G, Sun Q, Rahman SU, Tao S. Natural selection pressure exerted on "Silent" mutations during the evolution of SARS-CoV-2: Evidence from codon usage and RNA structure. Virus Res 2023; 323:198966. [PMID: 36244617 PMCID: PMC9561399 DOI: 10.1016/j.virusres.2022.198966] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 01/25/2023]
Abstract
From the first emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) till now, multiple mutations that caused synonymous and nonsynonymous substitutions have accumulated. Among them, synonymous substitutions were regarded as "silent" mutations that received less attention than nonsynonymous substitutions that cause amino acid variations. However, the importance of synonymous substitutions can not be neglected. This research focuses on synonymous substitutions on SARS-CoV-2 and proves that synonymous substitutions were under purifying selection in its evolution. The evidence of purifying selection is provided by comparing the mutation number per site in coding and non-coding regions. We then study the two forces of purifying selection: synonymous codon usage and RNA secondary structure. Results show that the codon usage optimization leads to an adapted codon usage towards humans. Furthermore, our results show that the maintenance of RNA secondary structure causes the purifying of synonymous substitutions in the structural region. These results explain the selection pressure on synonymous substitutions during the evolution of SARS-CoV-2.
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Affiliation(s)
- Haoxiang Bai
- College of Life Sciences, Northwest A&F University, Yangling, China; Bioinformatics Center, Northwest A&F University, Yangling, China
| | - Galal Ata
- College of Life Sciences, Northwest A&F University, Yangling, China; Bioinformatics Center, Northwest A&F University, Yangling, China
| | - Qing Sun
- College of Life Sciences, Northwest A&F University, Yangling, China; Bioinformatics Center, Northwest A&F University, Yangling, China
| | - Siddiq Ur Rahman
- Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak, Khyber Pakhtunkhwa, Pakistan
| | - Shiheng Tao
- College of Life Sciences, Northwest A&F University, Yangling, China; Bioinformatics Center, Northwest A&F University, Yangling, China.
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2
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Yu H, Qi Y, Ding Y. Deep Learning in RNA Structure Studies. Front Mol Biosci 2022; 9:869601. [PMID: 35677883 PMCID: PMC9168262 DOI: 10.3389/fmolb.2022.869601] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.
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Affiliation(s)
- Haopeng Yu
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom
| | | | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom
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3
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Li P, Zhou X, Xu K, Zhang QC. RASP: an atlas of transcriptome-wide RNA secondary structure probing data. Nucleic Acids Res 2021; 49:D183-D191. [PMID: 33068412 PMCID: PMC7779053 DOI: 10.1093/nar/gkaa880] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/13/2020] [Accepted: 09/26/2020] [Indexed: 02/06/2023] Open
Abstract
RNA molecules fold into complex structures that are important across many biological processes. Recent technological developments have enabled transcriptome-wide probing of RNA secondary structure using nucleases and chemical modifiers. These approaches have been widely applied to capture RNA secondary structure in many studies, but gathering and presenting such data from very different technologies in a comprehensive and accessible way has been challenging. Existing RNA structure probing databases usually focus on low-throughput or very specific datasets. Here, we present a comprehensive RNA structure probing database called RASP (RNA Atlas of Structure Probing) by collecting 161 deduplicated transcriptome-wide RNA secondary structure probing datasets from 38 papers. RASP covers 18 species across animals, plants, bacteria, fungi, and also viruses, and categorizes 18 experimental methods including DMS-seq, SHAPE-Seq, SHAPE-MaP, and icSHAPE, etc. Specially, RASP curates the up-to-date datasets of several RNA secondary structure probing studies for the RNA genome of SARS-CoV-2, the RNA virus that caused the on-going COVID-19 pandemic. RASP also provides a user-friendly interface to query, browse, and visualize RNA structure profiles, offering a shortcut to accessing RNA secondary structures grounded in experimental data. The database is freely available at http://rasp.zhanglab.net.
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MESH Headings
- Animals
- COVID-19/epidemiology
- COVID-19/prevention & control
- COVID-19/virology
- Computational Biology/methods
- Computational Biology/statistics & numerical data
- Databases, Genetic/statistics & numerical data
- Genome, Viral/genetics
- High-Throughput Nucleotide Sequencing/methods
- High-Throughput Nucleotide Sequencing/statistics & numerical data
- Humans
- Nucleic Acid Conformation
- Pandemics
- RNA/chemistry
- RNA/genetics
- RNA Probes/genetics
- RNA, Bacterial/chemistry
- RNA, Bacterial/genetics
- RNA, Fungal/chemistry
- RNA, Fungal/genetics
- RNA, Plant/chemistry
- RNA, Plant/genetics
- RNA, Viral/chemistry
- RNA, Viral/genetics
- SARS-CoV-2/genetics
- SARS-CoV-2/physiology
- Transcriptome
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Affiliation(s)
- Pan Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Xiaolin Zhou
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China
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4
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Wirecki TK, Merdas K, Bernat A, Boniecki MJ, Bujnicki JM, Stefaniak F. RNAProbe: a web server for normalization and analysis of RNA structure probing data. Nucleic Acids Res 2020; 48:W292-W299. [PMID: 32504492 PMCID: PMC7319577 DOI: 10.1093/nar/gkaa396] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/02/2020] [Accepted: 06/05/2020] [Indexed: 02/03/2023] Open
Abstract
RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.
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Affiliation(s)
- Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Katarzyna Merdas
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Agata Bernat
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Michał J Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.,Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, PL-61-614 Poznan, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
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