Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis.
SARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multipl...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298164&type=printable |
| _version_ | 1827287747114565632 |
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| author | Alison Ziesel Hosna Jabbari |
| author_facet | Alison Ziesel Hosna Jabbari |
| author_sort | Alison Ziesel |
| collection | DOAJ |
| description | SARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multiple sequence alignment with related coronaviruses as a starting point. We applied a computational pipeline developed for identifying non-coding RNA elements. Our pipeline employed three different RNA structural prediction approaches. We identified forty genomic regions likely to harbor structures, with ten of them showing three-way consensus substructure predictions among our predictive utilities. We conducted intracomparisons of the predictive utilities within the pipeline and intercomparisons with four previously published SARS-CoV-2 structural datasets. While there was limited agreement on the precise structure, different approaches seemed to converge on regions likely to contain structures in the viral genome. By comparing and combining various computational approaches, we can predict regions most likely to form structures, as well as a probable structure or ensemble of structures. These predictions can be used to guide surveillance, prophylactic measures, or therapeutic efforts. Data and scripts employed in this study may be found at https://doi.org/10.5281/zenodo.8298680. |
| first_indexed | 2024-04-24T11:18:17Z |
| format | Article |
| id | doaj.art-92d99490f0b14df396282a2e4e2e5d8c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-24T11:18:17Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-92d99490f0b14df396282a2e4e2e5d8c2024-04-11T05:31:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01194e029816410.1371/journal.pone.0298164Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis.Alison ZieselHosna JabbariSARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multiple sequence alignment with related coronaviruses as a starting point. We applied a computational pipeline developed for identifying non-coding RNA elements. Our pipeline employed three different RNA structural prediction approaches. We identified forty genomic regions likely to harbor structures, with ten of them showing three-way consensus substructure predictions among our predictive utilities. We conducted intracomparisons of the predictive utilities within the pipeline and intercomparisons with four previously published SARS-CoV-2 structural datasets. While there was limited agreement on the precise structure, different approaches seemed to converge on regions likely to contain structures in the viral genome. By comparing and combining various computational approaches, we can predict regions most likely to form structures, as well as a probable structure or ensemble of structures. These predictions can be used to guide surveillance, prophylactic measures, or therapeutic efforts. Data and scripts employed in this study may be found at https://doi.org/10.5281/zenodo.8298680.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298164&type=printable |
| spellingShingle | Alison Ziesel Hosna Jabbari Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. PLoS ONE |
| title | Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. |
| title_full | Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. |
| title_fullStr | Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. |
| title_full_unstemmed | Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. |
| title_short | Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis. |
| title_sort | unveiling hidden structural patterns in the sars cov 2 genome computational insights and comparative analysis |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298164&type=printable |
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