Supplementary data Tables 1-3.

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.

严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）是新型冠状病毒肺炎（COVID-19）的致病病原体，已知其5'非翻译区（5' untranslated region, 5'UTR）、3'非翻译区（3' untranslated region, 3'UTR）以及开放阅读框1a与开放阅读框1b（ORF1a和ORF1b）之间的移码刺激元件均存在二级结构。为鉴定出其他携带保守结构的基因组区域，本研究以与新冠病毒相关的冠状病毒的多序列比对结果作为分析起点。我们采用了一套专为识别非编码RNA（non-coding RNA, ncRNA）元件而开发的计算分析流程，该流程整合了三种不同的RNA结构预测方法。最终我们共鉴定出40个可能存在RNA结构的基因组区域，其中10个区域在我们使用的三类预测工具中得到了三方共识的亚结构预测结果。我们对该流程内的各预测工具开展了内部比对，并与四项已发表的SARS-CoV-2结构数据集进行了跨数据集对比。尽管不同方法在精确结构的判定上一致性有限，但各类预测工具似乎在病毒基因组中可能存在结构的区域上达成了一定共识。通过对比并整合多种计算方法，我们能够预测出最有可能形成RNA结构的基因组区域，以及对应的潜在结构或结构集合。这些预测结果可用于指导病毒监测、防控措施或治疗相关研究。本研究使用的数据集与脚本可在https://doi.org/10.5281/zenodo.8298680获取。