{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:10:51Z","timestamp":1772169051616,"version":"3.50.1"},"update-to":[{"DOI":"10.12688\/f1000research.72896.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000}},{"DOI":"10.12688\/f1000research.72896.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000}},{"DOI":"10.12688\/f1000research.72896.3","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000}}],"reference-count":68,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012024","name":"Multimedia University, Malaysia IRFund 2.0","doi-asserted-by":"publisher","award":["MMUI\/210119"],"award-info":[{"award-number":["MMUI\/210119"]}],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.<\/ns3:p>","DOI":"10.12688\/f1000research.72896.4","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T06:20:08Z","timestamp":1726640408000},"page":"1053","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase"],"prefix":"10.12688","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0424-5243","authenticated-orcid":false,"given":"Wan Xin","family":"Boon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boon Zhan","family":"Sia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-9831","authenticated-orcid":false,"given":"Chong Han","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","first-page":"497-506","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.","volume":"395","author":"C Huang","year":"2020","journal-title":"Lancet."},{"key":"ref-2","doi-asserted-by":"publisher","first-page":"44-48","DOI":"10.1016\/j.ijid.2020.03.004","article-title":"The SARS-CoV-2 outbreak: what we know.","volume":"94","author":"D Wu","year":"2020","journal-title":"Int J Infect 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Authors first identified common synonymous mutations such as 3037C&gt;T followed by secondary structure predictions using RNAfold.  First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.  RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https:\/\/covariants.org\/variants\/20I.Alpha.V1) and (https:\/\/assets.publishing.service.gov.uk\/government\/uploads\/system\/uploads\/attachment_data\/file\/959438\/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https:\/\/covariants.org\/variants\/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.  Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.  RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v4#referee-comment-7389","order":1,"name":"referee-comment-7389","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Chong Han Ng<\/b>; \n<i>Posted: 21 Nov 2022<\/i>; First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.  \n<b>RE:<\/b> We submitted a revised version in Sep 2022. Although we didn\u2019t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.  Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.  \n<b>RE:<\/b> In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v4#referee-comment-9047","order":2,"name":"referee-comment-9047","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.76505.r101828, Leyi Wang, Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, Urbana, IL, 20 Dec 2021, version 1, 1 approved with reservations, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v1#referee-response-101828","order":3,"name":"referee-response-101828","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Chong Han Ng<\/b>; \n<i>Posted: 22 Aug 2022<\/i>; The manuscript entitled \u201cPrediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome\u201d reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.  This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed. \n<b>Response:&nbsp;<\/b>We downloaded the raw data on 23\n<sup>rd<\/sup> March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well.&nbsp; We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.  A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?  \n<b>Response:&nbsp;<\/b>In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.  In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.  \n<b>Response<\/b>: The abstract has been revised to update the results and conclusion.","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v4#referee-comment-8660","order":4,"name":"referee-comment-8660","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.137703.r149467, Chandran Nithin, Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, 06 Sep 2022, version 2, 1 approved, 1 approved with reservations, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v2#referee-response-149467","order":5,"name":"referee-response-149467","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.137703.r208617, Roland Huber, Bioinformatics Institute, A*STAR, 18 Oct 2023, version 2, 1 approved, 1 approved with reservations, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v2#referee-response-208617","order":6,"name":"referee-response-208617","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.162881.r304726, Diego Forni, Scientific Institute IRCCS E Medea, Bosisio Parini, 27 Jul 2024, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v3#referee-response-304726","order":7,"name":"referee-response-304726","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Chong Han Ng<\/b>; \n<i>Posted: 05 Sep 2024<\/i>; In this manuscript, Dr Boon et al. analyzed the most common synonymous mutations of early pandemic SARS-CoV-2 genomes. They identified substitutions that influence the RNA secondary structure surrounding these positions. The authors have already modified and updated their manuscript based on previous comments, still there are few open questions in my opinion. First of all, it is clear that the dataset is not up to date and it will not be changed, still this should be clearly stated, starting from the title, reporting or the time period or something like &quot;at the beginning of the pandemic''. \n<b>Author response:<\/b>&nbsp; The title has been revised to \u201cPrediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase\u201d.  \n<b>Reviewer comment:<\/b> Following this, also the introduction section should be focused on what we know about genomic variability and viral evolution in the time period being analyzed herein. If I understood well, most of these sequences belong to the alpha variant. \n<b>Author response:<\/b> We added a few lines about Alpha variant in introduction, and result and discussion section (Identification of SARS-CoV-2 synonymous mutations).  \n<b>Reviewer comment:<\/b> Moreover, &quot; variant of concern&quot; (abbreviated VOC not VOI as stated in the text) refers to a specific lineage that carries several and distinct mutations. As it is stated in the text now it seems that a single mutation is a VOC. Please check it. \n<b>Author response:<\/b> The abbreviation has been corrected from VOI to VOC. In the text, we mentioned that different SARS-CoV-2 variants with multiple synonymous and nonsynonymous mutations have been reported since the beginning of the outbreak. Therefore, it is clear that VOCs contain multiple mutations.  \n<b>Reviewer comment:&nbsp;<\/b>It is expected that most mutations are found in ORF1ab, as this covers most of the genome, so counts should be normalized by ORF length (e.g. fig.1). \n<b>Author response:<\/b> Figure 1 show the distribution of 150 different types of synonymous mutations identified from 26645 SARS-CoV-2 genomes. It is not about the count or the average of the mutations in each ORFs, thus it should not be normalized.  \n<b>Reviewer comment:&nbsp;<\/b>Table 1 should report mutation frequency not count. \n<b>Author response:<\/b> Table 1 shows the mutation frequency as reported in this paper.  \n<b>Reviewer comment:<\/b> Authors should discuss more why different predictors give very different results in terms of rna secondary structure, and how this can affect their analysis. \n<b>Author response:<\/b> In the paper, there is about two paragraphs on comparison of these RNA secondary structure tools and we highlighted their similarities and differences of these tools. As mentioned in the discussion, we expected some differences in the outcomes obtained from different RNA secondary structure prediction tools due to the differences in algorithms, thermodynamic parameter settings, inclusion of pseudoknot calculation, incorporation of experimental data and the assumptions of each tool. Furthermore, SARS-CoV-2 may adopt different RNA secondary structure conformations. However, our major focus is to predict if the sSNP may affect\/change RNA secondary structure, rather than the predicted structures \n<i>per se<\/i>. Importantly the outcomes allow us to prioritize variants for the experiment functional studies in the future. In addition, we demonstrated the usability of prediction tools by using 5\u2019 UTR (1-480 nt) of SARS-CoV-2, which is well-characterized in other studies in the Extended data 1. For the four sSNPs which showed pronounced changes, we also compared the differences of these structures predicted by different tools.  \n<b>Reviewer comment:<\/b> Why did the author select 250 bp flanking positions for their prediction? Which is the rationale? How does this influence the results? \n<b>Author response:<\/b> Some sSNPs affect only RNA secondary structures of neighboring nucleotides while some sSNPs may have long-ranging effects, in addition to the changes in the neighboring nucleotides. The length of RNA sequences used should not be too short since we may miss out the long-ranging effects. On the other hand, we need to bear in mind that most of RNA secondary structure prediction tools are not suitable for long sequences analysis. We tried out different lengths of flanking sequences, including 100b, 250b, 500b and 1000b, and we found out that 250b flanking sequences produce the optimal results.  \n<b>Reviewer comment:<\/b>&nbsp; Are there any other mutations in the 501 nucleotide regions? Could mutations have an impact on predictions? \n<b>Author response:<\/b> Yes, there may be other synonymous mutations within the 501 nucleotide regions, but they are not from the top 10 synonymous mutations. We are not doing epitasis analysis of different mutations since it is out of the scope of this study.  \n<b>Reviewer comment:<\/b> How did the authors handle overlapping\/internal ORFs? In these situations a syn mutations could also be non-synonymous for the other ORF. I do not think this is the case for the top 10 mutations, but still this should be explained. \n<b>Author response:<\/b> Yes, we don\u2019t find any synonymous mutation of one ORF, which is also a synonymous mutation for another ORF. We included one sentence about the overlapping ORFs in the result and discussion section in Identification of SARS-CoV-2 synonymous mutation. Since some mutations, including C913U, U16176C are located near the boundaries of different ORFs, we postulated their potential long ranging effects at the neighboring ORFs in the discussion section.  \n<b>Reviewer comment:<\/b> The CUB section seems to me not really linked to the rest of the analyses and could be expanded more. \n<b>Author response:<\/b> We expanded CUB section, including additional relevant references, elaborating the results further to make it clearer to the reader.","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v4#referee-comment-12375","order":8,"name":"referee-comment-12375","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.162881.r304727, Tamar Schlick, Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, New York, New York, 11 Sep 2024, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/10-1053\/v3#referee-response-304727","order":9,"name":"referee-response-304727","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This research is supported by Multimedia University, Malaysia, IRFund 2.0 (grant number MMUI\/210119 awarded to Chong Han, Ng). The funder has no role in study design, data analysis, decision to publish or manuscript preparation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":10,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}