Emergence of novel SARS-CoV-2 variants in the Netherlands
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Abstract
Coronavirus disease 2019 (COVID-19) has emerged in December 2019 when the first case was reported in Wuhan, China and turned into a pandemic with 27 million (September 9th) cases. Currently, there are over 95,000 complete genome sequences of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, in public databases, accompanying a growing number of studies. Nevertheless, there is still much to learn about the viral population variation when the virus is evolving as it continues to spread. We have analyzed SARS-CoV-2 genomes to identify the most variant sites, as well as the stable, conserved ones in samples collected in the Netherlands until June 2020. We identified the most frequent mutations in different geographies. We also performed a phylogenetic study focused on the Netherlands to detect novel variants emerging in the late stages of the pandemic and forming local clusters. We investigated the S and N proteins on SARS-CoV-2 genomes in the Netherlands and found the most variant and stable sites to guide development of diagnostics assays and vaccines. We observed that while the SARS-CoV-2 genome has accumulated mutations, diverging from reference sequence, the variation landscape is dominated by four mutations globally, suggesting the current reference does not represent the virus samples circulating currently. In addition, we detected novel variants of SARS-CoV-2 almost unique to the Netherlands that form localized clusters and region-specific sub-populations indicating community spread. We explored SARS-CoV-2 variants in the Netherlands until June 2020 within a global context; our results provide insight into the viral population diversity for localized efforts in tracking the transmission of COVID-19, as well as sequenced-based approaches in diagnostics and therapeutics. We emphasize that little diversity is observed globally in recent samples despite the increased number of mutations relative to the established reference sequence. We suggest sequence-based analyses should opt for a consensus representation to adequately cover the genomic variation observed to speed up diagnostics and vaccine design.
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SciScore for 10.1101/2020.11.02.20224352: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All sequences were aligned against the Wuhan-Hu-1 reference using MAFFT (v7.46) with the FFT-NS-fragment option, and the alignment was filtered to remove identical sequences to obtain 24365 non-redundant genomes [26]. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Phylogenetic tree construction: The maximum likelihood phylogenetic tree for the samples in the Netherlands was built using IQ-TREE (v2.05) with GTR model, allowing to collapse non-zero branches, and ultrafast bootstrap with … SciScore for 10.1101/2020.11.02.20224352: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All sequences were aligned against the Wuhan-Hu-1 reference using MAFFT (v7.46) with the FFT-NS-fragment option, and the alignment was filtered to remove identical sequences to obtain 24365 non-redundant genomes [26]. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Phylogenetic tree construction: The maximum likelihood phylogenetic tree for the samples in the Netherlands was built using IQ-TREE (v2.05) with GTR model, allowing to collapse non-zero branches, and ultrafast bootstrap with 1000 replicates [28]. IQ-TREEsuggested: (IQ-TREE, RRID:SCR_017254)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The major limitation of our study is the biased dataset of SARS-CoV-2 sequences. Despite our efforts to combine all genome sequences publicly available up to date, due to imbalanced sampling and dramatic changes in the frequency of genome sequencing, our dataset is over-represented by samples from the Europe and the USA and there are several gaps in time since the beginning of pandemic. In addition, most of the viral sequencing today is performed on hospitalized patients. These issues could be circumvented to some extend by stratified sampling or controlled sequencing efforts with random samples collected from individuals. Nevertheless, our findings are significant to understand the viral population diversity within the Netherlands from late March to early May, where our dataset has the most coverage.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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