CoV-GLUE: A Web Application for Tracking SARS-CoV-2 Genomic Variation

Singer, Joshua B.; Gifford, Robert J.; Cotten, Matt; Robertson, David L.

doi:10.20944/preprints202006.0225.v1

Cited by 185 publications

(211 citation statements)

References 24 publications

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“…For SARS-CoV-2 lineage classification, consensus genomes were submitted to Pangolin software (https://github.com/cov-lineages/pangolin, downloaded on June 10 th , 2020) and to CoV-GLUE lineage system (http://cov-glue.cvr.gla.ac.uk/#/home, accessed on June 10 th , 2020) [25], both based on the nomenclature proposed by Rambaut et al [26]. An alignment including the consensus sequences generated and genomes from Brazilian sequences available on the GISAID Database classified as B1, B1.1 and the Brazilian clusters B1.1-BR/ B1.1-EU/BR (S1 Table) were submitted to a maximum likelihood phylogenic reconstruction using PhyML v.3.0 and the best model of nucleotide substitution was defined with Model Generator (GTR) to investigate the sublineage classification of the study sequences [16,27,28].…”

Section: Sars-cov-2 Classification and Phylogenetic Analysismentioning

confidence: 99%

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

Siqueira

Goes

Alves

et al. 2020

Preprint

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Numerous factors have been identified to influence susceptibility to SARS-CoV-2 infection and disease severity. Cancer patients are more prone to clinically evolve to more severe COVID-19 conditions, but the determinants of such a more severe outcome remain largely unknown. We have determined the full-length SARS-CoV-2 genomic sequences of cancer patients and healthcare workers (HCW; non-cancer controls) by deep sequencing and investigated the within-host viral quasispecies of each infection, quantifying intrahost genetic diversity. Naso- and oropharyngeal SARS-CoV-2+ swabs from 57 cancer patients and 14 healthcare workers (HCW) from the Brazilian Cancer Institute were collected in April–May 2020. Complete genome amplification using ARTIC network V3 multiplex primers was performed followed by next-generation sequencing. Assemblies were conducted in Geneious R11, where consensus sequences were extracted and intrahost single nucleotide variants (iSNVs) were identified. Maximum likelihood phylogenetic analysis was performed using PhyMLv.3.0 and lineages were classified using Pangolin and CoV-GLUE. Phylogenetic analysis showed that all but one strain belonged to clade B1.1. Four genetically linked mutations known as the globally dominant SARS-CoV-2 haplotype (C241T, C3037T, C14408T and A23403G) were found in the majority of consensus sequences. SNV signatures of previously characterized Brazilian genomes were also observed in most samples. Another 85 SNVs were found at a lower frequency (1.4-19.7%). Cancer patients displayed a significantly higher intrahost viral genetic diversity compared to HCW (p = 0.009). Intrahost genetic diversity in cancer patients was independent of SARS-CoV-2 Ct values, and was not associated with disease severity, use of corticosteroids, or use of antivirals, characteristics that could influence viral diversity. Such a feature may explain, at least in part, the more adverse outcomes to which cancer/COVID-19 patients experience.Author SummaryCancer patients are more prone to clinically evolve to more severe COVID-19 conditions, but the determinants of such a more severe outcome remain largely unknown. In this study, phylogenetic and variation analysis of SARS-CoV-2 genomes from cancer patients and non-cancer healthcare workers at the Brazilian National Cancer Institute were characterized by deep sequencing. Viral genomes showed signatures characteristic of Brazilian viruses, consistent with the hypothesis of local, community transmission rather than virus importation from abroad. Despite most genomes in patients and healthcare workers belonging to the same lineage, intrahost variability was higher in cancer patients when compared to non-cancer counterparts. The intrahost genomic diversity analysis presented in our study highlights the relaxed evolution of SARS-CoV-2 in a vulnerable population of cancer patients. The high number of minor variations can result in the selection of immune escape variants, resistance to potential drugs, and/or increased pathogenicity. The impact of this higher intrahost variability over time warrants further investigation.

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Section: Sars-cov-2 Classification and Phylogenetic Analysismentioning

confidence: 99%

SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution

Siqueira

Goes

Alves

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Genome analysis tools were also provided by several platforms such as The China National Center for Bioinformation (https://bigd.big.ac.cn/ncov/tool/annotation) 5 Nextstrain project (https://nextstrain.org) 6 , and CoV-GLUE (http://cov-glue.cvr.gla.ac.uk). 7 According to GISAID nomenclature system, most of the currently sequenced SARS-CoV-2 genomes were clustered into one of six major clades. In their submission order, SARS-CoV-2 clades include L, to which SARS-CoV-2 virus reference strain belongs, S, G, V, GR and GH.…”

Section: Introductionmentioning

confidence: 99%

Global dynamics of SARS-CoV-2 clades and their relation to COVID-19 epidemiology

Hamed

Elkhatib

Khairallah

et al. 2020

Preprint

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Expansion of COVID-19 worldwide increases interest in unraveling genomic variations of novel SARS-CoV-2 virus. Metadata of 60,703 SARS-CoV-2 genomes submitted to GISAID database were analyzed with respect to genomic clades and their geographic, age, and gender distributions. Clade GR was the most frequently identified followed by G and GH. Chronological analysis revealed expansion in SARS-CoV-2 clades with D614Gmutations indicating adaptation-driven evolution. Of them, clade GH showed a slight regression. GR, GH and L clades prevail in countries with higher deaths. GR clade showed higher prevalence among severe/deceased patients. Metadata analysis showed higher (p > 0.05) prevalence of severe/deceased cases among males than females and predominance of GR clade in female and children patients. Furthermore, severe disease/death was more prevalent (p < 0.05) in elderly than in adults/children. These findings uniquely provide an evidence-based evolution of SARS-CoV-2 leading to altered infectivity, virulence, and mortality.

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“…There are several tools that have been developed for viral sequence classification, including recently released applications for subtyping of SARS-CoV-2 sequences (Solis-Reyes et al, 2018;Randhawa et al, 2020;Singer et al, 2020;O'Toole et al, 2020). Most of them require the alignment of the input data against a set of reference sequences (Altschul et al, 1990;Edgar, 2010;Hauser et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Current SARS-CoV-2 classification software require the user to be familiar with command line usage, depend on the user to upload the data to an external server or require multiple steps for a correct installation (Singer et al, 2020;O'Toole et al, 2020).Alternatively, Covidex was developed as an open source alignment-free machine learning subtyping tool. It is an app that allows fast and accurate classification of viral genomes in pre-defined clusters.…”

Section: Introductionmentioning

confidence: 99%