The lag in SARS-CoV-2 genome submissions to GISAID

Kalia, Kishan; Saberwal, Gayatri; Sharma, Gaurav

doi:10.1038/s41587-021-01040-0

Cited by 66 publications

(55 citation statements)

References 11 publications

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“…3; Fig. S3; see also (18)). Some countries have been performing surveillance mainly in near real-time, with a median turnaround time below 21 days (Fig.…”

Section: Sequencing Regularity and Turnaround Timementioning

confidence: 90%

“…We also described turnaround time (TAT; defined as the time in days between sample collection and genome submission) of SARS-CoV-2 genome sequencing across 19 geographic regions (Fig. 1D; see also (11)). On average, virus sequences were deposited in public databases 48 days after sample collection, but in 2021, following the detection of the Alpha VOC, efforts were made in nearly all geographic regions to decrease TAT, and provide faster responses (Fig.…”

Section: Global Disparities In Sars-cov-2 Genomic Surveillancementioning

confidence: 99%

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Global disparities in SARS-CoV-2 genomic surveillance

Brito

Semenova

Dudas

et al. 2021

Preprint

106

127

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The COVID-19 pandemic has revealed the importance of virus genome sequencing to guide public health interventions to control virus transmission and understand SARS-CoV-2 evolution. As of July 20th, 2021, >2 million SARS-CoV-2 genomes have been submitted to GISAID, 94% from high income and 6% from low and middle income countries. Here, we analyse the spatial and temporal heterogeneity in SARS-CoV-2 global genomic surveillance efforts. We report a comprehensive analysis of virus lineage diversity and genomic surveillance strategies adopted globally, and investigate their impact on the detection of known SARS-CoV-2 virus lineages and variants of concern. Our study provides a perspective on the global disparities surrounding SARS-CoV-2 genomic surveillance, their causes and consequences, and possible solutions to maximize the impact of pathogen genome sequencing for efforts on public health.

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“…3; Fig. S3; see also (18)). Some countries have been performing surveillance mainly in near real-time, with a median turnaround time below 21 days (Fig.…”

Section: Sequencing Regularity and Turnaround Timementioning

confidence: 90%

Section: Global Disparities In Sars-cov-2 Genomic Surveillancementioning

confidence: 99%

Global disparities in SARS-CoV-2 genomic surveillance

Brito

Semenova

Dudas

et al. 2021

Preprint

106

127

View full text Add to dashboard Cite

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“…Our approach can be an alternative method for rapid investigation and early detection of prevalent variants to facilitate regional SARS-CoV-2 genomic surveillance. An efficient variant surveillance is firstly dependent on the timely availability of viral genomes ( Kalia et al, 2021 ). To compensate and minimize the time delay between sample collection and submission, surveillance activities at national and sub-national levels, where first hand data are actually acquired, are highly recommended ( World Health Organization [WHO], 2021 ).…”

Section: Discussionmentioning

confidence: 99%

A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations

et al. 2022

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Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.

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“…To date, the most common sequencing method employed with SARS-CoV-2 is a combination of PCR-based amplification and Next Generation Sequencing (NGS) [ 9 ]. Bioinformatic tools and databases are very helpful for researchers, although—unfortunately—not all countries have a sufficiently significant number of sequencing laboratories, and not all sequenced genomes are uploaded to databases [ 10 ]. The most employed tools used to report all useful information about the different viral clades and suitable to analyze and classify the variant of the sequenced SARS-CoV-2 strains are Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN, cov-lineages) [ 11 ] and Nextstrain [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Low Represented Mutation Clustering in SARS-CoV-2 B.1.1.7 Sublineage Group with Synonymous Mutations in the E Gene

et al. 2021

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Starting in 2019, the COVID-19 pandemic is a global threat that is difficult to monitor. SARS-CoV-2 is known to undergo frequent mutations, including SNPs and deletions, which seem to be transmitted together, forming clusters that define specific lineages. Reverse-Transcription quantitative PCR (RT-qPCR) has been used for SARS-CoV-2 diagnosis and is still considered the gold standard method. Our Eukaryotic Host Pathogens Interaction (EHPI) laboratory received six SARS-CoV-2-positive samples from a Sicilian private analysis laboratory, four of which showed a dropout of the E gene. Our sequencing data revealed the presence of a synonymous mutation (c.26415 C > T, TAC > TAT) in the E gene of all four samples showing the dropout in RT-qPCR. Interestingly, these samples also harbored three other mutations (S137L—Orf1ab; N439K—S gene; A156S—N gene), which had a very low diffusion rate worldwide. This combination suggested that these mutations may be linked to each other and more common in a specific area than in the rest of the world. Thus, we decided to analyze the 103 sequences in our internal database in order to confirm or disprove our “mutation cluster hypothesis”. Within our database, one sample showed the synonymous mutation (c.26415 C > T, TAC > TAT) in the E gene. This work underlines the importance of territorial epidemiological surveillance by means of NGS and the sequencing of samples with clinical and or technical particularities, e.g., post-vaccine infections or RT-qPCR amplification failures, to allow for the early identification of these SNPs. This approach may be an effective method to detect new mutational clusters and thus to predict new emerging SARS-CoV-2 lineages before they spread globally.

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The lag in SARS-CoV-2 genome submissions to GISAID

Cited by 66 publications

References 11 publications

Global disparities in SARS-CoV-2 genomic surveillance

Global disparities in SARS-CoV-2 genomic surveillance

A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations

Low Represented Mutation Clustering in SARS-CoV-2 B.1.1.7 Sublineage Group with Synonymous Mutations in the E Gene

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