1,721,122 research outputs found
COG-UK consortium.
No full textFull list and affiliations for COVID-19 Genomics UK (COG-UK) Consortium members. (DOCX)</p
Membership of the COVID-19 Genomics UK (COG-UK) Consortium.
No full textMembership of the COVID-19 Genomics UK (COG-UK) Consortium.</p
A list of members of the COVID-19 Genomics UK (COG-UK) consortium.
No full textA list of members of the COVID-19 Genomics UK (COG-UK) consortium.</p
COG-UK Viral Genome Sequences
No full textCOG-UK Consortium has published dataset contains over 10K SARS-CoV-2 viral genome sequences available as open access. The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents a major threat to health in the UK and globally. To fully understand the transmission and evolution of the virus requires sequencing and analysing viral genomes at scale and speed. The numbers of samples calls for a rapid increase in the UK’s pathogen genome sequencing capacity rapidly and robustly. To provide this increased capacity to collect, sequence and analyse the whole genomes of virus samples in the UK, the COVID-19 Genomics UK (COG-UK) consortium is pooling the world-leading knowledge and expertise in genomics of the four UK Public Health Agencies, multiple regional University hubs, and large sequencing centres such as the Wellcome Sanger Institute.
Protocols: https://www.cogconsortium.uk/protocols
Supplementary author list.
No full textThe COVID-19 Genomics UK (COG-UK) Consortium. COVID-19 impact project (Trinidad and Tobago Group). (DOCX)</p
Recommended from our members
[Correction] Genomic reconstruction of the SARS-CoV-2 epidemic in England (Nature, (2021), 600, 7889, (506-511), 10.1038/s41586-021-04069-y)
No full textThe name of author Erik Volz appeared as “Erik M. Volz” in The COVID-19 Genomics UK (COG-UK) Consortium contributions listings. Further, the affiliation listed for COG-UK Consortium member Adam A. Witney was shown incorrectly (Imperial College London) and has been now amended to the Institute for Infection and Immunity, St George’s Hospital of London, London, UK. The changes have been made to the HTML and PDF versions of the article.</p
Context-specific emergence and growth of the SARS-CoV-2 Delta variant
Full text linkThis is the final version. Available on open access from Nature Research via the DOI in this recordData availability:
UK genome sequences used were generated by the COVID-19 Genomics UK consortium (COG-UK,
https://www.cogconsortium.uk/). Data linking COG-IDs to location have been removed to protect
privacy, however if you require this data please visit https://www.cogconsortium.uk/contact/ for
information on accessing consortium-only data. The Google COVID-19 Aggregated Mobility Research
Dataset used for this study is available with permission from Google LLC. Shapefiles for county-level
analyses in the UK are openly accessible via the Global Administrative Database (gadm.org).The SARS-CoV-2 Delta (Pango lineage B.1.617.2) variant of concern spread globally, causing resurgences of COVID-19 worldwide1,2. The emergence of the Delta variant in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 SARS-CoV-2 genomes from England together with 93,649 genomes from the rest of the world to reconstruct the emergence of Delta and quantify its introduction to and regional dissemination across England in the context of changing travel and social restrictions. Using analysis of human movement, contact tracing and virus genomic data, we find that the geographic focus of the expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced more than 1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers reduced onward transmission from importations; however, the transmission chains that later dominated the Delta wave in England were seeded before travel restrictions were introduced. Increasing inter-regional travel within England drove the nationwide dissemination of Delta, with some cities receiving more than 2,000 observable lineage introductions from elsewhere. Subsequently, increased levels of local population mixing—and not the number of importations—were associated with the faster relative spread of Delta. The invasion dynamics of Delta depended on spatial heterogeneity in contact patterns, and our findings will inform optimal spatial interventions to reduce the transmission of current and future variants of concern, such as Omicron (Pango lineage B.1.1.529)
Viral burden is associated with age, vaccination, and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection-related sampling bias.
Full text linkIn this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burden, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. By analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior exposure, viral burden was 44% lower among Alpha variant infections, compared to those with the predecessor strain, B.1.177. Vaccination reduced viral burden by 67%, and among vaccinated individuals, viral burden was 286% higher among Delta variant, compared to Alpha variant, infections. In addition, viral burden increased by 17% for every 10-year age increment of the infected individual. In summary, within-host viral burden increases with age, is reduced by vaccination, and is influenced by the interplay of vaccination status and viral variant
Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021
Full text linkThis paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020-December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population
The mutational spectrum of SARS-CoV-2 genomic and antigenomic RNA.
Full text linkThe raw material for viral evolution is provided by intra-host mutations occurring during replication, transcription or post-transcription. Replication and transcription of Coronaviridae proceed through the synthesis of negative-sense 'antigenomes' acting as templates for positive-sense genomic and subgenomic RNA. Hence, mutations in the genomes of SARS-CoV-2 and other coronaviruses can occur during (and after) the synthesis of either negative-sense or positive-sense RNA, with potentially distinct patterns and consequences. We explored for the first time the mutational spectrum of SARS-CoV-2 (sub)genomic and anti(sub)genomic RNA. We use a high-quality deep sequencing dataset produced using a quantitative strand-aware sequencing method, controlled for artefacts and sequencing errors, and scrutinized for accurate detection of within-host diversity. The nucleotide differences between negative- and positive-sense strand consensus vary between patients and do not show dependence on age or sex. Similarities and differences in mutational patterns between within-host minor variants on the two RNA strands suggested strand-specific mutations or editing by host deaminases and oxidative damage. We observe generally neutral and slight negative selection on the negative strand, contrasting with purifying selection in ORF1a, ORF1b and S genes of the positive strand of the genome
- …
