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GISAID

GISAID

GISAID is a nonprofit organization dedicated to the collection of influenza related data and genome sequencing.

The GISAID Initiative enables the rapid sharing of influenza virus data through a unique sharing mechanism, that was recognized for its importance to global health by all G20 health ministers in 2017. The Initiative involves public-private partnerships between the nonprofit organization Freunde von GISAID e.V. and governments of the Federal Republic of Germany, the official host of the GISAID EpiFluTM database - which provides public access to the most complete collection of genetic sequence data of influenza viruses and related clinical and epidemiological data - Singapore and the United States of America.

Initially spurred by the threat posed by human infections with highly pathogenic avian influenza H5N1, GISAID introduced its novel sharing mechanism in 2008 that permits free and open access to influenza data, to anyone who positively identifies himself or herself, and agrees to respect the inherent rights of contributors. GISAID's database access agreement ensures that contributors of virus sequence data do not forfeit intellectual property rights to the data.

Covid-19

GISAID has developed an application to follow the data and genome sequencing of Covid-19. They are working to bring the data together and make it available for others studying and tracking the Covid-19 sequencing and related clinical and epidemiological data. They also track the spread and the phylogeny with visual aids. The data and the mapping are available for download along with the data through GISAID's website.

Timeline

People

Name
Role
LinkedIn

Benjamin Turner

Database Technical Group Member

Catherine B. Smith, VSDB OBC

Database Technical Group Member

Dr. Andrey B. Komissarov

Database Technical Group Member

Dr. David E. Wentworth

Scientific Advisory Council Member

Dr. Isabella Monne

Database Technical Group Member

Dr. Joachim Buch

Database Technical Group Member

Dr. John W. McCauley

Co-Chair Scientific Advisory Council

Dr. med Wenqing Zhang

Scientific Advisory Council Member

Dr. Mia Brytting

Database Technical Group Member

Dr. Monica Galiano

Database Technical Group Member

Dr. Rebecca J. Kondor

Co-Chair Database Technical Group

Dr. Richard J. Webby

Scientific Advisory Council Member

Dr. Sebastian Maurer-Stroh

Scientific Advisory Council Member

Dr. Seiichiro Fujisaki

Database Technical Group Member

Dr. Thorsten Wolff

Database Technical Group Member

Dr. Vivi Setiawaty

Database Technical Group Member

Dr. Yu Lan

Database Technical Group Member

Naomi Komadina

Co-Chair Database Technical Group

Prof. Dr. Andrew Rambaut

Scientific Advisory Council Member

Prof. Dr. George Fu Gao

Scientific Advisory Council Member

Prof. Dr. H.C. Thomas C. Mettenleiter

Scientific Advisory Council Member

Prof. Dr. Ian Barr

Scientific Advisory Council Member

Prof. Dr. Ian Brown

Scientific Advisory Council Member

Prof. Dr. med Hideki Hasegawa

Scientific Advisory Council Member

Prof. Dr. Richard Neher

Database Technical Group Member

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Further reading

Title
Author
Link
Type
Date

GISAID: Global initiative on sharing all influenza data - from vision to reality

Yuelong Shu, John McCauley

Web

March 30, 2017

Documentaries, videos and podcasts

Title
Date
Link

Companies

Company
CEO
Location
Products/Services

News

Title
Author
Date
Publisher
Description
Special Correspondent
June 15, 2021
The Hindu
AY.1 found in Andhra Pradesh, Maharashtra, Telangana and Karnataka
June 9, 2021
news.google.com
Given India's population and COVID-19 case burden, the number of sequences available remains disproportionately low, and insights available from existing efforts need to be sharpened.
June 9, 2021
Firstpost
Genomic surveillance allows researchers to examine the genome sequence of the viral strains infecting the population.
Arunabh Saikia
April 21, 2021
Scroll.in
It may evade some of the body's immune response but scientists say most vaccines should still help reduce severity of disease.
April 15, 2021
Hindustan Times
The analysis shows for the first time how the detection of various variants of the coronavirus may have changed.
Lythgoe, K. A., Hall, M., Ferretti, L., de Cesare, M., MacIntyre-Cockett, G., Trebes, A., Andersson, M., Otecko, N., Wise, E. L., Moore, N., Lynch, J., Kidd, S., Cortes, N., Mori, M., Williams, R., Vernet, G., Justice, A., Green, A., Nicholls, S. M., Ansari, M. A., Abeler-Dörner, L., Moore, C. E., Peto, T. E. A., Eyre, D. W., Shaw, R., Simmonds, P., Buck, D., Todd, J. A., on behalf of the Oxford Virus Sequencing Analysis Group (OVSG), Connor, T. R., Ashraf, S., da Silva Filipe, A., Shepherd, J., Thomson, E. C., The COVID-19 Genomics UK (COG-UK) Consortium, Bonsall, D., Fraser, C., Golubchik, T.
April 16, 2021
Science
A year into the severe acute respiratory syndrome coronavirus 2 pandemic, we are experiencing waves of new variants emerging. Some of these variants have worrying functional implications, such as increased transmissibility or antibody treatment escape. Lythgoe et al. have undertaken in-depth sequencing of more than 1000 hospital patients' isolates to find out how the virus is mutating within individuals. Overall, there seem to be consistent and reproducible patterns of within-host virus diversity. The authors observed only one or two variants in most samples, but a few carried many variants. Although the evidence indicates strong purifying selection, including in the spike protein responsible for viral entry, the authors also saw evidence for transmission clusters associated with households and other possible superspreader events. After transmission, most variants fizzled out, but occasionally some initiated ongoing transmission and wider dissemination. Science , this issue p. [eabg0821][1] ### INTRODUCTION Genome sequencing at an unprecedented scale during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is helping to track spread of the virus and to identify new variants. Most of this work considers a single consensus sequence for each infected person. Here, we looked beneath the consensus to analyze genetic variation within viral populations making up an infection and studied the fate of within-host mutations when an infection is transmitted to a new individual. Within - host diversity offers the means to help confirm direct transmission and identify new variants of concern. ### RATIONALE We sequenced 1313 SARS-CoV-2 samples from the first wave of infection in the United Kingdom. We characterized within-host diversity and dynamics in the context of transmission and ongoing viral evolution. ### RESULTS Within-host diversity can be described by the number of intrahost single nucleotide variants (iSNVs) occurring above a given minor allele frequency (MAF) threshold. We found that in lower-viral-load samples, stochastic sampling effects resulted in a higher variance in MAFs, leading to more iSNVs being detected at any threshold. Based on a subset of 27 pairs of high-viral-load replicate RNA samples (>50,000 uniquely mapped veSEQ reads, corresponding to a cycle threshold of ~22), iSNVs with a minimum 3% MAF were highly reproducible. Comparing samples from two time points from 41 individuals, taken on average 6 days apart (interquartile ratio 2 to 10), we observed a dynamic process of iSNV generation and loss. Comparing iSNVs among 14 household contact pairs, we estimated transmission bottleneck sizes of one to eight viruses. Consensus differences between individuals in the same household, where sample depth allowed iSNV detection, were explained by the presence of an iSNV at the same site in the paired individual, consistent with direct transmission leading to fixation. We next focused on a set of 563 high-confidence iSNV sites that were variant in at least one high-viral-load sample (>50,000 uniquely mapped); low-confidence iSNVs unlikely to represent genomic diversity were excluded. Within-host diversity was limited in high-viral-load samples (mean 1.4 iSNVs per sample). Two exceptions, each with >14 iSNVs, showed variant frequencies consistent with coinfection or contamination. Overall, we estimated that 1 to 2% of samples in our dataset were coinfected and/or contaminated. Additionally, one sample was coinfected with another coronavirus (OC43), with no detectable impact on diversity. The ratio of nonsynonymous to synonymous ( dN/dS ) iSNVs was consistent with within-host purifying selection when estimated across the whole genome [ dN/dS = 0.55, 95% confidence interval (95% CI) = 0.49 to 0.61] and for the Spike gene ( dN/dS = 0.60, 95% CI = 0.45 to 0.82). Nevertheless, we observed Spike variants in multiple samples that have been shown to increase viral infectivity (L5F) or resistance to antibodies (G446V and A879V). We observed a strong association between high-confidence iSNVs and a consensus change on the phylogeny (153 cases), consistent with fixation after transmission or de novo mutations reaching consensus. Shared variants that never reached consensus (261 cases) were not phylogenetically associated. ### CONCLUSION Using robust methods to call within-host variants, we uncovered a consistent pattern of low within-host diversity, purifying selection, and narrow transmission bottlenecks. Within-host emergence of vaccine and therapeutic escape mutations is likely to be relatively rare, at least during early infection, when viral loads are high, but the observation of immune-escape variants in high-viral-load samples underlines the need for continued vigilance. ![Figure][2] Diagram showing low SARS-CoV-2 within-host genetic diversity and narrow transmission bottleneck. Individuals with high viral load typically have few, if any, within-host variants. Narrow transmission bottlenecks mean that the major variant in the source individual was typically transmitted and the minor variants lost. Occasionally, the minor variant was transmitted, leading to a consensus change, or multiple variants were transmitted, resulting in a mixed infection. Credit: FontAwesome, licensed under CC BY 4.0. Extensive global sampling and sequencing of the pandemic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have enabled researchers to monitor its spread and to identify concerning new variants. Two important determinants of variant spread are how frequently they arise within individuals and how likely they are to be transmitted. To characterize within-host diversity and transmission, we deep-sequenced 1313 clinical samples from the United Kingdom. SARS-CoV-2 infections are characterized by low levels of within-host diversity when viral loads are high and by a narrow bottleneck at transmission. Most variants are either lost or occasionally fixed at the point of transmission, with minimal persistence of shared diversity, patterns that are readily observable on the phylogenetic tree. Our results suggest that transmission-enhancing and/or immune-escape SARS-CoV-2 variants are likely to arise infrequently but could spread rapidly if successfully transmitted. [1]: /lookup/doi/10.1126/science.abg0821 [2]: pending:yes
Lila Thulin
February 11, 2021
Smithsonian Magazine
A scattered and underfunded effort at genomic sequencing has hindered the country's ability to detect different forms of the virus
By DANICA KIRKA, Associated Press
March 28, 2021
Chron
LONDON (AP) -- On March 4, 2020, when there were just 84 confirmed cases of COVID-19 in...
Dr. Catherine Schuster-Bruce
February 12, 2021
Business Insider
The variant in Uganda, called A.23.1, has quickly become the most common coronavirus variant in Kampala.
Adam Rogers
February 8, 2021
Wired
As potentially more dangerous variants of Covid-19 spread, scientists are taking a crack at giving them clearer names that'll help in the fight.
Sophie Kevany and Tom Carstensen
November 18, 2020
the Guardian
Denmark has already launched a nationwide cull of its farmed mink herd after concerns for vaccine efficacy
Ishupal Singh Kang & Sachin Sathyarajan | The Wire
April 16, 2020
@bsindia
The philosophy of open science is informed by the idea that research communities must share socially useful knowledge, including scientific research, freely and without charge

References

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