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1.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.11.27.23298986

ABSTRACT

Viral sequencing has been critical in the COVID-19 pandemic response, but sequencing and bioinformatics capacity remain inconsistent. To examine the utility of a cloud-based sequencing analysis platform for SARS-CoV-2 sequencing, we conducted a cross-sectional study incorporating seven countries in July 2022. Sites submitted sequential SARS-CoV-2 sequences over two weeks to the Global Pathogen Analysis Service (GPAS). The GPAS bioinformatics cloud platform performs sequence assembly plus lineage and related sample identification. Users can share information with collaborators while retaining data ownership. Seven sites contributed sequencing reads from 5,346 clinical samples, of which 4,799/5,346 (89.8%) had a lineage identified. Omicron lineages dominated, with the vast majority being BA.5, BA.4 and BA.2, commensurate with contemporary genomic epidemiological observations. Phylogenetic analysis demonstrated low within-lineage diversity, and highly similar sequences present in globally disparate sites. A cloud-based analysis platform like GPAS addresses bioinformatics bottlenecks and facilitates collaboration in pathogen surveillance, enhancing epidemic and pandemic preparedness.


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COVID-19
2.
biorxiv; 2023.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2023.04.19.537514

ABSTRACT

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.


Subject(s)
COVID-19
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