Abstract
Current methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison method that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. We show that during the height of the original Omicron surge, countries across Europe, Asia, and the Americas had a spatially asynchronous distribution of Omicron sub-strains. Moreover, neighboring countries were often dominated by either different clusters of the same variant or different variants altogether throughout the pandemic. Analyses of this kind may suggest a different pattern of epidemiological risk than was understood from conventional data, as well as produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.
| Original language | English |
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| Title of host publication | Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023 |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9798400701900 |
| DOIs | |
| State | Published - Jun 26 2023 |
| Event | 2023 Platform for Advanced Scientific Computing Conference, PASC 2023 - Davos, Switzerland Duration: Jun 26 2023 → Jun 28 2023 |
Publication series
| Name | Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023 |
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Conference
| Conference | 2023 Platform for Advanced Scientific Computing Conference, PASC 2023 |
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| Country/Territory | Switzerland |
| City | Davos |
| Period | 06/26/23 → 06/28/23 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work is supported as part of the Genomic Sciences Program DOE Systems Biology Knowledgebase (KBase) funded by the Office of Biological and Environmental Research's Genomic Science program within the US Department of Energy Office of Science under Award Numbers DE-AC02-05CH11231, DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-98CH10886. This work was also supported by the U.S. National Science Foundation (EF-2133763). We gratefully acknowledge all data contributors, i.e., the Authors and their Originating laboratories responsible for obtaining the specimens, and their Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work is supported as part of the Genomic Sciences Program DOE Systems Biology Knowledgebase (KBase) funded by the Office of Biological and Environmental Research’s Genomic Science program within the US Department of Energy Office of Science under Award Numbers DE-AC02-05CH11231, DE-AC02-06CH11357, DE-AC05–00OR22725, and DE-AC02-98CH10886. This work was also supported by the U.S. National Science Foundation (EF-2133763). We gratefully acknowledge all data contributors, i.e., the Authors and their Originating laboratories responsible for obtaining the specimens, and their Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- SARS-CoV-2
- biological networks
- high performance computing