Emerging SARS-CoV-2 Diversity Revealed by Rapid Whole-Genome Sequence Typing.
Genome Biol Evol
; 13(9)2021 09 01.
Article
in English
| MEDLINE | ID: covidwho-1371724
ABSTRACT
Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. We developed a tool (GNU-based Virus IDentification [GNUVID]) that integrates whole-genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to all high-quality genomes available from GISAID. STs were clustered into clonal complexes (CCs) and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events and to estimate effective viral diversity across locations and over time in 16 US states. GNUVID is a highly scalable tool for viral genotype classification (https//github.com/ahmedmagds/GNUVID) that can quickly classify hundreds of thousands of genomes in a way that is consistent with phylogeny. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states, an average of 20.6 putative introductions and 7.5 exportations for each state over the time period analyzed. We introduce the use of effective diversity metrics (Hill numbers) that can be used to estimate the impact of interventions (e.g., travel restrictions, vaccine uptake, mask mandates) on the variation in circulating viruses. Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. GNUVID classification lends itself to measures of ecological diversity, and, with systematic genomic sampling, it could be used to track circulating viral diversity and identify emerging clones and hotspots.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Genome, Viral
/
SARS-CoV-2
/
COVID-19
Type of study:
Observational study
/
Randomized controlled trials
/
Systematic review/Meta Analysis
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Molecular Biology
Year:
2021
Document Type:
Article
Affiliation country:
Gbe
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