Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data.
Brief Bioinform
; 22(6)2021 11 05.
Article
in English
| MEDLINE | ID: covidwho-1354275
ABSTRACT
For epidemic prevention and control, the identification of SARS-CoV-2 subpopulations sharing similar micro-epidemiological patterns and evolutionary histories is necessary for a more targeted investigation into the links among COVID-19 outbreaks caused by SARS-CoV-2 with similar genetic backgrounds. Genomic sequencing analysis has demonstrated the ability to uncover viral genetic diversity. However, an objective analysis is necessary for the identification of SARS-CoV-2 subpopulations. Herein, we detected all the mutations in 186 682 SARS-CoV-2 isolates. We found that the GC content of the SARS-CoV-2 genome had evolved to be lower, which may be conducive to viral spread, and the frameshift mutation was rare in the global population. Next, we encoded the genomic mutations in binary form and used an unsupervised learning classifier, namely PhenoGraph, to classify this information. Consequently, PhenoGraph successfully identified 303 SARS-CoV-2 subpopulations, and we found that the PhenoGraph classification was consistent with, but more detailed and precise than the known GISAID clades (S, L, V, G, GH, GR, GV and O). By the change trend analysis, we found that the growth rate of SARS-CoV-2 diversity has slowed down significantly. We also analyzed the temporal, spatial and phylogenetic relationships among the subpopulations and revealed the evolutionary trajectory of SARS-CoV-2 to a certain extent. Hence, our results provide a better understanding of the patterns and trends in the genomic evolution and epidemiology of SARS-CoV-2.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Genomics
/
Epidemics
/
SARS-CoV-2
/
COVID-19
Type of study:
Observational study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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