Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of th...

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Bibliographic Details
Main Authors: Georg Hahn, Sanghun Lee, Dmitry Prokopenko, Jonathan Abraham, Tanya Novak, Julian Hecker, Michael Cho, Surender Khurana, Lindsey R. Baden, Adrienne G. Randolph, Scott T. Weiss, Christoph Lange
Format: Article
Language:English
Published: BMC 2022-12-01
Series:BMC Bioinformatics
Subjects:
SARS-CoV-2
Nucleotide sequences
Outlier detection
Variants of interest
Machine learning
Online Access:https://doi.org/10.1186/s12859-022-05105-y

Internet

https://doi.org/10.1186/s12859-022-05105-y

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