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

  1. Georg Hahn
  2. Sanghun Lee
  3. Dmitry Prokopenko
  4. Jonathan Abraham
  5. Tanya Novak
  6. Julian Hecker
  7. Michael Cho
  8. Surender Khurana
  9. Lindsey R. Baden
  10. Adrienne G. Randolph
  11. Scott T. Weiss
  12. Christoph Lange

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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 the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

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  1. Version published to 10.1186/s12859-022-05105-y
  2. Version published to 10.1101/2022.05.16.492178v2 on bioRxiv
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  4. Version published to 10.1101/2022.05.16.492178v1 on bioRxiv