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ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

Ahmad Pesaranghader, Justin Pelletier, Jean-Christophe Grenier, Raphaël Poujol, View ORCID ProfileJulie Hussin
doi: https://doi.org/10.1101/2021.08.13.456305
Ahmad Pesaranghader
Montreal Heart Institute, Research Center, 5000 Rue Bélanger, Montréal, QC H1T 1C8
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  • For correspondence: ahmad.pesaranghader@mhi-omics.org
Justin Pelletier
Montreal Heart Institute, Research Center, 5000 Rue Bélanger, Montréal, QC H1T 1C8
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Jean-Christophe Grenier
Montreal Heart Institute, Research Center, 5000 Rue Bélanger, Montréal, QC H1T 1C8
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Raphaël Poujol
Montreal Heart Institute, Research Center, 5000 Rue Bélanger, Montréal, QC H1T 1C8
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Julie Hussin
Montreal Heart Institute, Research Center, 5000 Rue Bélanger, Montréal, QC H1T 1C8
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  • ORCID record for Julie Hussin
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Abstract

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clade’s information, which means it could be used for predictive tasks using virus sequence analysis.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • julie.hussin{at}umontreal.ca

  • * Alternative Affiliation: Mila, 6666 St Urbain St, Montreal, Quebec H2S 3H1 (pesarana{at}mila.quebec)

  • 15th Conference in Machine Learning in Computational Biology (MLCB 2020), Vancouver, Canada.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 16, 2021.
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ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences
Ahmad Pesaranghader, Justin Pelletier, Jean-Christophe Grenier, Raphaël Poujol, Julie Hussin
bioRxiv 2021.08.13.456305; doi: https://doi.org/10.1101/2021.08.13.456305
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ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences
Ahmad Pesaranghader, Justin Pelletier, Jean-Christophe Grenier, Raphaël Poujol, Julie Hussin
bioRxiv 2021.08.13.456305; doi: https://doi.org/10.1101/2021.08.13.456305

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