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Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data.

Fatima Mostefai1,2,3, Jean-Christophe Grenier2, Raphaël Poujol2

  • 1Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Québec, Canada.

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Summary
This summary is machine-generated.

This study introduces a new workflow to accurately detect viral mutations within hosts, distinguishing true genetic changes from sequencing errors. This improves our understanding of viral evolution and aids in predicting future viral threats.

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Area of Science:

  • Genomics
  • Virology
  • Bioinformatics

Background:

  • Understanding viral genome evolution is key to managing viral diversity and predicting threats.
  • Intra-host single nucleotide variants (iSNVs) reveal new lineage emergence but are often obscured by sequencing artifacts.

Purpose of the Study:

  • To develop and validate a robust workflow for accurate iSNV detection in large next-generation sequencing (NGS) datasets.
  • To differentiate genuine viral mutations from sequencing errors, enhancing the reliability of viral evolution studies.

Main Methods:

  • Integration of bioinformatics protocols and stringent quality control measures.
  • Application of dimensionality reduction techniques to mitigate batch effects in large NGS libraries.
  • Pioneering the use of PHATE (Potential of Heat-Diffusion for Electronic Structure) for genomic data visualization and analysis.

Main Results:

  • Successfully distinguished true viral mutations from sequencing artifacts in over 130,000 SARS-CoV-2 libraries.
  • Improved the reliability and accuracy of iSNV detection through the developed workflow.
  • Demonstrated the utility of PHATE for visualizing genomic data and explaining clustering structures based on genetic similarity.

Conclusions:

  • The novel workflow significantly enhances the accuracy of intra-host mutation detection.
  • This advancement facilitates a deeper understanding of viral diversity and evolution.
  • The methodology provides a reliable tool for predicting and mitigating future viral threats.