Overcoming variant mutation-related impacts on viral sequencing and detection methodologies.

Area of Science:

• Virology
• Genomics
• Molecular Diagnostics

Background:

• Accurate pathogen identification is crucial for disease surveillance and control.
• Viral evolution leads to genomic mutations that can hinder diagnostic accuracy and sequencing coverage.
• Existing sequencing and detection methods may be susceptible to these mutations, impacting reliability.

Purpose of the Study:

• To assess, test, and optimize sequencing and detection methodologies for viral pathogens.
• To mitigate the negative impact of genomic mutations on genome targeting efficiency.
• To ensure reliable targeted pathogen sequencing and evaluation of detection methods for emerging viral variants, using SARS-CoV-2 as a model.

Main Methods:

• Utilized *in vitro*-transcribed (IVT) RNA for methodology assessment.
• Employed RNA extracted from clinical SARS-CoV-2 variant samples, including the Omicron variant.
• Evaluated and optimized oligonucleotide annealing and primer design for enhanced genome targeting.

Main Results:

• Demonstrated a framework for assessing the impact of mutations on sequencing and detection.
• Successfully optimized methodologies to improve genome targeting efficiency despite viral mutations.
• Showcased the ability to maintain reliable targeted sequencing for challenging variants like Omicron.

Conclusions:

• Optimized sequencing and detection strategies are essential for accurate pathogen identification in the face of viral evolution.
• The developed methods provide a robust approach to ensure reliable pathogen surveillance and diagnostics.
• This work is critical for adapting to emerging viral threats and maintaining public health security.