Robust Mutation Profiling of SARS-CoV-2 Variants from Multiple Raw Illumina Sequencing Data with Cloud Workflow

Area of Science:

• Genomics
• Bioinformatics
• Virology

Background:

• Emerging SARS-CoV-2 variants necessitate global surveillance.
• Current bioinformatics tools face challenges with speed and resource demands.
• Accurate variant identification is crucial for public health interventions.

Purpose of the Study:

• To develop an accessible, cloud-based workflow for SARS-CoV-2 variant mutation profiling.
• To enable rapid and robust analysis of multiple sequencing datasets.
• To support global efforts in tracking viral evolution.

Main Methods:

• Developed an installation-free, cloud-based bioinformatics workflow.
• Utilized parallel cloud computing for efficient data processing.
• Tested the workflow on 55 raw sequencing datasets of early SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta).

Main Results:

• The workflow successfully identified mutated sites and annotated protein-coding genes for SARS-CoV-2 variants.
• Achieved cost-effective and timely mutation profiling.
• Generated consensus genome sequences for public data sharing.

Conclusions:

• The developed cloud workflow enhances SARS-CoV-2 variant surveillance capabilities.
• It provides a robust, efficient, and accessible solution for mutation profiling.
• Facilitates timely data sharing to support global public health efforts.