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Orchestrating an Optimized Next-Generation Sequencing-Based Cloud Workflow for Robust Viral Identification during
Hendrick Gao-Min Lim1, Shih-Hsin Hsiao2,3, Yuan-Chii Gladys Lee1
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
Biology
|October 23, 2021
Summary
This study presents an optimized workflow using next-generation sequencing and cloud computing for accurate pandemic virus identification. The new method efficiently differentiates between COVID-19 and swine flu samples, improving pandemic control strategies.
Area of Science:
- Virology
- Bioinformatics
- Computational Biology
Background:
- The 2009 swine flu (H1N1 influenza A) and the ongoing Coronavirus disease 2019 (COVID-19) pandemics highlight challenges in rapid, accurate sample identification.
- Efficiently distinguishing between different pandemic-causing agents like SARS-CoV-2 and H1N1 is crucial for public health response.
Purpose of the Study:
- To develop and evaluate an optimized workflow integrating next-generation sequencing and cloud computing for accurate identification of pandemic viral samples.
- To assess the workflow's performance in differentiating between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (H1N1) subtypes.
Main Methods:
- Integration of next-generation sequencing technology with cloud computing resources into a streamlined workflow.
- Development of a specific identification algorithm and performance evaluation using short-read sequencing data from 182 samples (92 COVID-19, 90 H1N1).
- Utilized open-access datasets and created pandemic-specific indices for performance assessment.
Main Results:
- The optimized workflow demonstrated high accuracy in differentiating between COVID-19 (SARS-CoV-2) and swine flu (H1N1) samples.
- Performance was particularly enhanced when using indices exclusively representing each respective dataset.
- The workflow significantly outperformed the original platform workflow in terms of speed and cost-effectiveness.
Conclusions:
- The developed workflow offers a robust and efficient tool for identifying pandemic viral cases.
- This approach can significantly aid in the control of current and future pandemic events.
- The integration of sequencing and cloud computing provides a powerful solution for large-scale sample analysis during health crises.
