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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2
Hendrick Gao-Min Lim1,2, Yang C Fann3, Yuan-Chii Gladys Lee1
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031.
A new cloud workflow, COWID, efficiently analyzes SARS-CoV-2 genomic data. This tool offers scalable, reliable viral and variant identification at low cost, even with limited resources.
Area of Science:
- Genomics
- Bioinformatics
- Cloud Computing
Background:
- Analyzing extensive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic data presents challenges, especially with limited computational resources.
- Existing cloud platforms often require specialized configurations for novel pathogen research.
Purpose of the Study:
- To develop a cost-effective and efficient cloud-based workflow for SARS-CoV-2 genomic analysis.
- To repurpose existing cancer genomics cloud infrastructure for viral research.
Main Methods:
- Repurposed a cancer genomics cloud platform to create the Cloud Workflow for Viral and Variant Identification (COWID).
- Utilized the Common Workflow Language for scalable parallelization of genomic data analysis.
- Focused on reliable SARS-CoV-2 identification and variant detection.
Main Results:
- COWID processed raw sequencing data samples in under 5 minutes at a cost of US$0.01 per sample.
- Achieved scalable identification and reliable variant findings with no false-positive results.
- Outperformed contemporary methods in speed and accuracy.
Conclusions:
- COWID provides a user-friendly, time-efficient solution for SARS-CoV-2 genomic analysis during pandemics.
- The workflow is publicly available and accessible, requiring no prior programming knowledge.
- Repurposing cloud infrastructure offers a viable strategy for rapid response to emerging infectious diseases.
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