Abstract

The COVID Sequence Clustering Service is an AI-driven system that analyzes and groups SARS-CoV-2 genetic sequences based on similarities and mutation patterns. Using bioinformatics and machine learning clustering techniques, the service identifies variants, tracks mutation trends, and helps researchers understand virus evolution. This tool supports public health efforts, vaccine development, and epidemiological research by enabling fast and accurate genomic analysis.

Existing System

1. Manual genomic comparison and analysis
2. Time-consuming laboratory-based sequencing interpretation
3. Limited scalability for large datasets
4. Delayed identification of new variants

Proposed System

1. Automated clustering using machine learning algorithms (K-Means, Hierarchical Clustering)
2. Rapid analysis of large genomic datasets
3. Early detection of mutation patterns and variants
4. API integration for research labs and health organizations

Software Requirements

1. Python
2. Libraries: Biopython, Scikit-learn, NumPy, Pandas, Matplotlib
3. Jupyter Notebook / VS Code
4. Flask or FastAPI for service deployment

Hardware Requirements

1. Minimum 8 GB RAM (recommended for genomic datasets)
2. Multi-core processor
3. High-capacity storage for sequence data
4. GPU (optional for large-scale analysis)
5. Secure server for sensitive health data