AI-enabled pipeline for virus detection, validation, and SNP discovery from next-generation sequencing data

Area of Science:

• Bioinformatics
• Genomics
• Virology

Background:

• Accurate virus detection and single nucleotide polymorphism (SNP) discovery are vital for disease management and understanding viral evolution.
• Next-generation sequencing (NGS) generates large datasets crucial for genomic analysis.

Purpose of the Study:

• To develop an automated bioinformatics pipeline for virus detection, validation, and SNP discovery from NGS data.
• To integrate advanced bioinformatics tools with artificial intelligence for enhanced viral sequence analysis.
• To improve the understanding of viral evolution and genetic diversity.

Main Methods:

• Quality control and adapter trimming of raw sequencing reads.
• De novo assembly of unmapped reads to identify novel viral sequences.
• SNP discovery using a custom Python script comparing viral reads to a reference genome.

Main Results:

• Successful identification of viral sequences, demonstrating pipeline effectiveness for known and emerging pathogens.
• Comprehensive analysis of viral genetic diversity, identifying dominant variants and variations.
• Recovery of complete viral genomes, enhancing understanding of viral genomics.

Conclusions:

• The developed pipeline offers a robust tool for viral sequence discovery and SNP analysis.
• The integration of AI and bioinformatics enhances the accuracy and sensitivity of viral genomic analysis.
• This research contributes to the field of viral genomics by providing a powerful platform for studying viral evolution.