Abstract
BACKGROUND AND AIMS: The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are critical for disease management and understanding viral evolution. This study presents a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data. The pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity by integrating state-of-the-art bioinformatics tools with artificial intelligence. METHODS: Before aligning the reads to the reference genomes, quality control measures, and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements. RESULTS: The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for detecting known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations. CONCLUSION: The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This research aims to develop an auto-bioinformatics pipeline for novel viral sequence discovery, in vitro validation, and SNPs using the Python (AI) language to understand viral evolution. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.