In-silico matching of SARS-CoV-2 genes and oligonucleotides using the Simple Oligo Matching Tool.

BACKGROUND: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a global pandemic, necessitating accurate diagnostic methods to control its spread. The World Health Organization (WHO) recommends nucleic acid amplification tests (NAATs), particularly reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR), as the diagnostic standard. Due to SARS-CoV-2’s high mutation rate, continuous validation of oligonucleotides (primers and probes) used in these tests is essential to ensure diagnostic accuracy. OBJECTIVE: Current validation tools for personal computers, such as Primer-BLAST, face limitations in handling large-scale genetic data and providing inclusivity analysis. To address these issues, we developed the Simple Oligo Matching Tool, a Python-based tool to facilitate in-silico analysis. METHODS: The performance of the Simple Oligo Matching Tool was assessed by comparing it with Primer-BLAST using a small-scale database, in which it demonstrated equivalent results. Additionally, this tool enables the analysis of oligonucleotide inclusivity and exclusivity across large-scale databases, identifying matches and mismatches with diverse SARS-CoV-2 variants and non-target microbes. RESULTS: The Simple Oligo Matching Tool demonstrated equivalent performance to Primer-BLAST in small-scale analysis and further showed the capability to handle large-scale database analysis for both inclusivity and exclusivity. It successfully identified matches and mismatches with a wide variety of SARS-CoV-2 variants and non-target organisms. CONCLUSION: The Simple Oligo Matching Tool is expected to significantly reduce the complexity of large-scale in-silico analysis for non-commercial and commercial diagnostic development. It could be applied in various fields and could support continuous improvement and adaptation to respond to emerging infectious diseases.