Alignment-free k-mer-guided design of a pan-Orthoflavivirus RT-qPCR assay

The co-circulation and rapid expansion of the genus Orthoflavivirus, including dengue virus (DENV), Zika virus (ZIKV), West Nile virus (WNV), Yellow fever virus (YFV), and Japanese encephalitis virus (JEV), pose significant global health challenges. Developing inclusive pan-genus molecular diagnostics is hindered by high nucleotide divergence (>25-30%) and the computational limitations of traditional multiple sequence alignment in detecting conserved motifs across large data sets. To overcome these limitations, we developed a systematic alignment-free design pipeline that uses rigorous k-mer analysis and compacted De Bruijn graphs. We analyzed 11,846 RefSeq viral genomes to identify phylogenetically conserved, functionally relevant signatures within the Orthoflavivirus genus as a case study. The pipeline identified a conserved 600 bp region within the non-structural protein 5 gene, facilitating the design of a broad-spectrum TaqMan RT-qPCR assay. Analytical validation demonstrated limits of detection (LODs) of 1-10 copies/µL for DENV1-4, ZIKV, JEV, and YFV, with no cross-reactivity against non-target pathogens; WNV was consistently detected at 1,000 copies/µL in pilot experiments. In a clinical evaluation of archived samples, the assay achieved 97.33% overall accuracy. It demonstrated 100% sensitivity and specificity for DENV serotypes, yielding significantly earlier cycle threshold (Ct) values compared to a standard commercial kit, while ZIKV detection showed 100% specificity with 71.43% sensitivity. This study validates an alignment-free, k-mer-guided strategy for uncovering conserved diagnostic targets in highly variable viral genera, followed by local MSA-assisted primer-probe design. The resulting assay offers a robust tool for broad frontline surveillance, and the computational framework provides a scalable solution for future pandemic preparedness.IMPORTANCEMosquito-borne viruses like dengue, Zika, and West Nile pose a massive threat to global health. However, because these viruses are highly diverse and mutate rapidly, creating a single diagnostic test to detect all of them has been a challenge. Traditional software struggles to find shared genetic targets across such different viruses. To solve this, we developed a new computational approach. By analyzing over 11,000 viral genomes, we successfully identified a shared genetic signature of a group of mosquito-borne viruses. We used this discovery to create a single, highly accurate test capable of detecting multiple dangerous mosquito-borne viruses at once. This breakthrough provides a crucial, ready-to-use tool for frontline clinical diagnosis and global outbreak surveillance. More importantly, our strategy can be quickly adapted to design broad-spectrum tests for other rapidly mutating viruses, significantly strengthening our ability to prepare for and respond to future pandemics.