Alignment-free <i>k</i> -mer-guided design of a pan- <i>Orthoflavivirus</i> RT-qPCR assay

ABSTRACT 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 (&gt;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. IMPORTANCE Mosquito-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.