Back

Alignment-Free Guided Design of a Pan-Orthoflavivirus RT-qPCR Assay

Sayasit, K.; Chaimayo, C.; Nuwong, W.; Boondouylan, T.; Tanliang, N.; Nookaew, I.; Horthongkham, N.

2026-03-20 microbiology
10.64898/2026.03.17.712358 bioRxiv
Show abstract

The co-circulation and rapid expansion of the genus Orthoflavivirus, including dengue virus (DENV), Zika (ZIKV), 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 datasets. 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 against standard reference strains demonstrated a limit of detection of 1-10 copies/{micro}L for DENV1-4, ZIKV, and JEV, with no cross-reactivity against non-target pathogens. 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 approach for uncovering conserved diagnostic targets in highly variable viral genera. The resulting assay offers a robust tool for frontline surveillance, and the computational framework provides a scalable solution for future pandemic preparedness.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Journal of Clinical Microbiology
120 papers in training set
Top 0.2%
12.4%
2
Viruses
318 papers in training set
Top 0.5%
9.2%
3
Scientific Reports
3102 papers in training set
Top 17%
6.4%
4
Journal of Virological Methods
36 papers in training set
Top 0.1%
4.9%
5
Frontiers in Microbiology
375 papers in training set
Top 2%
4.9%
6
PLOS ONE
4510 papers in training set
Top 34%
4.3%
7
Microbiology Spectrum
435 papers in training set
Top 0.6%
4.0%
8
Clinical Chemistry
22 papers in training set
Top 0.2%
2.6%
9
Nature Communications
4913 papers in training set
Top 45%
2.4%
50% of probability mass above
10
Genome Medicine
154 papers in training set
Top 3%
2.1%
11
mSphere
281 papers in training set
Top 2%
2.1%
12
Emerging Microbes & Infections
74 papers in training set
Top 0.6%
2.1%
13
Journal of Virology
456 papers in training set
Top 2%
1.7%
14
Journal of Infection
71 papers in training set
Top 1%
1.7%
15
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 3%
1.5%
16
mBio
750 papers in training set
Top 8%
1.5%
17
Nucleic Acids Research
1128 papers in training set
Top 12%
1.5%
18
Virus Evolution
140 papers in training set
Top 0.9%
1.5%
19
Journal of General Virology
46 papers in training set
Top 0.5%
1.3%
20
Analytical Chemistry
205 papers in training set
Top 2%
1.2%
21
PLOS Pathogens
721 papers in training set
Top 7%
1.1%
22
The Journal of Molecular Diagnostics
36 papers in training set
Top 0.3%
1.0%
23
ACS Infectious Diseases
74 papers in training set
Top 1%
0.9%
24
PLOS Biology
408 papers in training set
Top 16%
0.9%
25
Virus Research
36 papers in training set
Top 1.0%
0.9%
26
Microbial Genomics
204 papers in training set
Top 2%
0.8%
27
Communications Biology
886 papers in training set
Top 21%
0.8%
28
Briefings in Bioinformatics
326 papers in training set
Top 6%
0.8%
29
eLife
5422 papers in training set
Top 55%
0.8%
30
Cell Reports Medicine
140 papers in training set
Top 8%
0.7%