AbstractChikungunya virus (CHIKV) poses a significant global health threat. Effective genomic surveillance of CHIKV is critical for tracking transmission and evolution. To define an optimal framework, we systematically evaluated four next-generation sequencing (NGS) configurations combining second-generation (DNBSEQ-G99) and third-generation (QPursue-6k) chemistries with 200-bp or 400-bp amplicon schemes using 13 clinical samples (Ct 17.41-38.14). While second-generation sequencing, particularly with 400-bp amplicons, yielded the highest sequencing depth (mean 152,430×), third-generation platforms provided more uniform genome coverage (99.9%) and were resilient to amplification biases, especially in challenging regions like the capsid domain. Crucially, third-generation sequencing uncovered substantially greater intra-host viral diversity, detecting 1,553 and 756 unique variants with 200-bp and 400-bp amplicons, respectively, compared to only 416-417 variants from second-generation platforms. This included a broad spectrum of low-frequency variants (VAF < 0.5), which were largely missed by short-read methods. However, we observed that region-specific coverage biases in second-generation sequencing may cause false negatives, underscoring the importance of coverage-aware interpretation. Both platforms reliably identified known epidemiologically relevant mutations, such as E1-A226 V. However, variant detection sensitivity correlated inversely with viral load for second-generation sequencing but showed a paradoxical positive correlation for third-generation sequencing, indicating platform-dependent performance at low template concentrations. Ct-stratified analysis revealed that in low-coverage, ultra-low viral load samples, third-generation (44.54%-77.32%) sequencing outperforms 2nd-seq (0.85%-27.88%) in variant detection. Our results establish that third-generation sequencing with amplicon-based enrichment is superior for comprehensive variant recovery and uniform coverage, providing a robust solution for high-resolution CHIKV genomic surveillance even in ultra-low viral load samples.
Wang et al. (Wed,) studied this question.