Abstract
As viral sequencing datasets continue to grow, traditional alignment-based variant calling pipelines are becoming computationally prohibitive. To address these challenges, we developed bronko, an ultrafast alignment-free framework for detecting viral variation directly from sequencing data. The novel computational approach implemented in bronko allows scaling to massive viral sequencing datasets and has four key components: i) a locality-sensitive bucketing function to rapidly identify single-nucleotide polymorphisms (SNPs) relative to reference(s), ii) a direct k-mer count pseudo-mapping approach that approximates a pileup without alignment, iii) a greedy de novo assembly algorithm to reconstruct mutation-dense regions and indels and iv) a streaming-based sliding window outlier test to estimate baseline noise across the genome and precisely differentiate real minor variants from noise. Together, these components yield near-linear computational complexity with respect to sequencing depth, enabling bronko to process thousands of viral samples rapidly on modest hardware. Our results are threefold: 1) On simulated amplicon sequencing, bronko recovers variants with higher precision and comparable recall to existing tools while running up to one to three orders of magnitude faster; 2) bronko generates sequence alignments directly from sequencing data, with SNP content nearly identical to that of whole-genome alignment while also running in a fraction of the time, and 3) applying bronko to longitudinal sequencing data from chronically infected SARS-CoV-2 patients revealed consistent patterns of intrahost diversification and adaptive mutations over time. Altogether, these results demonstrate bronko's potential as a scalable tool for large-scale viral genomic analyses, overcoming longstanding computational barriers for intrahost and interhost characterization of viral variation.