Biology subjects

Doughty, R. D.

Publications and source records attributed to Doughty, R. D..

2 recordsLinked to original sources

bioRxiv2026

Min-frame transformation enables more sensitive viral genome alignment

MotivationMaximal unique matches (MUMs) are a fundamental primitive in genome comparison, where they serve as high-confidence anchors for downstream multiple genome alignment. However, because MUMs rely on exact string matching, their effectiveness degrades with increased genome divergence and larger sets of genomes, inhibiting their ability to recover long homologous regions and reducing the number of base pairs covered by the multiple genome alignment. Additionally, existing approaches that improve robustness to mutation, such as spaced seeds or translated alignment methods, introduce trade-offs in specificity, scalability, or computational complexity. MethodsTo address this gap, we introduce the Min-Frame Transformation (MFT), a deterministic encoding of nucleotide sequences to sequences over a transformed alphabet that preserves the coordinate structure of the original sequence. At each position, the MFT selects a k-mer from a local window according to a fixed global ordering and assigns it a character in the transformed alphabet via a predefined mapping. This process captures local sequence context and can mask the impact of mutations, increasing the likelihood that homologous regions remain detectable as exact matches. The resulting transformed sequences can be indexed using standard string data structures, such as suffix arrays and suffix trees, enabling efficient extraction of MUMs without modifying existing algorithms. ImpactThe MFT is a novel computational approach for improving the robustness of MUM-based seeding for genome alignment by producing longer and more contiguous matches that span a greater fraction of the genome, leading to improved alignment coverage and SNP recall. Altogether, these improvements have the potential to result in improvements for downstream viral genome analysis applications such as phylogenetic inference and transmission analysis. FundingTandy Warnow: NSF grant 2316233 Todd J. Treangen: NSF grants 2126387, 2239114, NIH grants U19-AI144297, P01-AI152999

bioinformatics↗

bioRxiv2025

bronko: ultrafast, alignment-free detection of viral genome variation

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 three 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, and iii) 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 similar 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 bronkos potential as a scalable tool for large-scale viral genomic analyses, overcoming longstanding computational barriers for intrahost and interhost characterization of viral variation. Availabilitybronko is implemented in Rust and publicly available at https://github.com/treangenlab/bronko or via conda at https://anaconda.org/channels/bioconda/packages/bronko/overview. All results, evaluations, and other code used in this study are available at https://github.com/treangenlab/bronko-test.

bioinformatics↗