Daniel J. Wilson

This paper introduces ClonalFrame2, a Bayesian method for phylogenetic inference in bacteria that accounts for recombination, showing improved accuracy in simulated and real datasets.

This study identifies genome degradation and convergent mutations in Staphylococcus aureus during severe infections, linking them to adaptation and clinical outcomes.

This paper uses machine learning on whole genome data to improve source attribution of Campylobacter jejuni infections, achieving higher accuracy than traditional methods.

This study performs genome-wide association analyses on 10,228 Mycobacterium tuberculosis genomes to identify genetic variants linked to resistance against 13 antimicrobials.

GenomegaMap estimates genome-wide dN/dS ratios across 10,209 tuberculosis genomes to identify selection pressures shaping the pathogen's evolution.

The paper introduces the harmonic mean p-value (HMP) to combine dependent tests, improving statistical power while controlling the family-wise error rate in large-scale hypothesis testing.

The paper introduces a Bayesian method for dating bacterial phylogenies, leveraging precomputed phylogenetic relationships to improve speed and scalability.

This paper introduces BadTrIP, a Bayesian method to infer host-to-host transmission using genomic variants and epidemiological data, showing improved accuracy over existing approaches.

Severe S. aureus infections arise from the nasal microbiome through adaptive evolution, with specific mutations enriched in infecting bacteria compared to colonizing ones.