David L. Gally

This paper develops a machine learning model that integrates bacterial and viral genomic data to predict phage activity against E. coli, improving prediction accuracy by incorporating phage gene content.

This study benchmarks in silico AMR prediction tools using nanopore data, finding that assembly strategy and data volume impact accuracy, but no tool perfectly matches lab-based AST results.

This paper evaluates the use of an endotoxin-reduced E. coli strain for phage production, showing it can generate diverse phage with low endotoxin and minimal downstream processing.

This study identifies mechanisms by which E. coli ST131 evades phage killing through quorum sensing and capsule expression, and develops a phage therapy strategy to overcome these defenses in urine-based conditions.

The paper identifies a prophage-encoded sRNA, HnrS, that counteracts xenogenic silencing by repressing Hha, enabling virulence gene expression in E. coli.

This study analyzes the genome of Klebsiella variicola from diverse sources to identify genetic adaptations linked to human infection.

This paper uses machine learning to predict effective phage cocktails for treating E. coli urinary tract infections based on bacterial genome data.

The paper compares stx2a-encoding prophages in UK STEC O157:H7 sub-lineages, finding high diversity and independent acquisition, while stx2c-encoding prophages show vertical inheritance.