Jacqueline Toussaint

This paper introduces a Bayesian ordered logistic regression GWAS model for bacterial antimicrobial resistance phenotypes, showing improved variant discovery and MIC prediction by accounting for threshold-dependent effects.

BaGPipe automates bacterial GWAS workflows, improving efficiency and reproducibility by integrating preprocessing, analysis, and visualization in a scalable pipeline.

The paper develops a model to analyze pangenome dynamics and finds that differences in bacterial pangenomes are mainly due to variation in the number of rapidly exchanged genes.

PopPIPE clusters bacterial populations and detects transmission events using reference-free methods, outperforming traditional approaches in nosocomial outbreak analysis.

A uniformly processed set of 2,440,377 bacterial and archaeal genomes with search tools and annotations, plus an AI-mined peptide set yielding ATB20, active in a mouse skin infection model.