BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies

Background Microbial genome-wide association studies (GWAS) are crucial for linking genetic variation to phenotypic traits in bacteria. However, current tools often involve complex manual processing, limited scalability, and fragmented workflows, which constrain large-scale or routine bacterial GWAS. Results We developed BaGPipe, an automated and flexible bacterial GWAS pipeline built using Nextflow and incorporating Pyseer for association analysis. BaGPipe integrates pre-processing, statistical analysis, and downstream visualisation into a unified workflow that is reproducible and easy to deploy across diverse computational environments. BaGPipe was validated on a publicly available dataset of Streptococcus pneumoniae whole-genome sequences, and reproduced published findings with improved computational efficiency. BaGPipe was then applied to a dataset of Staphylococcus aureus whole-genome sequences, successfully identifying known and novel antibiotic resistance associations. Conclusions By offering an accessible, efficient, and reproducible platform, BaGPipe accelerates bacterial GWAS and facilitates deeper exploration into the genetic underpinnings of phenotypic traits. BaGPipe is freely available at https://github.com/sanger-pathogens/BaGPipe.