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Background <p>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.</p> Results <p>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 <i>Streptococcus pneumoniae</i> whole-genome sequences, and reproduced published findings with improved computational efficiency. BaGPipe was then applied to a dataset of <i>Staphylococcus aureus</i> whole-genome sequences, successfully identifying known and novel antibiotic resistance associations.</p> Conclusions <p>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 <a href="https://github.com/sanger-pathogens/BaGPipe">https://github.com/sanger-pathogens/BaGPipe</a>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Kuangyi Charles Wei,
  • Beth Blane,
  • Jacqueline Toussaint,
  • Sandra Reuter,
  • Michelle S. Toleman,
  • Mili Estee Torok,
  • Sharon J. Peacock,
  • Ewan M. Harrison,
  • Dinesh Aggarwal,
  • William Roberts-Sengier

摘要

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.