Welcome to microGWAS’s documentation!

microGWAS is a snakemake-powered pipeline to carry out an end-to-end microbial GWAS analysis. Starting from genome assemblies and a phenotype file, microGWAS will run a number of single-locus and whole genome associations using pyseer, and annotate the associations results, as well as generating a number of functional enrichment tests.

Citation

If you find the microGWAS pipeline useful, please cite it as:

Burgaya, J., Damaris, B. F., Fiebig, J., & Galardini, M. (2025).
microGWAS: a computational pipeline to perform large-scale bacterial genome-wide association studies. Microbial Genomics, 11(2), 001349.

Please also consider citing the underlying tools used by the pipeline. See Underlying tools for more details.

Contents

• Inputs
  • Phenotype file
  • Sample’s genome sequences
• Usage
  • Installation
    • Download the latest release from GitHub
    • Clone the repository using git
    • Create a new repository from the GitHub template
  • Other preparatory steps
    • Creating the base microGWAS environment
    • Setup the eggnog-mapper database
  • Configure the pipeline run
    • Prepare the input phenotype file
    • Edit the pipeline configuration file
      • Which lineage file to use?
  • Run the pipeline
    • Run the bootstrapping script
    • Run the actual snakemake pipeline
    • Running specific rules
  • Troubleshooting
    • Avoid using samples as references
  • Testing
• Beginner’s guide
  • Prerequisites
  • 1. Before you begin
    • i. Install Conda (if not already installed):
    • ii. Install mamba (if not already installed):
    • iii. Installing microGWAS:
    • iv. Set up the microGWAS conda environment (if not present already):
    • v. Prepare your input data:
    • vi. Set up the environment and configure the pipeline:
  • 2. Running the microGWAS pipeline
    • Customizing your analysis
  • 3. Understanding the results
    • a. Unitig-based association analysis
    • b. Gene cluster-specific k-mer association analysis
    • c. Functional enrichment analysis
    • d. Other outputs
  • 4. Troubleshooting
• Outputs
  • General output folder structure
  • Output files
• Rules
• Underlying tools