Skip to content

marcoteix/StrainGECloud

 
 

Repository files navigation

StrainGE: Strain-level Genome Exploration

StrainGE is a set of tools to analyse the within-species strain diversity in bacterial populations. It consists of two main components: 1) StrainGST: Strain Genome Search tool, a tool to find close reference genomes for strains present in a sample and 2) StrainGR: Strain Genome Recovery, a tool to perform strain-aware variant calling at low coverages.

Documentation Status Python Package Index DOI

Dependencies

Python packages

  • Python >= 3.7
  • NumPy
  • SciPy
  • matplotlib
  • scikit-bio >= 0.5
  • scikit-learn >= 0.24
  • pysam
  • h5py
  • intervaltree

Bioinformatics tools

  • bwa
  • samtools
  • mummer

Installation

Python Package Index

pip install strainge

Warning: NumPy already has to be installed otherwise the above command will fail. You'll have to make sure all tools like bwa, samtools and mummer are installed as well.

Conda

  1. Install Anaconda or miniconda (if not already present on your system)

  2. Create a new environment:

    conda create -n strainge python=3

  3. Activate the environment:

    source activate strainge

  4. Enable bioconda and conda-forge channels:

    conda config --add channels bioconda
    conda config --add channels conda-forge
    
  5. Install StrainGE:

    conda install strainge

Optional tip: also consider installing mamba before installing StrainGE for much faster conda operations.

Documentation

The documentation can be read on readthedocs.

Citation

Dijk, Lucas R. van, Bruce J. Walker, Timothy J. Straub, Colin J. Worby, Alexandra Grote, Henry L. Schreiber, Christine Anyansi, et al. 2022. “StrainGE: A Toolkit to Track and Characterize Low-Abundance Strains in Complex Microbial Communities.” Genome Biology 23 (1): 74. https://doi.org/10.1186/s13059-022-02630-0.

About

Cloud-native strain-level analysis tools

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 95.0%
  • C++ 5.0%