mariesaitou/gwidecodeml

A Python package for testing evolutionary hypotheses in genome-wide approaches.

GWideCodeML

GWideCodeML is a Python package that provides support for testing evolutionary hypothesis using codeml (from the PAML package) in a genome-wide framework.

For further information on installation and usage, please visit https://github.com/lauguma/GWideCodeML/wiki

Installation

Option 1:

1. Download GWideCodeML
   git clone https://github.com/lauguma/GWideCodeML.git
cd GWideCodeML

2. Install
   python setup.py install

3. Run GWideCodeML: if succesfull installation, gwidecodeml executable is created. You can check it by writing in your console:
   gwidecodeml -h

Note: if you choose this option, all requirements must be satisfaied before running GWideCodeML (e.g. codeml must be installed and available from the working directory), see Requirements section.

Option 2: conda environment

(easier, recommended option)

0. Install and initialize miniconda
   (skip in case you already have a conda env and know how it works)
   https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html
   close and open a new console before continue

1. Download GWideCodeML
   git clone https://github.com/lauguma/GWideCodeML.git
cd GWideCodeML

2. Create a conda environment from yml file
   conda env create -f gwidecodeml_conda.yml

3. Activate conda environment
   conda activate gwcodeml

4. Install and run GWideCodeML
   python setup.py install
gwidecodeml -h

Requirements

Python >= 3.5

Python libraries

• Biopython
• Scipy
• ete3

Software

• codeml from the PAML package (Phylogenetics Analysis by Maximum Likelihood). http://abacus.gene.ucl.ac.uk/software/paml.html \
• fastTree: http://www.microbesonline.org/fasttree/

Citation

Macías L. G., Barrio E. and Toft. C. "GWideCodeML: a Python package for testing evolutionary hypothesis at the genome-wide level" G3: Genes, Genomes, Genetics (2020) doi:10.1534/g3.120.401874.

Our pipeline uses third-party software:

Yang, Z. "PAML 4: a program package for phylogenetic analysis by maximum likelihood." Mol Biol Evol (2017) doi: 10.1093/molbev/msm088

Huerta-Cepas, J., Serra, F and Bork, P. "ETE 3: Reconstruction, analysis and visualization of phylogenomic data." Mol Biol Evol (2016) doi: 10.1093/molbev/msw046