System Requirements¶

• python >= 3.8

• numba

• numpy

• matplotlib

• pyyaml

• scipy

• seaborn

For the docs:

• sphinx

For parameter visualization:

• bokeh >= 3.1.1

Installation¶

We recommend the installation using conda:

$ conda create -n ddrage -c bioconda ddrage
$ source activate ddrage

Alternatively, you can download the source code from bitbucket and install it using pip:

$ git clone https://bitbucket.org/genomeinformatics/rage.git ddrage
$ cd ddrage
/ddrage$ pip install .  # basic version
# OR
# to install with support to build the documentation and visualize bbd parameters
/ddrage$ pip install .[documentation,BBD-visualization]

Usage¶

To simulate a ddRAD data set, call ddrage from the command line:

$ ddrage

you can specify parameters to change data set parameters such as number of individuals (-n), nr of loci (-l), and coverage (--coverage):

$ ddrage -n 6 -l 10000 --coverage 30

This creates a data set with reads from 6 individuals at 10000 loci with an expected coverage of 30.

A more detailed tutorial can be found on readthedocs.

Citing ddRAGE¶

Our paper describing ddRAGE has been published in Molecular Ecology Resources. You can cite it as follows:

Timm H, Weigand H, Weiss M, Leese F, Rahmann S. ddRAGE: A data set generator to evaluate ddRADseq analysis software. Mol Ecol Resour. 2018;18:681–690. https://doi.org/10.1111/1755-0998.12743

BibTeX version:

@article{timm2018ddrage,
  title={ddrage: A data set generator to evaluate ddRADseq analysis software},
  author={Timm, Henning and Weigand, Hannah and Weiss, Martina and Leese, Florian and Rahmann, Sven},
  journal={Molecular Ecology Resources},
  volume={18},
  number={3},
  pages={681--690},
  year={2018},
  url = {http://dx.doi.org/10.1111/1755-0998.12743},
  doi = {10.1111/1755-0998.12743},
}

The source code itself is also published on Zenodo under the DOI 10.5281/zenodo.4390216.

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