A logical, reasonably standardized, but flexible project structure for doing and sharing bioinformatics work.
This cookiecutter template was developed by the Max Planck Institute of Immunobiology and Epigenetics Bioinformatics Core Facility with the aim of establishing a standardized project directory structure within the institute. It is a fork of cookiecutter-fair-data-science, with a healthy dose of Snakemake workflow best practices mixed in. There is less focus on building a python package that can train and run machine learning models, but rather on building bioinformatics workflows that can run on the MPI-IE cluster according to FAIR principles.
- Python 2.7 or 3.5+
- Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:
$ pip install cookiecutteror
$ conda config --add channels conda-forge
$ conda install cookiecutterOn the MPI-IE infrastructure, you can also run module load cookiecutter.
cookiecutter gh:maxplanck-ie/cookiecutter-bioinformatics-project
The directory structure of your new project looks like this:
├── CITATION.cff <- Contains metadata on how the project might eventually be published.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── config <- Configuration options for the analysis.
| ├── config.yaml <- Snakemake config file.
| └── samples.tsv <- A metadata table for all the samples run in the analysis.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── environment.yaml <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `conda env export > environment.yaml`
│
├── img <- A place to store images associated with the project/pipeline, e.g. a
│ a figure of the pipeline DAG.
│
├── notebooks <- Jupyter or Rmd notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── resources <- Place for data. By default excluded from the git repository.
│ ├── external <- Data from third party sources.
│ └── raw_data <- The original, immutable data dump.
│
├── results <- Final output of the data processing pipeline. By default excluded from the git repository.
│
├── sandbox <- A place to test scripts and ideas. By default excluded from the git repository.
│
├── scripts <- A place for short shell or python scripts.
│
├── setup.py <- Makes project pip installable (pip install -e .) so src can be importe
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
├── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
│
├── workflow <- Place to store the main pipeline for rerunning all the analysis.
│ ├── envs <- Contains different conda environments in .yaml format for running the pipeline.
│ ├── rules <- Contains .smk files that are included by the main Snakefile, including common.smk for functions.
│ ├── scripts <- Contains different R or python scripts used by the script: directive in Snakemake.
│ ├── Snakefile <- Contains the main entrypoint to the pipeline.
│
├── workspace <- Space for intermediate results in the pipeline. By default excluded from the git repository.
We welcome contributions! Feel free to fork and add pull requests.
pip install -r requirements.txt
py.test tests