# PyDamage Pydamage, is a Python software to automate the process of contig damage identification and estimation. After modelling the ancient DNA damage using the C to T transitions, Pydamage uses a likelihood ratio test to discriminate between truly ancient, and modern contigs originating from sample contamination. ## Installation ### With [conda](https://docs.conda.io/en/latest/) (recommended) ```bash conda install -c bioconda pydamage ``` ### With pip ```bash pip install pydamage ``` ### Install from source to use the development version Using pip ```bash pip install git+ssh://git@github.com/maxibor/pydamage.git@dev ``` By cloning in a dedicated conda environment ```bash git clone git@github.com:maxibor/pydamage.git cd pydamage git checkout dev conda env create -f environment.yml conda activate pydamage pip install -e . ``` Running tests ```bash python -m pytest ``` ## Quick start ```bash pydamage --outdir result_directory analyze aligned.bam ``` > Note that if you specify `--outdir`, it has to be before the PyDamage subcommand, example: `pydamage --outdir test filter pydamage_results.csv` ## CLI help Command line interface help message ```bash pydamage --help ``` ## Documentation [pydamage.readthedocs.io](https://pydamage.readthedocs.io) ## Cite PyDamage has been published in PeerJ: [10.7717/peerj.11845](https://doi.org/10.7717/peerj.11845) ``` @article{borry_pydamage_2021, author = {Borry, Maxime and Hübner, Alexander and Rohrlach, Adam B. and Warinner, Christina}, doi = {10.7717/peerj.11845}, issn = {2167-8359}, journal = {PeerJ}, language = {en}, month = {July}, note = {Publisher: PeerJ Inc.}, pages = {e11845}, shorttitle = {PyDamage}, title = {PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly}, url = {https://peerj.com/articles/11845}, urldate = {2021-07-27}, volume = {9}, year = {2021} } ```