02. Installation an dependencies
Notice that the pipeline requires the "UTF-8" encoding for text files, in order to run both the conda environment building and to allow the reading of configuration tables by the pipeline.
Usually ATAC-seq experiments represent a large amount of data that may be difficult to analyze on a desktop machine, for this reason we strongly recommend the use of a cluster/high-performance computation (HPC) system to run analyses with this pipeline.
In the Example for testing section of this Wiki we provide some insights on CPU and memory usage for a test data set.
This pipeline depends on a conda environment. The installation of the environment can be achieved using the snakeATAC_conda_env_stable.yaml file, or installing the bioconda recipe snakeatac_env [recommended].
To download the repository:
- navigate to the directory where the repository should be downloaded by typing, for instance:
cd $HOME - download the GitHub repository with
git clone https://github.com/sebastian-gregoricchio/snakeATAC, or click on Code > Download ZIP on the GitHub page
- install the conda environment from the .yaml environment file contained in the repository:
conda env create -f $HOME/snakeATAC/workflow/envs/snakeATAC_conda_env_stable.yaml - activate the conda environment:
conda activate snakeATAC(if the env is not activated the pipeline won't work properly)
Usage of mamba is recommended since it is faster than conda itself. Follow the steps below:
- create an empty snakeATAC environment:
conda create -n snakeATAC - if not already available, create a mamba environment:
conda create -n mamba conda-forge::mamba - activate the mamba environment:
conda activate mamba - now install snakeatac_env from mamba in snakeATA environment:
mamba install -c bioconda -c anaconda -c conda-forge -c defaults -n snakeATAC snakeatac_env - now close the mamba env and activate the snakeATAC one (if the env is not activated the pipeline won't work properly):
conda deactivate; conda activate snakeATAC
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