Running the recipe

The recipe supports running the workflow using Python in a conda environment or using Nextflow in either a conda environment or in a Docker container.

Step 1: Initialize the computational environment¶

This step can be skipped if you are using Nextflow with Docker.

Install conda. We use conda as an environment manager.

# Install computational environment
conda env create --force --file environment.yml

# Initialize the environment
conda activate pooled-profiling

Step 2: Configure the workflow¶

Edit config/experiment.json to set parameters specific to your experiment. config/defaults.yaml contains parameters that are less likely to need changing between experiments. See Configuration for every parameter.

Step 3: Run the workflow¶

Run the workflow with Python¶

python run.py

To pass configuration files that have custom location or naming:

python run.py --defaults-config path/to/defaults.yaml --experiment-config path/to/experiment.json

run.py checks that experiment.json contains all required keys and then runs each step in order for which the matching perform_* flag is true. It stops if any step exits with an error.

A single step can also be run directly by passing it the two config paths:

python scripts/1.process-SBS.py config/defaults.yaml config/experiment.json

Run the workflow with Nextflow¶

The same steps can be run as a Nextflow pipeline (main.nf) instead of run.py. Each step is still one of the scripts/N.*.py files; Nextflow orchestrates which steps run and in what order, based on the perform_* flags in your experiment config.

Install Nextflow (see the Nextflow docs), activate the pooled-profiling conda environment from Step 1, then run:

nextflow run main.nf

To use custom configuration file locations:

nextflow run main.nf --defaults_config path/to/defaults.yaml --experiment_config path/to/experiment.json

Run Nextflow with Docker¶

Instead of installing the conda environment locally, you can run every step inside a Docker container built from the same environment.yml. Install Docker and make sure the daemon is running, then build the image once:

docker build -t pooled-profiling .

Then run the workflow with the docker profile:

nextflow run main.nf -profile docker

The container only contains the Python environment; your repo checkout and data directories (file_location, library_location) are bind-mounted automatically based on your config files, so editing scripts or configs never requires rebuilding the image — only changes to environment.yml do.