If running the pipeline in a local environment, we highly recommend using Docker
Docker is a great way to run programs, as it manages all software installations and allows the pipeline to be run in an identical software environment across a range of systems.
Nextflow has excellent integration with Docker, and beyond installing the two tools, not much else is required. The hybrid-assembly profile comes with a configuration profile for docker, making it very easy to use. This also comes with the required presets to use the AWS iGenomes resource, meaning that if using common reference genomes you just specify the reference ID and it will be autaomtically downloaded from AWS S3.
First, install docker on your system: Docker Installation Instructions
Then, simply run the analysis pipeline.
Nextflow will recognise TrisKast/Cas_mediated_enrichment_read_mapping_pipeline and download the pipeline from GitHub.
The public docker images are tagged with the same version numbers as the code, which you can use to ensure reproducibility. When running the pipeline, specify the pipeline version with -r, for example -r v1.3. This uses pipeline code and docker image from this tagged version.