Installation Requirements

Minimum Requirements

1. Nextflow version 25.10.5

   • Make the nextflow binary executable (chmod 755 nextflow) and ensure it is available in your $PATH.
   • If your existing JAVA install does not support the newest Nextflow version, use Amazon's JAVA (OpenJDK): Corretto.

2. Container or package manager: One of micromamba, docker, singularity, or apptainer installed and available in $PATH.

   • To install micromamba, follow these installation steps.
   • Just the curl step is sufficient for running workflows.
   • After installation, upgrade to at least version 2.3.2:

   micromamba --version
   micromamba self-update -c conda-forge

3. Note: Due to testing priority, only container (apptainer, singularity) based workflow has been fully validated. Please report issues via GitHub if you encounter failures.

4. Minimum hardware: 20 CPU cores and 128 GB of memory for all workflow steps.

Computational Resources

Most pipeline processes use the process_low_turbo resource label (20 CPUs, 128 GB memory per task). By default, MAGGIC uses 10 CPU cores where possible. Adjust with --max_cpus:

./cpipes \
    --pipeline maggic \
    --input /path/to/metagenomic_fastq/dir \
    --output /path/to/output \
    --max_cpus 5 \
    -profile ahptainer \
    -resume

Runtime Profiles

Run the workflow on different compute environments by specifying a profile:

./cpipes \
    --pipeline maggic \
    --input /path/to/fastq_pass_dir \
    --output /path/to/where/output/should/go \
    -profile your_institution

Output goes to the --output path. Nextflow reports are stored in the working directory where cpipes is run.

Adding a Custom Profile

Append to conf/computeinfra.config:

your_institution {
    process.executor = 'sge'
    process.queue = 'normal.q'
    apptainer.enabled = false
    apptainer.autoMounts = true
    docker.enabled = false
    params.enable_conda = true
    conda.enabled = true
    conda.useMicromamba = true
    params.enable_module = false
}

By default, all software provisioning is disabled except conda. You can remove process.queue to let MAGGIC request resources automatically.

Cloud Computing

Add runtime profiles for cloud environments. Example AWS Batch:

my_aws_batch {
    executor = 'awsbatch'
    queue = 'my-batch-queue'
    aws.batch.cliPath = '/home/ec2-user/miniconda/bin/aws'
    aws.batch.region = 'us-east-1'
    apptainer.enabled = false
    apptainer.autoMounts = true
    docker.enabled = true
    params.conda_enabled = false
    params.enable_module = false
}