Installation and resource requirements

Quick start

  • Install with conda via Miniforge:

    conda install zamp

  • Install with docker from Dockerhub:

    docker pull metagenlab/zamp:latest

Operating system

zAMP is available on Bioconda which only support Linux and macOS.

If you use windows, you can still run zAMP via WSL.

Installation methods

From source

You can install zAMP from source, by cloning the repository:

git clone https://github.com/metagenlab/zAMP
pip install -e zAMP/
Dependencies:

  • python >=3.11

  • apptainer

  • conda

Conda

You can install zAMP from Bioconda with conda installed from Miniforge:

# Install conda from Miniforge
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh

# Install zamp
conda install zamp

Containers

You can install zAMP by pulling a container image for maximum reproducibility and no extra dependency installation.

We recommend installing zAMP containers via docker or apptainer from any of the container registries below:

  • Dockerhub:

    docker pull metagenlab/zamp:latest

  • ghcr:

    docker pull ghcr.io/metagenlab/zamp:latest

  • biocontainers:

    docker pull quay.io/biocontainers/zamp:1.0.0--pyhdfd78af_1

Resource usage

Some steps in zAMP can be quite resource-intensive, requiring more RAM.

For example, if you want to re-train the RDP classifier on SILVA138.1, you might need around 100GB of RAM.

In terms of duration, and when using the default zAMP module, the bottleneck is usually the DADA2 denoising step, which can take some time with lots of samples.

Actual resource usage like RAM and CPU-time depends on:

  • Number of samples

  • Sequencing depth of each sample

  • Threads set by the user

Note

Usually, a zAMP run duration is less than 1h and does not require more than 32 GB of RAM.