These instructions assume that you have installed MinKNOW and are able to run it.
We also assume that you are using conda – See instructions here to install conda on your machine.
Create a new conda environment and activate it via:
conda create -n artic-rampart -y nodejs=12 # any version >10 should be fine
conda activate artic-rampart
Or install NodeJS into your currently activated environment via:
conda install -y nodejs=12 # any version >10 should be fine
conda install -y artic-network::rampart
…this will install the latest release. To install a particular version (say, 1.1.0) use:
conda install -y artic-network::rampart=1.1.0
Note that you may already have some or all of these in your environment, in which case they can be skipped. Additionally, some are only needed for certain analyses and can also be skipped as desired.
If you are installing RAMPART into the artic-ncov2019 conda environment, you will already have all of these dependencies.
Python, biopython, snakemake and minimap2 are required
conda install -y "python>=3.6"
conda install -y anaconda::biopython
conda install -y -c conda-forge -c bioconda "snakemake-minimal=5.8.1" # later snakemake versions will not work currently
conda install -y bioconda::minimap2=2.17
If you are using MinKNOW to separate samples by barcodes, you don’t need Porechop, however if you require RAMPART to perform demuxing then you must install the ARTIC fork of Porechop:
python -m pip install git+https://github.com/artic-network/Porechop.git@v0.3.2pre
If you wish to use the post-processing functionality available in RAMPART to bin reads, then you’ll need binlorry:
python -m pip install binlorry==1.3.0_alpha1
rampart --help
(1) Clone the Github repo
git clone https://github.com/artic-network/rampart.git
cd rampart
(2) Create an activate the conda environment with the required dependencies.
You can either follow steps 1 & 3 above, or use the provided environment.yml file via
conda env create -f environment.yml
conda activate artic-rampart
(3) Install dependencies using npm
npm install
(4) Build the RAMPART client bundle
npm run build
(5) (optional, but recommended) install rampart globally within the conda environment
so that it is available via the rampart command
npm install --global .
Check that things work by running rampart --help