pipe19

Large scale sequencing of SARS-CoV-2 in Region Midtjylland, Denmark

Pipeline overview

We use the Artic V3 primers to amplify 98 overlapping amplicons. The amplicons are then fragmented and we sequence each isolate with QiaSeq on Illumina NextSeq and NovaSeq platforms at the local Department of Molecular Medicine.
This pipeline accepts the demultiplexed data from this sequencing protocol and generally follows the iVar consensus pipeline:

Steps:

• Trim index-adaptors from reads with trim-galore.
• Map reads with bwa mem.
• Trim primer regions from alignment with iVar trim.
• Call consensus with iVar consensus (read-depth >= 10, base-frequency >= 0.8).
• Call lineage with Pangolin.
• Call clade with Nextclade.

For each batch of samples, the following is done:

• Integration of file metadata, pangolin and nextclade calls.
• Compression of raw- and consensus data, tailored for upload.
• Generation of a QC/surveillance report.

Dependencies:

• Singularity (or a similar docker-compatible platform)
• Python 3.7
  • yaml (pyyaml)
  • pandas
  • gwf (gwf-org)

Setup

1. Clone the repo into the dir where you want its base to be: git clone git@github.com:cmkobel/pipe19.git

2. Install dependencies listed in conda_env.yml: conda env create -f conda_env.yml

3. Set up your local parameters in the config_example.yaml file.