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Detect interesting SARS-CoV-2 spike protein variants from Sanger sequencing data.

Project description

Detect interesting SARS-CoV-2 spike protein mutations from Sanger sequencing data

install with bioconda Docker Cloud Build Status

covid-spike-classification is a script to call interesting SARS-CoV-2 spike protein mutations from Sanger sequencing to support the Danish COVID-19 monitoring efforts.

Using Sanger-sequenced RT-PCR product of the spike protein, this tool should pick up all relevant mutations currently tracked (see covid_spike_classification/ for the full list of tracked mutations) and give a table with one row per sample and a yes/no/failed column per tracked mutation.

This workflow is built and maintained at

If you found this tool useful, please cite


covid-spike-classification is distributed via this git repository, pypi or bioconda.


Installing via bioconda is the fastest way to get up and running:

conda create -n csc -c conda-forge -c bioconda covid-spike-classification
conda activate csc

git & pypi

When installing via git or pypi, you first need to install the external binary dependencies.

covid-spike-classification depends on three excellent tools to do most of the work:

  • tracy (versions 0.5.3 & 0.5.7 tested)
  • bowtie2 (version 2.4.2 tested)
  • samtools (versions 1.10 & 1.11 tested)

If you have conda installed, the easiest way to get started is to just install these via calling

git clone
cd covid-spike-classification
conda env create -n csc -f environment.yml
conda activate csc
pip install .

Docker, Podman, Singularity

While not technically an installation method, covid-spike-classification is also shipped as an OCI container. To use it, you ideally run the container from a workflow management system like Snakemake or Nextflow that will take care of mounting filesystems into the container for you.

The OCI container image is available from the Docker Hub kblin/covid-spike-classification repository.


You also need to generate the samtools and bowtie2 indices for your reference genome. We ship a copy of NC_045512 and a script to generate these indices:

conda activate csc
cd ref
cd ..


Assuming you used above instructions to install via conda, you can run the tool like this:

conda activate csc
covid-spike-classification --reference /path/to/your/reference.fasta --outdir /path/to/result/dir /path/to/sanger/reads/

Notably, you can provide the input either as a ZIP file or as a directory, as long as they directly contain the ab1 files you want to run the analysis on.

See also the --help output for more detailed usage information.


All code is available under the Apache License version 2, see the LICENSE file for details.

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