Metadata-Version: 2.1
Name: covid-spike-classification
Version: 0.6.0
Summary: Detect interesting SARS-CoV-2 spike protein variants from Sanger sequencing data.
Home-page: https://github.com/kblin/covid-spike-classification/
Author: Kai Blin
Author-email: kblin@biosustain.dtu.dk
License: Apache Software License
Description: # Detect interesting SARS-CoV-2 spike protein mutations from Sanger sequencing data
        
        [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/covid-spike-classification/README.html)
        [![Docker Cloud Build Status](https://img.shields.io/docker/cloud/build/kblin/covid-spike-classification?style=flat)](https://hub.docker.com/r/kblin/covid-spike-classification)
        
        `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/core.py`](https://github.com/kblin/covid-spike-classification/blob/main/covid_spike_classification/core.py#L15-L35)
        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 https://github.com/kblin/covid-spike-classification
        
        If you found this tool useful, please cite https://www.medrxiv.org/content/10.1101/2021.03.27.21252266v1
        
        ## Installation
        
        `covid-spike-classification` is distributed via this git repository, pypi or bioconda.
        
        
        ### Bioconda
        
        Installing via bioconda is the fastest way to get up and running:
        
        ```sh
        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
        ```sh
        git clone https://github.com/kblin/covid-spike-classification.git
        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](https://snakemake.github.io/)
        or [Nextflow](https://www.nextflow.io/) 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`](https://hub.docker.com/r/kblin/covid-spike-classification)
        repository.
        
        
        ## Setup
        
        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:
        
        ```sh
        conda activate csc
        cd ref
        ./build_indices.sh
        cd ..
        ```
        
        ## Usage
        
        Assuming you used above instructions to install via conda, you can run the tool like this:
        
        ```sh
        conda activate csc
        covid-spike-classification --reference /path/to/your/reference.fasta --outdir /path/to/result/dir /path/to/sanger/reads/dir_or.zip
        ```
        
        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.
        
        
        ## License
        All code is available under the Apache License version 2, see the
        [`LICENSE`](LICENSE) file for details.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: testing
