IKIM-Essen/uncovar

Transparent and robust SARS-CoV-2 variant calling and lineage assignment with comprehensive reporting.

Overview

Topics: sars-cov-2 variant-calling lineage-assignment

Latest release: v1.1.1, Last update: 2024-11-07

Linting: linting: passed, Formatting:formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/IKIM-Essen/uncovar . --tag v1.1.1

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

General settings

To configure this workflow, modify config/config.yaml according to your needs, following the explanations provided in the file.

Sample sheet

The sample sheet contains all samples to be analyzed by UnCoVar.

Auto filling

UnCoVar offers the possibility to automatically append samples to the sample sheet. To load your data into the workflow execute

snakemake --cores all --use-conda update_sample

with the root of the UnCoVar as working directory. It is recommended to use the following structure to when adding data automatically:

├── archive
├── incoming
└── snakemake-workflow-sars-cov2
    ├── data
    └── ...

However, this structure is not set in stone and can be adjusted via the config/config.yaml file under data-handling. Only the following path to the corresponding folders, relative to the directory of UnCoVar are needed:

  • incoming: path of incoming data, which is moved to the data directory by the preprocessing script. Defaults to ../incoming/.
  • data: path to store data within the workflow. defaults to data/.
  • archive: path to archive data from the results from the analysis to. Defaults to ../archive/.

The incoming directory should contain paired end reads in (compressed) FASTQ format. UnCoVar automatically copies your data into the data directory and moves all files from incoming directory to the archive. After the analysis, all results are compressed and saved alongside the reads.

Moreover, the sample sheet is automatically updated with the new files. Please note, that only the part of the filename before the first '_' character is used as the sample name within the workflow.

Manual filling

Of course, samples to be analyzed can also be added manually to the sample sheet. For each sample, the a new line in config/pep/samples.csv with the following content has to be defined:

  • sample_name: name or identifier of sample
  • fq1: path to read 1 in FASTQ format
  • fq2: path to read 2 in FASTQ format
  • date: sampling date of the sample
  • is_amplicon_data: indicates whether the data was generated with a shotgun (0) or amplicon (1) sequencing

Linting and formatting

Linting results

None

Formatting results

None