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
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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
.
To configure this workflow, modify config/config.yaml
according to your
needs, following the explanations provided in the file.
The sample sheet contains all samples to be analyzed by UnCoVar.
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.
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