dbespiatykh/RDscan
A snakemake workflow for regions of difference discovery in Mycobacterium tuberculosis complex (MTBC) samples
Overview
Topics: bioinformatics snakemake mycobacterium mycobacterium-tuberculosis-complex structural-variants bacterial-genome-analysis mycobacterium-tuberculosis
Latest release: v1.0.3r, Last update: 2023-01-26
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/dbespiatykh/RDscan . --tag v1.0.3r
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
Following the explanations in the config.yml, you should modify it according to your needs.
Samples sheet
The location of this sheet must be specified in the config.yml.
It should be formatted like this:
| Run_accession | R1 | R2 |
|---|---|---|
| SRR2024996 | /path/to/SRR2024996_1.fastq.gz | /path/to/SRR2024996_2.fastq.gz |
| SRR2024925 | /path/to/SRR2024925_1.fastq.gz | /path/to/SRR2024925_2.fastq.gz |
Run_accession - Run accession number or sample name;
R1 - Path to the first read pair;
R2 - Path to the second read pair.
- Both
R1andR2should be specified with reads paths.
RDs
RD.bed table.
Columns are:
1. Chromosome name;
2. Start position of the RD;
3. End position of the RD;
4. Name of the RD.
| NC_000962.3 | 29988 | 34322 | RDcap_Spain1 |
| NC_000962.3 | 34789 | 35209 | RD301 |
| NC_000962.3 | 76163 | 84826 | RD105ext |
| NC_000962.3 | 79571 | 83036 | RD105 |
| … | … | … | … |
Linting and formatting
Linting results
None
Formatting results
None