snakemake-workflows/dna-seq-short-read-circle-map

A snakemake workflow for calling extrachromosomal circular DNA in Illumina short-read sequencing data with Circle-Map

Overview

Topics:

Latest release: v1.3.0, Last update: 2024-04-23

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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/snakemake-workflows/dna-seq-short-read-circle-map . --tag v1.3.0

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.

Describe how to configure the workflow (using config.yaml and maybe additional files). All of them need to be present with example entries inside of the config folder.

General settings

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

Sample sheet

Add samples to the TSV file specified via the samples: directive in config/config.yaml. For each sample, the columns sample_name, alias, platform, and group have to be defined.

  • Samples within the same group will be handled jointly. This can for example be multiple samples from the same individual.
  • aliases represent the name of the sample within its group (they can be the same as the sample name, or something simpler / more abstract, like tumor or normal).
  • The platform column needs to contain the used sequencing plaform (with this workflow focused on Circle-Map, this will always be 'ILLUMINA', for now -- but we keep this info for compatibility with other workflows).

Missing values can be specified by empty columns or by writing NA. Lines can be commented out with #.

Unit sheet

For each sample, add one or more sequencing units (runs, lanes or replicates) to the TSV file specified via the units: directive in config/config.yaml. For each unit, the columns unit_name, sample_name, fq1, and fq2 have to be defined.

  • Each unit has a unit_name, which can be for example be a running number, or an actual run, lane or replicate id.
  • Each unit has a sample_name, which associates it with the biological sample it comes from.
  • For each unit, define the two paired FASTQ files (columns fq1, fq2, these can point to anywhere on your system).
  • Optional: Define adapters in the adapters column, by putting cutadapt arguments in quotation marks (e.g. "-a ACGCGATCG -A GCTAGCGTACT"). If adapters have already been removed in your raw data, or if you don't want to remove them, just leave this column empty for the respective units.

Missing values can be specified by empty columns or by writing NA. Lines can be commented out with #.

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