open2c/distiller-sm
a Snakemake version of distiller - the Open2C Hi-C mapping workflow
Overview
Topics: bioinformatics bioinformatics-pipeline hi-c hic snakemake snakemake-pipeline snakemake-workflow
Latest release: None, Last update: 2025-01-31
Linting: linting: failed, 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/open2c/distiller-sm . --tag None
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.
For each sample the input files are typically a pairs of .fastq.gz files, one with forward and one with reverse reads. Additionally, the input can be specified as an accession in the SRA database, and reads will be downloaded automatically.
For each biological sample multiple technical replicates ("lanes") can be provided, they are then merged at the stage of pairs.
Biological samples (e.g. biological replicates) can also be grouped into "library groups", so they are merged at the level of coolers.
You need to provide the name of the genome assembly, the path to the bwa index with a wildcard, and to the chromsizes file. The index doesn't need to already exist, as long as provided path matches exactly the fasta file with the reference genome (e.g. sequence in mm10.fa.gz, provide mm10.fa.gz*). If the index doesn't exist, it will be created.
Mapping can be done with bwa-mem, bwa-mem2, bwa-meme (all produce identical or near-identical results), or chromap. Chromap outputs .pairs directly and works very fast, but you lose the flexibility of custom parising options.
Linting and formatting
Linting results
Using workflow specific profile workflow/profiles/default for setting default command line arguments.
KeyError in file /tmp/tmp6nl0o7yp/workflow/Snakefile, line 17:
'output'
File "/tmp/tmp6nl0o7yp/workflow/Snakefile", line 17, in <module>
Formatting results
None