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.

General configuration

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

Input

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

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