mrvollger/StainedGlass
Make colorful identity heatmaps of genomic sequence
Overview
Topics:
Latest release: v0.6, Last update: 2024-07-16
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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/mrvollger/StainedGlass . --tag v0.6
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.
Below are the options that can be changed in config.yaml.
A sample/prefix identifier to append to your result files
sample: test
Path to a fasta file to deploy the workflow on
fasta: .test/small.fasta
Size of the window in which to breakup the input fasta before all by all alignment.
window: 5000
Setting for the minimap2 -f parameter. A smaller number will increase sensitivity at the cost of runtime.
See the minimap2 man page for more details.
mm_f: 10000
The number of alignment jobs to distribute the workflow across. Does not change final output.
nbatch: 1
The number of alignment threads per job.
alnthreads: 4
Path for a temp dir to be used by pipeline.
tempdir: temp
This defines the smallest bin size (highest resolution) used in the cooler file.
cooler_window: 100
Since window defines the read length, it should be made to be smaller than cooler_window. I like to use window: 32 and cooler_window: 100.
This is the maximum number of alignments to output for each read. From the bwa help page: Maximum number of alignments to output in the XA tag for reads paired properly. If a read has more than INT hits, the XA tag will not be written.
num_dups: 100
Linting and formatting
Linting results
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Formatting results
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