usnistgov/defrabb
Genome In A Bottle Development Framework for Assembly Based Benchmarks
Overview
Topics:
Latest release: None, Last update: 2025-03-05
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/usnistgov/defrabb . --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
.
defrabb uses two configuration files
See schema/analyses-schema.yml
and schema/resources-schema.yml
for detailed descriptions and field formats requirements.
used to define:
- parameters, threads, and memory for compute intensive steps
- urls for remote files: diploid assemblies, genome reference files, stratifications, and callsets used to evaluate draft benchmark
- exclusion sets and how they are applied
Provides run specific configurations
- input diploid assembly
- version of reference genome
- assembly-based variant caller and parameters
- vcf and bed processing including what exclusions to use
- benchmarking method and comparison callset used for initial evaluation
Linting and formatting
Linting results
/tmp/tmp9tudc8xq/rules/bench_vcf_processing.smk:74: SyntaxWarning: invalid escape sequence '\.'
log:
/tmp/tmp9tudc8xq/rules/bench_vcf_processing.smk:87: SyntaxWarning: invalid escape sequence '\.'
rule normalize_vars:
Lints for rule write_report_params (line 95, /tmp/tmp9tudc8xq/rules/report.smk):
* Migrate long run directives into scripts or notebooks:
Long run directives hamper workflow readability. Use the script or
notebook directive instead. Note that the script or notebook directive
does not involve boilerplate. Similar to run, you will have direct access
to params, input, output, and wildcards.Only use the run directive for a
handful of lines.
Also see:
https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
Formatting results
None