usnistgov/defrabb
Genome In A Bottle Development Framework for Assembly Based Benchmarks
Overview
Latest release: None, Last update: 2026-05-07
Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=usnistgov/defrabb
Quality control: linting: failed formatting: passed
Wrappers: bio/assembly-stats bio/bcftools/index bio/bcftools/sort bio/bedtools/intersect bio/bedtools/sort bio/bwa/index bio/samtools/faidx bio/samtools/index
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run
conda create -c conda-forge -c bioconda -c nodefaults --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
For other installation methods, refer to the Snakemake and Snakedeploy documentation.
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.
Configuration options
defrabb uses two configuration files
See schema/analyses-schema.yml and schema/resources-schema.yml for detailed descriptions and field formats requirements.
resource.yaml
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
Analyses Tables
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
1/tmp/tmpl4twzg3q/rules/bench_vcf_normalize.smk:76: SyntaxWarning: invalid escape sequence '\.'
2 shell:
3/tmp/tmpl4twzg3q/rules/bench_vcf_normalize.smk:89: SyntaxWarning: invalid escape sequence '\.'
4 vcf="results/asm_varcalls/{vc_id}/annotations/{prefix}.vcf.gz",
5Lints for rule run_pav (line 91, /tmp/tmpl4twzg3q/rules/asm-varcall.smk):
6 * No log directive defined:
7 Without a log directive, all output will be printed to the terminal. In
8 distributed environments, this means that errors are harder to discover.
9 In local environments, output of concurrent jobs will be mixed and become
10 unreadable.
11 Also see:
12 https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
13
14Lints for rule standardize_vcasm_output (line 140, /tmp/tmpl4twzg3q/rules/asm-varcall.smk):
15 * No log directive defined:
16 Without a log directive, all output will be printed to the terminal. In
17 distributed environments, this means that errors are harder to discover.
18 In local environments, output of concurrent jobs will be mixed and become
19 unreadable.
20 Also see:
21 https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
22 * Specify a conda environment or container for each rule.:
23 This way, the used software for each specific step is documented, and the
24 workflow can be executed on any machine without prerequisites.
25 Also see:
26 https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
27 https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
28
29Lints for rule write_report_params (line 96, /tmp/tmpl4twzg3q/rules/report.smk):
30 * Migrate long run directives into scripts or notebooks:
31 Long run directives hamper workflow readability. Use the script or
32 notebook directive instead. Note that the script or notebook directive
33 does not involve boilerplate. Similar to run, you will have direct access
34 to params, input, output, and wildcards.Only use the run directive for a
35 handful of lines.
36 Also see:
37 https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
38 https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
39
40Lints for rule install_dfam_hmm (line 93, /tmp/tmpl4twzg3q/rules/bench_vcf_anno.smk):
41 * No log directive defined:
42 Without a log directive, all output will be printed to the terminal. In
43 distributed environments, this means that errors are harder to discover.
44 In local environments, output of concurrent jobs will be mixed and become
45 unreadable.
46 Also see:
47 https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
Formatting results
All tests passed!