nate-d-olson/defrabb

Development Environment For Assembly Based Benchmarks

Overview

Latest release: None, Last update: 2023-01-10

Linting: linting: failed, Formatting: formatting: failed

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/nate-d-olson/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

A description of every valid option in config.yaml.

e.g.

A prefix for outputs.

sample: a

Linting and formatting

Linting results

 1Lints for rule download_bed_gz (line 11, /tmp/tmpvwcwrx5o/rules/exclusions.smk):
 2    * Specify a conda environment or container for each rule.:
 3      This way, the used software for each specific step is documented, and the
 4      workflow can be executed on any machine without prerequisites.
 5      Also see:
 6      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 7      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 8
 9Lints for rule get_bed_size (line 53, /tmp/tmpvwcwrx5o/rules/report.smk):
10    * Specify a conda environment or container for each rule.:
11      This way, the used software for each specific step is documented, and the
12      workflow can be executed on any machine without prerequisites.
13      Also see:
14      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
15      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
16    * Shell command directly uses variable sum from outside of the rule:
17      It is recommended to pass all files as input and output, and non-file
18      parameters via the params directive. Otherwise, provenance tracking is
19      less accurate.
20      Also see:
21      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
22    * Shell command directly uses variable print from outside of the rule:
23      It is recommended to pass all files as input and output, and non-file
24      parameters via the params directive. Otherwise, provenance tracking is
25      less accurate.
26      Also see:
27      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
28
29Lints for rule move_asm_vcf_to_draft_bench (line 223, /tmp/tmpvwcwrx5o/rules/bench_vcf_processing.smk):
30    * Specify a conda environment or container for each rule.:
31      This way, the used software for each specific step is documented, and the
32      workflow can be executed on any machine without prerequisites.
33      Also see:
34      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
35      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
36
37Lints for rule move_processed_draft_bench_vcf (line 247, /tmp/tmpvwcwrx5o/rules/bench_vcf_processing.smk):
38    * Specify a conda environment or container for each rule.:
39      This way, the used software for each specific step is documented, and the
40      workflow can be executed on any machine without prerequisites.
41      Also see:
42      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
43      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
44
45Lints for rule get_assemblies (line 265, /tmp/tmpvwcwrx5o/Snakefile):
46    * Specify a conda environment or container for each rule.:
47      This way, the used software for each specific step is documented, and the
48      workflow can be executed on any machine without prerequisites.
49      Also see:
50      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
51      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
52
53Lints for rule get_ref (line 290, /tmp/tmpvwcwrx5o/Snakefile):
54    * Specify a conda environment or container for each rule.:
55      This way, the used software for each specific step is documented, and the
56      workflow can be executed on any machine without prerequisites.
57      Also see:
58      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
59      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
60
61Lints for rule get_strats (line 409, /tmp/tmpvwcwrx5o/Snakefile):
62    * Specify a conda environment or container for each rule.:
63      This way, the used software for each specific step is documented, and the
64      workflow can be executed on any machine without prerequisites.
65      Also see:
66      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
67      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
68
69Lints for rule get_comparison_vcf (line 437, /tmp/tmpvwcwrx5o/Snakefile):
70    * Specify a conda environment or container for each rule.:
71      This way, the used software for each specific step is documented, and the
72      workflow can be executed on any machine without prerequisites.
73      Also see:
74      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
75      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
76
77Lints for rule get_comparison_bed (line 463, /tmp/tmpvwcwrx5o/Snakefile):
78    * Specify a conda environment or container for each rule.:
79      This way, the used software for each specific step is documented, and the
80      workflow can be executed on any machine without prerequisites.
81      Also see:
82      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
83      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
84
85Lints for rule postprocess_bed (line 711, /tmp/tmpvwcwrx5o/Snakefile):
86    * Specify a conda environment or container for each rule.:
87      This way, the used software for each specific step is documented, and the
88      workflow can be executed on any machine without prerequisites.
89      Also see:
90      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
91      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Formatting results

1[DEBUG] 
2[DEBUG] 
3[DEBUG] 
4[DEBUG] In file "/tmp/tmpvwcwrx5o/rules/common.smk":  Formatted content is different from original
5[DEBUG] 
6[DEBUG] 
7[ERROR] In file "/tmp/tmpvwcwrx5o/Snakefile":  InvalidPython: Black error:

Cannot parse: 119:0: happy_analyses = analyses[analyses[“eval_cmd”] == “happy”]


[INFO] In file "/tmp/tmpvwcwrx5o/Snakefile":  1 file(s) raised parsing errors 🤕
[INFO] In file "/tmp/tmpvwcwrx5o/Snakefile":  1 file(s) would be changed 😬
[INFO] In file "/tmp/tmpvwcwrx5o/Snakefile":  3 file(s) would be left unchanged 🎉

snakefmt version: 0.8.0