alzel/refseq_pipeline

None

Overview

Latest release: None, Last update: 2024-08-23

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/alzel/refseq_pipeline . --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.

Describe how to configure the workflow (using config.yaml and maybe additional files). All of them need to be present with example entries inside of the config folder.

Linting and formatting

Linting results

  1Lints for snakefile /tmp/tmpcysasrw2/workflow/Snakefile:
  2    * Absolute path "/" + genome + f" in line 21:
  3      Do not define absolute paths inside of the workflow, since this renders
  4      your workflow irreproducible on other machines. Use path relative to the
  5      working directory instead, or make the path configurable via a config
  6      file.
  7      Also see:
  8      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
  9    * Absolute path "/data" in line 109:
 10      Do not define absolute paths inside of the workflow, since this renders
 11      your workflow irreproducible on other machines. Use path relative to the
 12      working directory instead, or make the path configurable via a config
 13      file.
 14      Also see:
 15      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
 16    * Absolute path "/data" in line 129:
 17      Do not define absolute paths inside of the workflow, since this renders
 18      your workflow irreproducible on other machines. Use path relative to the
 19      working directory instead, or make the path configurable via a config
 20      file.
 21      Also see:
 22      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
 23    * Mixed rules and functions in same snakefile.:
 24      Small one-liner functions used only once should be defined as lambda
 25      expressions. Other functions should be collected in a common module, e.g.
 26      'rules/common.smk'. This makes the workflow steps more readable.
 27      Also see:
 28      https://snakemake.readthedocs.io/en/latest/snakefiles/modularization.html#includes
 29    * Path composition with '+' in line 6:
 30      This becomes quickly unreadable. Usually, it is better to endure some
 31      redundancy against having a more readable workflow. Hence, just repeat
 32      common prefixes. If path composition is unavoidable, use pathlib or
 33      (python >= 3.6) string formatting with f"...".
 34      Also see:
 35
 36    * Deprecated singularity directive used for container definition in line 110.:
 37      Use the container directive instead (it is agnostic of the underlying
 38      container runtime).
 39      Also see:
 40      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 41
 42Lints for rule download_genomic (line 78, /tmp/tmpcysasrw2/workflow/Snakefile):
 43    * No log directive defined:
 44      Without a log directive, all output will be printed to the terminal. In
 45      distributed environments, this means that errors are harder to discover.
 46      In local environments, output of concurrent jobs will be mixed and become
 47      unreadable.
 48      Also see:
 49      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 50    * Specify a conda environment or container for each rule.:
 51      This way, the used software for each specific step is documented, and the
 52      workflow can be executed on any machine without prerequisites.
 53      Also see:
 54      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 55      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 56
 57Lints for rule download_gff (line 105, /tmp/tmpcysasrw2/workflow/Snakefile):
 58    * No log directive defined:
 59      Without a log directive, all output will be printed to the terminal. In
 60      distributed environments, this means that errors are harder to discover.
 61      In local environments, output of concurrent jobs will be mixed and become
 62      unreadable.
 63      Also see:
 64      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 65    * Specify a conda environment or container for each rule.:
 66      This way, the used software for each specific step is documented, and the
 67      workflow can be executed on any machine without prerequisites.
 68      Also see:
 69      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 70      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 71
 72Lints for rule gunzip (line 131, /tmp/tmpcysasrw2/workflow/Snakefile):
 73    * No log directive defined:
 74      Without a log directive, all output will be printed to the terminal. In
 75      distributed environments, this means that errors are harder to discover.
 76      In local environments, output of concurrent jobs will be mixed and become
 77      unreadable.
 78      Also see:
 79      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 80    * Specify a conda environment or container for each rule.:
 81      This way, the used software for each specific step is documented, and the
 82      workflow can be executed on any machine without prerequisites.
 83      Also see:
 84      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 85      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 86
 87Lints for rule extract_parts (line 199, /tmp/tmpcysasrw2/workflow/Snakefile):
 88    * Migrate long run directives into scripts or notebooks:
 89      Long run directives hamper workflow readability. Use the script or
 90      notebook directive instead. Note that the script or notebook directive
 91      does not involve boilerplate. Similar to run, you will have direct access
 92      to params, input, output, and wildcards.Only use the run directive for a
 93      handful of lines.
 94      Also see:
 95      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
 96      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
 97
 98Lints for rule filter_fasta (line 247, /tmp/tmpcysasrw2/workflow/Snakefile):
 99    * No log directive defined:
100      Without a log directive, all output will be printed to the terminal. In
101      distributed environments, this means that errors are harder to discover.
102      In local environments, output of concurrent jobs will be mixed and become
103      unreadable.
104      Also see:
105      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
106    * Specify a conda environment or container for each rule.:
107      This way, the used software for each specific step is documented, and the
108      workflow can be executed on any machine without prerequisites.
109      Also see:
110      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
111      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Formatting results

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2[DEBUG] In file "/tmp/tmpcysasrw2/workflow/Snakefile":  Formatted content is different from original
3[INFO] 1 file(s) would be changed 😬
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5snakefmt version: 0.10.2