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# configuration of display in snakemake workflow catalog: https://snakemake.github.io/snakemake-workflow-catalog
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usage:
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mandatory-flags:# optional definition of additional flags
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desc: # describe your flags here in a few sentences (they will be inserted below the example commands)
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mandatory-flags:
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desc: # describe your flags here in a few sentences
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flags: # put your flags here
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software-stack-deployment:# definition of software deployment method (at least one of conda, singularity, or singularity+conda)
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conda: true # whether pipeline works with --use-conda
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singularity: false# whether pipeline works with --use-singularity
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singularity+conda: false# whether pipeline works with --use-singularity --use-conda
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report: true #add this to confirm that the workflow allows to use 'snakemake --report report.zip' to generate a report containing all results and explanations
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software-stack-deployment:
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conda: true # whether pipeline works with '--sdm conda'
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apptainer: true# whether pipeline works with '--sdm apptainer/singularity'
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apptainer+conda: true# whether pipeline works with '--sdm conda apptainer/singularity'
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report: true #whether creation of reports using 'snakemake --report report.zip' is supported
@@ -21,7 +20,7 @@ A Snakemake workflow for `<description>`
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## Usage
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The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/?usage=<owner>%2F<repo>).
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The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/docs/workflows/<owner>/<repo>).
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If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository or its DOI.
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@@ -30,11 +29,10 @@ If you use this workflow in a paper, don't forget to give credits to the authors
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This workflow is a best-practice workflow for `<detailed description>`.
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The workflow is built using [snakemake](https://snakemake.readthedocs.io/en/stable/) and consists of the following steps:
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1.Parse sample sheet containing sample meta data (`python`)
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1.Download genome reference from NCBI
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2. Simulate short read sequencing data on the fly (`dwgsim`)
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3. Check quality of input read data (`FastQC`)
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4. Trim adapters from input data (`cutadapt`)
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5. Collect statistics from tool output (`MultiQC`)
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4. Collect statistics from tool output (`MultiQC`)
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## Running the workflow
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To run the workflow from command line, change the working directory.
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```
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Adjust options in the default config file `config/config.yml`.
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Before running the entire workflow, you can perform a dry run using:
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Before running the complete workflow, you can perform a dry run using:
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```bash
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snakemake --dry-run
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```
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To run the complete workflow with test files using **conda**, execute the following command. The definition of the number of compute cores is mandatory.
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To run the workflow with test files using **conda**:
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```bash
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snakemake --cores 3 --sdm conda --directory .test
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snakemake --cores 2 --sdm conda --directory .test
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```
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To run the workflow with **singularity** / **apptainer**, add a link to a container registry in the `Snakefile`, for example:
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To run the workflow with **apptainer** / **singularity**, add a link to a container registry in the `Snakefile`, for example:
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`container: "oras://ghcr.io/<user>/<repository>:<version>"` for Github's container registry. Run the workflow with:
| ncbi_ftp | str | link to a genome on NCBI's FTP server | link to _S. cerevisiae_ genome |
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|**simulate_reads**||||
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| read_length | num | length of target reads in bp | 100 |
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| read_number | num | number of total reads to be simulated | 100000 |
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| random_freq | num | frequency of random read sequences | 0.01 |
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## Authors
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## References
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> Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. *Sustainable data analysis with Snakemake*. F1000Research, 10:33, 10, 33, **2021**. https://doi.org/10.12688/f1000research.29032.2.
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> Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. _Sustainable data analysis with Snakemake_. F1000Research, 10:33, 10, 33, **2021**. https://doi.org/10.12688/f1000research.29032.2.
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## TODO
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* Replace `<owner>` and `<repo>` everywhere in the template (also under .github/workflows) with the correct `<repo>` name and owning user or organization.
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* Replace `<name>` with the workflow name (can be the same as `<repo>`).
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* Replace `<description>` with a description of what the workflow does.
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* Update the workflow description, parameters, running options, authors and references in the `README.md`
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* Update the `README.md` badges. Add or remove badges for `conda`/`singularity`/`apptainer` usage depending on the workflow's capability
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* The workflow will occur in the snakemake-workflow-catalog once it has been made public. Then the link under "Usage" will point to the usage instructions if `<owner>` and `<repo>` were correctly set.
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- Replace `<owner>` and `<repo>` everywhere in the template with the correct user name/organization, and the repository name. The workflow will be automaticallky added to the [snakemake workflow catalog](https://snakemake.github.io/snakemake-workflow-catalog/index.html) once it is publicly available on Github.
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- Replace `<name>` with the workflow name (can be the same as `<repo>`).
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- Replace `<description>` with a description of what the workflow does.
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- Update the [workflow overview](#running-the-workflow), and [running instructions](#running-the-workflow) including parameters, deployment, authors and references
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- Update the `README.md` badges. Add or remove badges for `conda`/`singularity`/`apptainer` usage depending on the workflow's [deployment options](#execution)
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- Do not forget to also adjust the configuration-specific `config/README.md` file
To run the workflow from command line, change the working directory.
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```
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Adjust options in the default config file `config/config.yml`.
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Before running the entire workflow, you can perform a dry run using:
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Before running the complete workflow, you can perform a dry run using:
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```bash
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snakemake --dry-run
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```
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To run the complete workflow with test files using **conda**, execute the following command. The definition of the number of compute cores is mandatory.
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To run the workflow with test files using **conda**:
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```bash
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snakemake --cores 3 --sdm conda --directory .test
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snakemake --cores 2 --sdm conda --directory .test
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```
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To run the workflow with **singularity** / **apptainer**, add a link to a container registry in the `Snakefile`, for example:
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To run the workflow with **apptainer** / **singularity**, add a link to a container registry in the `Snakefile`, for example:
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`container: "oras://ghcr.io/<user>/<repository>:<version>"` for Github's container registry. Run the workflow with:
| config | str | path to multiQC config |`config/multiqc_config.yml`|
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## TODO
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* Replace `<owner>` and `<repo>` everywhere in the template (also under .github/workflows) with the correct `<repo>` name and owning user or organization.
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* Replace `<name>` with the workflow name (can be the same as `<repo>`).
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* Replace `<description>` with a description of what the workflow does.
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* Update the workflow parameters and running options
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