Galaxy is a great solution to train bioinformatics concepts:

• numerous bioinformatics tools are available (over 8,000 in the ToolShed)
• it can be used by people without any computer science skills
• it trains to use technology, outlining available resources and efforts that have made them accessible to researchers
• it is scalable

In 2016, the Galaxy Training Network decide to set up a new infrastructure for easily delivering Galaxy related training material. The idea was to develop something open, online, based on a community effort, and on top of the Galaxy platform.

We took inspiration from Software Carpentry and collected everything on a GitHub repository: https://github.com/galaxyproject/training-material. We decided on a structure focusing on tutorials with hands-on activities; fitting both for online self-training but also for workshops. Each tutorial follows the same structure and comes with a virtualised instance allowing you to run the training anywhere you have resources available.

Here you will learn how to create a new tutorial by developing a small tutorial explaining how to retrieve climate data from Copernicus (using Copernicus Climate Data Store tool).

Agenda

In this tutorial, we will cover:

1. Define the topic
2. Store a tutorial
3. Create the workflow
4. Get a toy dataset
5. Create the skeleton of the tutorial
6. Write the tutorial
7. Keep track of the changes
8. Add slides (optional)
9. Conclusion

Comment

This tutorial explains the different steps to create a tutorial for the Galaxy Training Material. It may require some knowledge that you may not have or do not have the time to learn. If this is the case, you can create a skeleton of a tutorial with whatever existing materials you have, using your prefered text editor, and then share it with us by opening issue on GitHub, writing us on Gitter, or sending us an email.

Define the topic

The first step we need to do is to identify in which topic to place our new tutorial. This can be tricky. When we structured the GTN, we decided that each training material should be related to a topic.

We decided to use the categories from the ToolShed as our initial list of topics. Since every tool uploaded to the ToolShed must be in at least one category, you can look at the main tools in your tutorial and see which categories they are placed in within the ToolShed. This can provide a guide for where you might put your new tutorial. For example, if your tutorial will rely on the Copernicus tool:

Hands-on: Defining the topic for the tutorial

1. Search for Copernicus on the ToolShed
2. Check in which category it has been placed

Solution

There are a couple steps to reaching the answer:

1. Search for Copernicus
2. Press the Enter key to search
3. Click on the result named c3s
4. At the bottom of this page there is a box labelled “Categories”

It is placed in one category: “Climate Analysis”

1. Look if the categories fit to existing topics in the Galaxy Training Material

Comment: No fitting topic for the tools in your tutorial?

If the categories in ToolShed do not fit to any existing topics, we recommend to use your better judgment to identify in which topic your tutorial should go. You can also ask us on Gitter or raise an issue on GitHub explaining the aim of the tutorial. We will be happy to help you there.

Comment: Creating a new topic

Want to create a new topic? Check out our tutorial to create a new topic

Store a tutorial

All training materials (slides, tutorials, …) related to a topic are found in a dedicated directory (e.g. climate directory contains the material related to Climate analyses). Each topic has the following structure:

├── README.md
├── metadata.yaml
├── images
├── docker
│   ├── Dockerfile
├── slides
│   ├── index.html
├── tutorials
│   ├── tutorial1
│   │   ├── tutorial.md
│   │   ├── slides.html
│   │   ├── data-library.yaml
│   │   ├── workflows
│   │   │   ├── workflow.ga
│   │   ├── tours
│   │   │   ├── tour.yaml

Once the topic has been chosen, we can create the tutorial. An ideal tutorial in the Galaxy Training Network contains:

• a tutorial file tutorial.md written in Markdown with hands-on
• an optional slides file slides.html in Markdown with slides to support the tutorial
• a directory workflows with workflows extracted from the tutorial
• a YAML file data-library.yaml with the links to the input data needed for the tutorial

The most important file is the tutorial.md where the content of the tutorial is. The other files are not mandatory. They are there to support the tutorial and make it robust and usable across many environments. But they can help a lot in the development of new tutorial.

Create the workflow

Our tutorials try to follow the “learn by doing” approach; they combine both theoretical and practical sections done on Galaxy.

Most tutorials explain step-by-step a data analysis by running the tools. The steps taken in the tutorial can be represented as a workflow.

Before writing the tutorial, it is a good practice to get a workflow with the different steps that will be run during the tutorial. The workflow does not have to be the final one but at least the major steps. It helps to get a direction for the tutorial but also to generate a skeleton of the tutorial as we will see later.

Comment: Use tools that are available on the ToolShed

We recommmend you to use in your workflows, specially for training, tools that are available on the Galaxy ToolShed.

Hands-on: Prepare the workflow for the training

1. Go to your favorite Galaxy server

2. Create a workflow with the different steps (tools) of your tutorial either from scratch or from an existing history

   Tip: Creating a new workflow

   1. Click Workflow on the top bar
   2. Click the new workflow galaxy-wf-new button
   3. Give it a clear and memorable name
   4. Clicking Save will take you directly into the workflow editor for that workflow
   5. Need more help? Please see the How to make a workflow subsection here

   Tip: Extracting a workflow from your history

   1. Clean up your history: remove any failed (red) jobs from your history by clicking on the galaxy-delete button.

      This will make the creation of the workflow easier.

   2. Click on galaxy-gear (History options) at the top of your history panel and select Extract workflow.

      

      The central panel will show the content of the history in reverse order (oldest on top), and you will be able to choose which steps to include in the workflow.

   3. Replace the Workflow name to something more descriptive.

   4. Rename each workflow input in the boxes at the top of the second column.

   5. If there are any steps that shouldn’t be included in the workflow, you can uncheck them in the first column of boxes.

   6. Click on the Create Workflow button near the top.

      You will get a message that the workflow was created.

   For the Copernicus tutorial:

   1. Add an Input Dataset
   2. Add Copernicus Climate Data Store
   3. Link the Input Dataset to Copernicus Climate Data Store on API Request filename
   4. Rename Input Dataset to API Request file
   5. Save the workflow

3. Add the topic name as a Tag and the tutorial title as Annotation/Notes to the workflow using the workflow editor

   Tip: Annotate a workflow

   • Open the workflow editor for the workflow
   • Click on galaxy-pencil Edit Attributes on the top right
   • Write a description of the workflow in the Annotation box
   • Add a tag (which will help to search for the workflow) in the Tags section

   For the Copernicus tutorial:

   • Annotation: Retrieve climate data from Copernicus
   • Tag: climate

4. Make the workflow accessible (publishing is not necessary)

   Tip: Make a workflow public

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows
   • Click on the interesting workflow
   • Click on Share
   • Click on Make Workflow accessible. This makes the workflow publicly accessible but unlisted.
   • To also list the workflow in the Shared Data section (in the top menu bar) of Galaxy, click Make Workflow publicly available in Published Workflows

Get a toy dataset

To run the different steps, the tutorial needs some data. The selected data must be informative enough to illustrate the meaning of using a tool or a given technique, but not too big to require long waiting times for processing during a workshop. Upload and download of files into and out of Galaxy is usually quick, but the time taken for a tool to run can be long. Tool run times of no more than 10-15 mins are recommended. Typically, the selected data should be the informative subset of a full real-life dataset.

Below we describe two examples of how toy datasets were generated for tutorials:

• Example 1: creating a toy dataset from scratch
  • Take one 16S sequence (for example found in the test case of a Galaxy tool):
  • Generate a reference database
    • Blast it on the NR database on NCBI Blast
    • Extracting one similar sequence found with Blast
    • Search and extract 2 other sequences of the same species using the NCBI Nucleotide database
• Example 2: creating a toy dataset from an existing larger one
  • When the experiment takes a FASTQ as input and a few reads are sufficient:
    • Use seqtk_sample tool to extract randomly reads from your input fastq.
  • However, when it requires a lot of reads to be meaningful, you can use the following strategy (used for the ATAC-seq tutorial using this workflow):
    • Run the workflow until the mapping step on the full dataset (or big enough to have good results).
    • Select IDs of reads which map on the smallest chromosome (for example chr22 for human data).
    • In order to keep in the toy dataset enough diversity, you can also take randomly 1% of the reads IDs.
    • Concatenate the two lists and remove the duplicated IDs.
    • Use seqtk_subseq tool to sample your original FASTQ with the list of IDs.

We would then develop the tutorial and test it on this toy dataset. Once we were ready to share it, we would upload the datasets to Zenodo to store them on long-term and obtain a dedicated DOI in the Galaxy training network community.

Hands-on: Upload the dataset to Zenodo

1. Go to Zenodo

2. Log in using your GitHub credentials

   You may need to authorize Zenodo to access your GitHub account (only to read your information)

3. Click on Upload (top panel)
4. Start a new upload

5. Upload the files corresponding to your datasets

   Comment: No possible changes in the files after publication

   File addition, removal or modification are not allowed after you have published your upload. So be careful when you start your upload that all your needed files are ready.

   The metadata can be changed after publication.

6. Search for and Select Galaxy Training Network in Communities
7. Select Dataset in Upload type
8. Use the title of your tutorial and mention also Galaxy Training Material
9. Add all the persons who contributed to the tutorial as authors
10. Add a short description of the tutorial and a link to the training material website
11. Keep Open Access as Access right and Creative Commons Attribution 4.0 as License
12. Fill out any remaining information
13. Click on Save
14. Click on Publish
15. Copy the DOI link in the new page

Create the skeleton of the tutorial

Once we have the workflows and toy dataset (optionally already on Zenodo), we can generate the tutorial folder, including the tutorial.md.

Writing the tutorial while adding the different tools and their parameters and following good formatting can be quite cumbersome. To help in that process, we developed a training development kit available with Planemo. The tool has several commands. But the main one can be used to create the skeleton of a tutorial (folder, well located, with good structure). It can also take a workflow as input, add it to the workflows folder. But more interestingly, the tool can extract from the workflow and add in the tutorial.md file the different steps with which tools to run and which parameters to select. If a Zenodo URL is provided, it also creates the data-library.yaml file.

This tool can be used via the command-line but also via a webserver. The webserver can take a public workflow that is avialable on usegalaxy.org, usegalaxy.eu or usegalaxy.org.au.

Hands-on: Create the skeleton of a tutorial using the webserver

1. Make the workflow public

   Tip: Make a workflow public

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows
   • Click on the interesting workflow
   • Click on Share
   • Click on Make Workflow accessible. This makes the workflow publicly accessible but unlisted.
   • To also list the workflow in the Shared Data section (in the top menu bar) of Galaxy, click Make Workflow publicly available in Published Workflows

2. Copy the workflow id that can be found in the URL of the current page (after ?id=)
3. Open the PTDK webserver
4. Fill in the information
   • Tutorial name (the name will be the name of the folder of the tutorial)
   • Tutorial title
   • Galaxy instance with the public workflow
   • Id of the workflow
   • (Not mandatory) Zenodo URL with the input data

   For the Copernicus tutorial:

   • Tutorial name (the name will be the name of the folder of the tutorial): climate-data-retrieval-copernicus
   • Tutorial title: Retrieve climate data from Copernicus
   • Galaxy instance with the public workflow: usegalaxy.eu
   • Id of the workflow: ac5b66c42681e7a8
5. Click on Submit

6. Download the generated archive

   This archive contains the tutorial skeleton including:

   • tutorial content, tutorial.md file, filled with all steps from the workflow and their parameters
   • its workflow (workflow folder)
   • a data_library.yaml file if Zenodo link was provided

7. Add the new material to Galaxy Training Material by unzip the downloaded archive in the tutorials folder of the topic for the new tutorial

   Comment: Using the GitHub interface

   Prefer to use the GitHub interface?

   1. Unzip the downloaded archive
   2. Edit the content of the tutorial.md (as explained below)
   3. Go to the GitHub repository of the Training Material
   4. Fork the GitHub repository
   5. Click on topics
   6. Select the topic for the new tutorial
   7. Go to tutorials
   8. Click on Create new file
   9. Type name/tutorial.md, replacing “name” by the name of your tutorial (not the title)
   10. Copy the content of downloaded and edited tutorial.md file there
   11. Fill the Commit new file form
   12. Create a new branch using the name of the tutorial
   13. Click on Propose new file
   14. Open a Pull Request, as explained in our tutorial
   15. Add the workflow file (in workflow folder) by updating the Pull Request

If the workflow is not available on one of the previously listed Galaxy servers, we recommend you to run the tool via the command line:

Hands-on: Create the skeleton of a tutorial via the command line

1. Get the workflow id

   The id can be found on URL when running, editing or sharing the workflow (after ?id=)

2. Get your API key on the Galaxy instance

   Tip: Getting your API key

   1. In your browser, open your Galaxy homepage
   2. Log in, or register a new account, if it’s the first time you’re logging in
   3. Go to User -> Preferences in the top menu bar, then click on Manage API key
   4. If there is no current API key available, click on Create a new key to generate it
   5. Copy your API key to somewhere convenient, you will need it throughout this tutorial

3. (If not done yet) Get the Galaxy Training Material repository locally and move in it

   1. (If not done yet) Clone the training material GitHub repository: git clone https://github.com/galaxyproject/training-material.git
   2. Navigate to the training-material/ folder with cd

4. Get planemo

   • Option 1: Using Conda:

     1. Set up the conda environment

        It will install some needed tools (ruby, nodejs, etc) in a protected environment, without interfering with the existing tools or versions.

        1. Install conda (if not already installed): make install-conda
        2. (You may need to exit the terminal and re-open for conda to be recognised. Navigate back to the same place.)
        3. Create the galaxy_training_material conda environment: make create-env

     2. Activate the conda environment with conda activate galaxy_training_material

   • Option 2: Using pip by running pip install planemo

5. Generate the skeleton of your tutorial (by adapting the information between the quotes)

     $ planemo training_init \
            --topic_name "my-topic" \
            --tutorial_name "my-new-tutorial" \
            --tutorial_title "Title of the tutorial" \
            --galaxy_url "URL to Galaxy instance in which you created the workflow" \
            --galaxy_api_key "Your API key on the Galaxy instance" \
            --workflow_id "ID of the workflow on the Galaxy instance" \
            --zenodo_link "URL to the Zenodo record"
   

6. Check that a new directory (with your tutorial name) has been generated in the topic folder

Write the tutorial

Now that you have the structure in place, you can then fill the tutorial per se.

Hands-on: Write the tutorial

1. Open the tutorial.md file with your favorite text editor

2. Fill out the tutorial by following the dedicated tutorial

   1. Add metadata on the top of the tutorial
   2. Add a proper introduction
   3. Organize the tutorial in different sections
   4. Introduce the different concepts and the different steps of the analysis
   5. Check the hands-on boxes
   6. Comment on the outputs of the different steps
   7. Add some question/solution boxes for assessment
   8. Add a conclusion
3. (Optional) Build the website locally and check that the tutorial is there by following the Jekyll tutorial

Keep track of the changes

The material is stored in a GitHub repository, a code hosting platform for version control and collaboration. So to develop training material, we are following the GitHub flow, which is based on fork, branches, and pull requests.

This can be done online via the GitHub interface or locally on your computer via command-line.

Comment: Learning how to contribute

Want to learn how to contribute? Check our tutorials:

• Contributing with GitHub via its interface
• Contributing with GitHub via command-line

Add slides (optional)

Sometimes, you may want to have slides to support a tutorial and introduce it during a workshop. Sometimes, a set of slides is better than a tutorial to cover a specific topic.

Hands-on: Add slides

1. Create a slide deck in slides.html following the Slide tutorial

Conclusion

To develop a new tutorial:

1. Determine the topic
2. Create a workflow
3. Find a good toy dataset and upload it to Zenodo
4. Create the skeleton for the tutorial
5. Add the skeleton to the training material
6. Write the tutorial
7. Keep track of the changes
8. Add slides (optional)
9. Submit as a Pull Request to GitHub

For the next times, you can make it quicker.

Hands-on: Generation of a tutorial

1. Determine the topic
2. Create your workflow on a running Galaxy instance
3. Add the topic name as a Tag and the tutorial title as Annotation/Notes to the workflow using the workflow editor
4. Create a Zenodo record with the input data

5. Generate the skeleton of your tutorial

   • Option 1: from the PTDK webserver and get the skeleton in the training material

   • Option 2: from a workflow located on a Galaxy

     $ planemo training_init \
            --topic_name "my-topic" \
            --tutorial_name "my-new-tutorial" \
            --tutorial_title "Title of the tutorial" \
            --galaxy_url "URL to Galaxy instance in which you created the workflow" \
            --galaxy_api_key "Your API key on the Galaxy instance" \
            --workflow_id "ID of the workflow on the Galaxy instance" \
            --zenodo_link "URL to the Zenodo record"
     

6. Fill the remaining metadata in the tutorial.md
7. Fill the content of the tutorial.md

8. Check it by serving the website locally

   Comment: Serving the website locally

   Want to learn how to see the change on the website locally? Check out our dedicated tutorial

9. Submit it to GitHub

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Finding good training datasets is hard!

• Creating a new tutorial involves several steps: some are mandatory, some can be skipped even if they are recommended

Frequently Asked Questions

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.

Citing this Tutorial

1. Bérénice Batut, Helena Rasche, Saskia Hiltemann, Lucille Delisle, Creating a new tutorial (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/contributing/tutorials/create-new-tutorial/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{contributing-create-new-tutorial,
author = "Bérénice Batut and Helena Rasche and Saskia Hiltemann and Lucille Delisle",
	title = "Creating a new tutorial (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/contributing/tutorials/create-new-tutorial/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Developing GTN training material

This tutorial is part of a series to develop GTN training material, feel free to also look at:

1. Contributing with GitHub via command-line
2. Overview of the Galaxy Training Material
3. Creating Interactive Galaxy Tours
4. Contributing with GitHub via its interface
5. Generating PDF artefacts of the website
6. Tools, Data, and Workflows for tutorials
7. Teaching Python
8. Running the GTN website online using GitHub CodeSpaces
9. Design and plan session, course, materials
10. GTN Metadata
11. Running the GTN website online using GitPod
12. Adding auto-generated video to your slides
13. Including a new topic
14. Adding Quizzes to your Tutorial
15. Principles of learning and how they apply to training and teaching
16. Updating diffs in admin training
17. Running the GTN website locally using the command line
18. Creating a new tutorial
19. Single Cell Publication - Data Plotting
20. FAIR Galaxy Training Material
21. Single Cell Publication - Data Analysis
22. Creating content in Markdown
23. Creating Slides

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/contributing/tutorials/create-new-tutorial/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: text_processing
  owner: bgruening
  revisions: ddf54b12c295
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: c3s
  owner: climate
  revisions: 250554670f72
  tool_panel_section_label: Climate Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bam_to_sam
  owner: devteam
  revisions: 88eedb4abea0
  tool_panel_section_label: SAM/BAM
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bowtie2
  owner: devteam
  revisions: 017aba02828d
  tool_panel_section_label: Mapping
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: fastq_to_tabular
  owner: devteam
  revisions: 614a63f14adf
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: seqtk
  owner: iuc
  revisions: 3da72230c066
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: cutadapt
  owner: lparsons
  revisions: 49370cb85f0f
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

Feedback

5 stars	1

February 2019

• 5 stars: Liked: the option to automatically extract all steps of my workflow instead of typing them all is sooooo increadibly helpful!!!