Workflows are a powerful Galaxy feature that allows you to scale up your analysis by performing an end-to-end analysis with a single click of a button. In order to keep provenance of the workflow invocation (an invocation of a workflow means one run or execution of the workflow) it can be exported from Galaxy in the form of a Workflow Run Crate RO-Crate profile.
Agenda
In this tutorial, you will learn how to create a git repo, and begin working with it.
Additionally, the exported Workflow Run Crate allows for sharing workflow run provenance with those unfamiliar with Galaxy and its standard export format.
This tutorial will guide you through the steps of defining such a report for your workflow, .
This tutorial will show you how to generate Galaxy-based Workflow Run RO-Crate after running the workflow.
Hands-on: Choose Your Own Tutorial
This is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial
Are you running Galaxy locally ?
Enable RO-Crate on your local instance
Hands-on: Update your galaxy configuration
Go to where your Galaxy folder is in your computer
In your root Galaxy folder navigate to the config folder where a the galaxy.yml should be located. Please open it.
(In case you only find a galaxy.yml.sample file, copy this one and name it galaxy.yml)
make sure the option enable_celery_tasks is set to true:
galaxy:
enable_celery_tasks: true
That’s it ! Now you can launch your local instance as usual.
Import an example workflow
For this tutorial, we will use the workflow from the Galaxy 101 for everyone tutorial. If you have not done this tutorial yet, the only thing you need to know is that this is a workflow that takes as input a table of data about different species of iris plants, this table is subsequently sorted and filtered, and some plots are made. The specifics of the workflow are not important for this tutorial, only that it outputs a number of different kinds of outputs (images, tables, etc).
We will start by importing this workflow into your Galaxy account:
Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
Click on galaxy-uploadImport at the top-right of the screen
Paste the following URL into the box labelled “Archived Workflow URL”: https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/workflow-reports/workflows/galaxy-101-everyone.ga
Click the Import workflow button
Below is a short video demonstrating how to import a workflow from GitHub using this procedure:
Video: Importing a workflow from URL
Run the workflow
Galaxy will produce several export options for any workflow. The default export gives us a serialization of the invocation data model while the RO-Crate export gives an Workflow Run Crate which includes the default export as well.
Let’s run the workflow and export the RO-Crate.
Hands-on: Run the workflow
Import the file iris.csv via link
https://zenodo.org/record/1319069/files/iris.csv
Copy the link location
Click galaxy-uploadUpload Data at the top of the tool panel
Select galaxy-wf-editPaste/Fetch Data
Paste the link(s) into the text field
Press Start
Close the window
Run GTN Training: Galaxy 101 For Everyone workflow using the following parameters:
“Send results to a new history”: No
“1: Iris Dataset”“: the iris.csv file we just uploaded
Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
Click on the workflow-run (Run workflow) button next to your workflow
Configure the workflow as needed
Click the Run Workflow button at the top-right of the screen
You may have to refresh your history to see the queued jobs
View the workflow outputs once the workflow has completed
The workflow produces several text and tabular outputs, and two plot (image) outputs
Export the Workflow Run Crate
After the workflow has completed, we can export the RO-Crate. The crate does not appear in your history, but can be accessed from the galaxy-history-options-> Show Invocations menu on the top right of your history OR on the left pannel from the galaxy-panelview Workflow Invocations .
Hands-on: Export the Workflow Run Crate
In the top right of your history, go to galaxy-history-options-> Show Invocations
Our latest workflow run should be listed at the top.
Click on it to expand it:
Click on the Export tab in the expanded view of the workflow invocation.
Click on the Export tab in the expanded view of the workflow invocation.
You should see a page that contains three download options:
- Research Object Crate (RO-Crate)
- BioCompute Object
- File
Click on the Generategalaxy-download option of the RO-Crate box (1st box)
Great work! You have created a Workflow Run Crate. This makes it easy to track the provenance of the executed workflow.
You've Finished the Tutorial
Please also consider filling out the Feedback Form as well!
Key points
Galaxy Workflow Run Crates help you keep provenance of a workflow run / invocation.
Galaxy Workflow Run Crates give extra context to the standard workflow run export
Galaxy Workflow Run Crates can be exported from the top menu, User -> Workflow Invocations.
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.
Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
@misc{fair-ro-crate-in-galaxy,
author = "Paul De Geest",
title = "Exporting Workflow Run RO-Crates from Galaxy (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/fair/tutorials/ro-crate-in-galaxy/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}
}
Funding
These individuals or organisations provided funding support for the development of this resource
Paul De Geest
Marie Josse
