What is BioNT?

BioNT is an international consortium of academic institutions and SMEs, co-funded by the EU Digital Europe programme (grant agreement 101100604), running from January 2023 to June 2026. Its mission is to build a high-quality training programme in digital skills for the biotechnology and biomedical sectors, particularly for learners with limited prior experience in biological data handling and computational tools.

The University of Freiburg’s Freiburg Galaxy Team is an active BioNT partner, contributing expertise in bioinformatics training and the Galaxy Training Network.

Human-curated translated Tutorials Now Available

One of BioNT’s core goals is to make training materials accessible to non-English-speaking communities. To that end, the BioNT team has produced careful, human-quality translations of key GTN tutorials into German (DE), Spanish (ES), and Italian (IT).

The following tutorials are now available in four languages:

In addition to the tutorials, the BioNT team has translated a comprehensive set of Galaxy FAQs into all three languages, covering common actions such as importing data, creating collections, renaming datasets, using tutorial mode, and more. These translated FAQs are now embedded throughout the corresponding tutorials, giving learners a fully localised experience from start to finish.

Why This Matters

Machine translation can provide a rough guide, but learners, especially those new to bioinformatics, benefit enormously from materials that read naturally in their own language. The BioNT team’s human-translated content ensures that terminology is accurate, explanations are clear, and the learning experience is as smooth as possible for German, Spanish, and Italian-speaking communities across Europe and beyond.

This work is a wonderful example of the GTN community’s commitment to open, FAIR, and inclusive training for everyone.

Thank You!

A huge thank you to all the BioNT contributors who made this possible — translators, reviewers, and everyone involved in getting these materials across the finish line:

Helena Vela , Lisanna Paladin , Alan O’Cais , Daniela Schneider , Saskia Hiltemann , Silvia Di Giorgio , Teresa Müller , Till Sauerwein , Renato Alves .

We also thank the BioNT consortium partners — including EMBL, ZB MED, EPFL, University of Barcelona, University of Freiburg, University of Oslo, biobyte solutions, RIcapacity, and HPCNow! — for making this international effort possible.

The BioNT project is co-funded by the EU Digital Europe programme, grant agreement number 101100604.

As in previous years, GTA is an asynchronous, self-paced global learning event 👩🏽‍💻🌐 open to participants from all around the world. If you would like to learn more about Galaxy, Python programming, Transcriptomics, Microbiome analysis, Genome Assembly, and many other topics, make sure to save the date!

🗓️ Key Infromation

• Dates: May 18-22, 2026
• Location: Global online
• Registration opens: March the 6th

📘 Initial Program:

(*)Tracks: Assembly, Transcriptomics, Microbiome, Machine Learning, From Zero to Hero with Python …

We are currently reaching out to the community to onboard both long-standing and new tracks.

Next steps

Over the coming months, we will finalise the core program and launch a Call for Contributions. If you are a trainer, have experience with Galaxy, or simply want to support the event, we would be thrilled to have you join us next February to help refine and improve the content.

Stay connected with us on LinkedIn, Bluesky, and Mastodon for updates and announcements.

The conference aimed to bring together teaching staff, educational researchers, training coordinators, and HR developers to exchange methods, best practices, and experiences - strengthening collaboration within the Helmholtz Association and its partners.

We were delighted to participate as an external member and to present a short pitch talk about the GTN during the BarCamp session. It was inspiring to listen to engaging talks, take part in interactive workshops, and discuss training-related topics throughout the BarCamp sessions and networking breaks.

We would like to sincerely thank the organizers for hosting this excellent event and for giving us the opportunity to take part.

As in previous years, GTA is planned to take place in Spring 2026 as an asynchronous, self-paced learning event ​👩🏽‍💻​ open to participants all around the world 🌐​. We hope to make this edition even more engaging and to build on the valuable feedback we received from last year’s event.

To make this possible, we need your help! Would you like to join the planning committee for GTA 2026? Do you want to help shape and organize the event together with us?

Based on your availability, we will set up regular organizing meetings at times that best fit the time zones of all organizers. In addition, we will use a Matrix chat for quick and asynchronous communication.

If you would like to join us later as a trainer or helper, stay tuned - we will soon announce an open call for trainers and helpers.

👉 If you are interested in helping with the planning, please fill the following Google form. In the coming weeks, we will arrange one or more meeting times, depending on your time zone, to bring you on board.

Stay connected with us on LinkedIn, Bluesky, and Mastodon for updates and announcements.

Workflows maintained by the Intergalactic Workflow Commission are also automatically registered with WorkflowHub thanks to an earlier integration. Those workflows can be viewed here: IWC on WorkflowHub

Why is it important?

The GTN encompasses a wide array of tutorials that guide researchers in the application of Galaxy for specific analyses, while also onboarding them to both the Galaxy platform itself and its vibrant community of contributors. The tutorials are often underpinned by a computational workflow that has been created to assist with the described training outcomes, and there are currently more than 330 such computational workflows. Each workflow is an investment of time, effort, and most importantly expertise. When combined with the scale, global reach, and impact of Galaxy, there is a demonstrable need for these workflows to be as findable as possible. To address this, every new tutorial that features a workflow will now be registered automatically with the WorkflowHub registry.

About WorkflowHub

WorkflowHub is a public registry dedicated to sharing computational workflows across all scientific domains. It is deliberately agnostic to workflow language, management system or discipline, meaning a Galaxy workflow, a Snakemake pipeline or a custom Python script are equally welcome.

Each workflow entry contains rich metadata, version history, license information and ontology annotations. WorkflowHub supports citation infrastructure (such as DOIs and ORCID IDs) so that authors receive scholarly credit.

Galaxy’s existing integration with WorkflowHub allows researchers to discover, import, and run workflows from WorkflowHub directly within Galaxy. Alternatively, from the WorkflowHub website, users can utilise the “Run on Galaxy” button to launch the workflow in Galaxy.

Where can I learn more?

The existing set of registered GTN workflows can be viewed here.

Read the new WorkflowHub publication to learn more about the registry: Gustafsson et al. 2025

The integration with WorkflowHub will be improved over time, enriching the metadata made discoverable through registration, and further improving the interoperability of WorkflowHub with the Galaxy ecosystem.

Band quantification can be carried out using interactive tools such as QuPath that allow researchers to define regions of interest (ROIs) directly on gel images. These ROIs can be exported as GeoJSON files, which capture the spatial coordinates of the selected bands in a standardized, machine-readable format compatible with other tools. To address this, we’ve developed a new hands-on Galaxy tutorial that shows how to combine QuPath and Galaxy imaging tools for a transparent, reproducible workflow for gel band analysis.

Why This Matters

Open-source workflows make gel quantification more accessible and reliable. They run in a web browser without extra software or powerful hardware. Each step is recorded, ensuring reproducibility, and results can be shared or adapted with ease. With this new workflow:

• QuPath is used to interactively select gel bands as regions of interest (ROIs) and export them as GeoJSON files.
• Galaxy processes these ROI files to create label maps, extract intensity values, and generate tabular results for downstream analysis.

This approach reduces bias, makes results reproducible, and integrates seamlessly with other Galaxy-based analyses.

How the Workflow Works

The tutorial walks learners through each step:

1. Image preparation – Uploading gel images into Galaxy, converting them to TIFF format, and splitting channels for analysis.
2. Annotation with QuPath – Drawing rectangles around gel bands, assigning labels, and exporting the results as GeoJSON files.
3. ROI processing in Galaxy – Converting the coordinates into label maps and extracting pixel intensities.
4. Quantification and output – Generating tabular outputs with mean intensity values for each band.

The workflow is fully documented with screenshots, tool links, and downloadable datasets, making it accessible for both beginners and advanced users.

Smooth, Reproducible Analysis

One of the key advantages of this tutorial is how it bridges the gap between interactive annotation (via QuPath) and automated quantification (via Galaxy). Learners don’t need specialized software installations—everything runs in the browser—and the results can be reproduced or extended in new analyses.

By following the steps, users gain experience with:

• Using QuPath for image annotation
• Generating and handling ROI files
• Leveraging Galaxy imaging tools for reproducible quantification

Open Tools, Open Training

As with all Galaxy Training Network (GTN) tutorials, this workflow is fully open-source and freely available. It supports the GTN’s mission to provide transparent, accessible, and reusable training resources in bioinformatics and bioimaging.

We invite educators, researchers, and learners to try the tutorial, adapt it to their datasets, and integrate it into teaching or research pipelines.

👉 Explore the tutorial here: Quantification of electrophoresis gel bands using QuPath and Galaxy imaging tools

In May 2025, from the 12th to the 16th, the Galaxy Training Network (GTN) hosted the second edition of the Galaxy Training Academy (GTA) - a global, self-paced online training event. Since its inception in 2021 with the GTN Smörgåsbord, this was already the fifth large-scale, open-access learning event organized by the GTN.

The GTA offered a range of training paths, known as ‘tracks’, covering diverse scientific topics. Learners worldwide could follow these tracks using a combination of pre-recorded video and detailed written tutorials, all free and openly accessible.

This global effort was made possible by an international team of dedicated trainers and contributors, who prepared materials, set up the infrastructure, and offered live support throughout the event.

The event

The Galaxy Training Academy began on Monday with a live welcome and introduction session. The first day offered a variety of entry-level training sessions - perfect for those new to Galaxy, with tutorals like ‘Galaxy 101’. However also more advanced tutorials where offered including Data manipulation, or High-throughput sequencing (HTS) fundamentals like read mapping and quality control.

Over the next three days, participants could explore one or more specialized tracks. This year’s lineup included both returning favorites and new additions:

• Returning Tracks: Proteomics, Assembly, Transcriptomics, Single Cell, Microbiome, and Machine Learning

• New Tracks: Variant Analysis, Climate Science, and ‘From Zero to Hero with Python’

On Friday, participants had the opportunity to explore additional topics such as FAIR training or Plant Galaxy, or simply catch up on materials from earlier in the week.

The full program and detailed event setup instruction were provided to participants in advance to ensure a smooth learning experience.

Participants

This year’s GTA saw 3,574 registered participants from across the globe, with particularly strong representation from India and the United States. Most participants were either complete beginners (42.3%) or had limited experience (44.2%) with Galaxy.

A total of 763 participants requested certificates, and 561 certificates were issued. The most popular certificate requests was for the Introduction to Galaxy, followed by Microbiome, Assembly, Python Programming, and Transcriptomics Tracks.

While most learners requested a certificate for a single track, many pursued multiple tracks and earned multiple certificates.

The GTA 2025 welcome video was viewed 1,237 times in May, and the most popular video tutorials were the ‘Reference-based RNA-Seq Data Analysis’ Hands-on session and the ‘Introduction to Genome Assembly’ lecture.

Community and support

The success of GTA 2025 was made possible by the vibrant GTN community, who contributed to every aspect of the event - from track development and tutorial testing to video recording, tool integration, and user support.

Each track had its own dedicated Slack channel, where trainers and experts provided real-time assistance throughout the week.

An open call for trainers was launched earlier in 2025, attracting both long-time contributors and new community members. The final roster included 21 Track Leads, who selected and organized content. Additionally and the total of 24 Tutorial Testers took care to the correct content of the tutorials and 52 Trainers supporting before and during the event.

Their efforts were instrumental in creating a supportive, accessible, and high-quality training experience.

Feedback

Feedback from participants was overwhelmingly positive. Already during the event, many expressed appreciation for the high-quality content, organization, and support.

The majority of participants were satisfied with the event and indicated that they would attend future Galaxy events.

The overall satisfaction level with the accessability, support during the event, the reachability of experts, and the pace of the training, the ease of navigation was high.

Additionally, most participants used the videos to complete the training and 96.2% of the participants are planning to use galaxy again.

Next Galaxy Training Academy event

We’re excited to announce that the next Galaxy Training Academy will take place in Spring 2026! In the meantime, we’ll use this year to further improve and update our materials and infrastructure.

Stay connected with us on LinkedIn, Bluesky, and Mastodon for updates and announcements.

Closing Thoughts

The Galaxy Training Academy 2025 showcased the strength of community-driven, open science education. With the support of the GTN, Galaxy continues to empower learners worldwide through accessible, scalable, and high-quality training.

A huge thank you to everyone who contributed - and to all participants for making GTA 2025 such a success. We look forward to welcoming even more learners in future editions!

What really made this possible was the support we had from Training Infrastructure as a Service (TIAAS), provided by the Galaxy Project. It gave us the technical foundations we needed to run compute-intensive tutorials for a large group—without the usual hiccups, delays, or fiddly setup. This was the first ever bioinformatics event at the Open University, and the support to pilot it - without any funding - was crucial.

Supporting Open University Students

One of the biggest challenges in running this kind of course is making it work for OU students, who often have very different needs from typical full-time university learners. Many of our students are working, caring for family, or studying later in life—often all three at once. They’re focused, practical, and looking to develop skills they can take into real jobs in areas like healthcare, biotech, and data science.

To support that kind of learner, courses have to be flexible, accessible, and immediately useful. That’s why we designed the bootcamp to be fully online, free to attend, and built around real-world bioinformatics workflows. But we also needed infrastructure that wouldn’t get in the way—something students could just log in and use, without needing to install software or battle with setup instructions. That’s where TIAAS came in.

Why TIAAS Made a Big Difference

Bioinformatics isn’t exactly light on compute. Even relatively simple analyses—like aligning sequences or working with single-cell data—can be demanding. And when 80 students are all running these jobs at the same time, things can grind to a halt pretty quickly on shared public servers.

TIAAS solves this by providing dedicated compute resources for training events. That meant our students could run their jobs quickly, without queuing, and stay focused on the learning rather than wondering whether the system had frozen. For OU learners—who might be studying late at night on an old laptop or shared family device—that reliability makes a huge difference. It also helps with troubleshooting: if a job fails, students can try again immediately, rather than giving up out of frustration.

Smooth for Students, Straightforward for Us

From our side, TIAAS gave us real-time insight into how the course was going—who was running jobs, where there might be issues, and whether anyone needed help. That allowed us to stay focused on teaching and supporting learners, not firefighting tech problems. Even when our numbers grew beyond initial expectations, TIAAS scaled without a hitch. We didn’t have to turn anyone away, and we didn’t have to compromise on the quality of the experience. That kind of flexibility is essential for widening participation and making sure we can say “yes” to as many students as possible.

Open Tools, Open Education

This project really brought home how well open-source tools can support open education. We used training materials from the Galaxy Training Network, delivered through the Galaxy platform, and ran everything on open infrastructure via TIAAS. That combination meant we could offer something that was free, hands-on, and genuinely useful—and it aligned perfectly with the OU’s mission to make high-quality learning available to all.

We’re incredibly proud of what the students achieved, and grateful for the infrastructure that made it possible. If you’re planning to run hands-on bioinformatics training—especially for learners with complex lives or limited resources—we can’t recommend TIAAS enough.

The Training Life-Cycle

Training in the data sciences often follows a structured path from identifying needs to evaluating impact, known as the Training Life-Cycle. This cycle helps training providers to plan, design, develop, deliver, and evaluate training activities effectively in order to create high-quality, sustainable training.

ELIXIR Training Life-Cycle and SPLASH

To guide trainers through the Training Life-Cycle, the ELIXIR Training Platform developed and maintains the SPLASH framework (Skills: Developing essential skills for trainers and learners; Professional Development: Supporting ongoing professional growth; Learning Assessment: Providing tools and methods for assessing learning outcomes; Support: Offering resources and assistance throughout the training life cycle; Help: Ensuring trainers have access to necessary help and guidance).

This framework provides support for the different steps of the Training Life-Cycle by offering recommendations and linking to services/tools supported by ELIXIR.

Comparison of ELIXIR Services/Tools and GTN Implementations

Let’s examine the different steps of the Training Life-Cycle and their associated SPLASH Recommendations, comparing ELIXIR Services/Tools and GTN Implementations.

Plan

In this stage, training needs are identified and assessed to determine whether training is needed and what needs to be trained. This stage helps ensure that the training results in the desired and intended outcomes.

Design

After identifying the training needs and determining the objectives, an effective training method can be designed. This stage includes determining the best method of delivery, which may include instructor-led, online, self-study, or a blended method.

Develop

In this stage, materials including tutorials, activities, presentations, and assessments are developed. The training material assets are created and stored on a training material repository or an e-learning platform.

Deliver

This stage focuses on the delivery of the training, including logistical aspects like registration, advertisement, and the actual execution of the course, either synchronously or asynchronously if it is an e-learning course.

Evaluate

After participants have had a chance to apply the learning, the effectiveness of the training should be assessed. These findings are integrated into an overall evaluation and review process to allow improvements to the training program.

Conclusion

The Galaxy Training Network is a vital component of the Training Life-Cycle. By adhering to the SPLASH recommendations and utilizing the robust capabilities of the Galaxy platform, the GTN guarantees that researchers have access to high-quality, practical, and sustainable training resources. As the field of data science continues to advance, the GTN’s dedication to excellence and collaboration will be essential in empowering the next generation of scientists.

To keep the tables accurate and up-to-date, please contribute by adding comments to the following spreadsheets!