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WORKSHOP: Spatial omics

This record includes training materials associated with the Australian BioCommons workshop ‘Spatial omics. This workshop took place over two sessions on 28 - 29 October 2025.
Event description
Spatial omics provides unprecedented opportunities for the understanding of cells, tissues and systems...

Keywords: Bioinformatics, Analysis, Spatial omics

WORKSHOP: Spatial omics https://dresa.org.au/materials/workshop-spatial-omics This record includes training materials associated with the Australian BioCommons workshop ‘Spatial omics. This workshop took place over two sessions on 28 - 29 October 2025. Event description Spatial omics provides unprecedented opportunities for the understanding of cells, tissues and systems by combining omics and imaging technologies. This hands-on workshop will show you how to analyse data from in situ spatial (e.g. CosMx, Xenium) experiments with Seurat in R to visualise gene expression within tissue samples. Using real-life experimental data we step through the process of reading in data, quality control, filtering, dimensionality reduction, visualisation and differential expression analysis. We will discuss the ‘why’ behind each step and essential best practices for designing and running spatial omics experiments.   Lead trainers:  Dr Sarah Williams, QCIF. Fred Jaya, Sydney Informatics Hub, University of Sydney and Australian BioCommons Facilitators: Dr Nicholas Matigian, QCIF Dr Ciccy Wang, Garvan Institute of Medical Research Dr Mitchell O’Brien, Sydney Informatics Hub, University of Sydney and Australian BioCommons Dr Amarinder Singh Thind, Sydney Informatics Hub, University of Sydney and Australian BioCommons Infrastructure provision:  Dr Mitchell O’Brien, Sydney Informatics Hub, University of Sydney and Australian BioCommons Dr Giorgia Mori, Australian BioCommons Host: Dr Melissa Burke, Austrlaian BioCommons   Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Materials shared elsewhere: Training materials including detailed notes, exercises and code:  https://swbioinf.github.io/intro-spatial-transcriptomics-workshop/index.html Rmd files of R code used during the workshop via GitHub: https://github.com/swbioinf/intro-spatial-transcriptomics-workshop Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Spatial omics
WEBINAR SERIES: AI in the life sciences: exploring possibilities, inspiring change

This record collates training materials associated with the Australian BioCommons webinar series 'AI in the life sciences: Exploring possibilities, inspiring change' that took place between June - September 2025.
Series description
Join us for a series of webinars where we explore how Artificial...

Keywords: AI, Bioinformatics, Life Sciences

WEBINAR SERIES: AI in the life sciences: exploring possibilities, inspiring change https://dresa.org.au/materials/webinar-series-ai-in-the-life-sciences-exploring-possibilities-inspiring-change This record collates training materials associated with the Australian BioCommons webinar series 'AI in the life sciences: Exploring possibilities, inspiring change' that took place between June - September 2025. Series description Join us for a series of webinars where we explore how Artificial Intelligence (AI) is shaping the future of life sciences! This series provides an accessible introduction to AI while giving direct access to experts and practical insights into real-world applications. Designed to inspire and help you recognise potential applications of AI in the life sciences, these webinars will spark new ways of thinking so that you can start applying AI in your work.  The webinars include: A foundational session covering AI basics, its evolution, and why it matters for life sciences. Watch the recording here! Guest speaker sessions where leading experts from academia and industry share how AI is being applied in different domains Live Q&A to engage with speakers, ask questions, and participate in discussions Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Series metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Campos (PDF): a PDF copy of the slides presented by Dr Túlio de Lima Campos during the webinar. Li (PDF): a PDF copy of the slides presented by Dr Maisie Li during the webinar. Salazar (PDF): a PDF copy of the slides presented by Dr Vinícius W. Salazar during the webinar. Harms (PD): a PDF copy of the slides presented by Dr Rebekah Harms during the webinar. Veit (PDF): a PDF copy of the slides presented by Dr Veit Schwämmle during the webinar. Materials shared elsewhere: Recordings of all webinars in this series are available Australian BioCommons YouTube channel. Deciphering AI for the Life Sciences  Dr Benjamin Goudey, Australian BioCommons Recording: https://www.youtube.com/watch?v=sbVzcrD-wko  Slides: https://doi.org/10.5281/zenodo.15110330 Our journey incorporating AI into our cancer computational research Dr Anna Trigos, PeterMac Recording: https://youtu.be/vCkGbWuyLaQ?si=KLtjgdMs5sQscrq- Slides: https://doi.org/10.5281/zenodo.15770502 Towards Human-AI Collaboration in Genomics and Bioinformatics Dr Maisie Li, CSIRO A Journey into Binary Classification Challenges in AI Dr Túlio de Lima Campos, Oswaldo Cruz Foundation (Brazil) and University of Melbourne Recording: https://youtu.be/3Ge9aymRKRI?si=9Rg4sl1wWXIrkKmv Deep Learning Meets the Deep Sea: AI in Microbial Oceanography  Dr Vinícius W. Salazar, Melbourne Bioinformatics AI-Driven Discovery and Therapeutic Innovation in Fungal and Bacterial Pathogenesis Dr Carlos Santos-Martin, University of Melbourne Recording: https://youtu.be/qXK7Uvf6Utk?si=15iSaeVkgnMa-nOC Improving the interpretability of AI models for cell biology and precision medicine  Dr Stefano Mangiola, University of Adelaide Bridging pharmacology and AI: Accelerating GPCR drug discovery with deep learning Dr Anh TN Nguyen, Monash University Recording: https://youtu.be/-m0tvmNgFic?si=jBruJ3U4uSnUYeoa Ensuring equity in the integration of artificial intelligence in engineering biology Dr Rebekah Harms, UNSW Data equity and the challenges of diversifying datasets for artificial intelligence Dr Yves Saint James Aquino, University of Wollongong Recording: https://youtu.be/6bPY4Dquabs?si=YLl5PxNzoEdguR0J AI-readiness of proteomics data: challenges, applications, and future perspectives Tine Claeys, UGent An overview of deep learning methods to enhance proteomics data analysis Dr Veit Schwämmle, SDU Recording: https://youtu.be/qImAEHkXBKY?si=ItX-2af6Fyhy3rY9   Melissa Burke (melissa@biocommons.org.au) AI, Bioinformatics, Life Sciences
WORKSHOP: Nextflow on HPC

This record includes training materials associated with the Australian BioCommons workshop ‘Nextflow on HPC’. This workshop took place over two sessions on 18 - 19 December 2025.
Event description
Outgrown your laptop? Learn how to take your bioinformatics pipelines to the next level by scaling...

Keywords: Bioinformatics, Analysis, Workflow, Nextflow

WORKSHOP: Nextflow on HPC https://dresa.org.au/materials/workshop-nextflow-on-hpc This record includes training materials associated with the Australian BioCommons workshop ‘Nextflow on HPC’. This workshop took place over two sessions on 18 - 19 December 2025. Event description Outgrown your laptop? Learn how to take your bioinformatics pipelines to the next level by scaling them on Australia’s national high performance computing (HPC) clusters. In collaboration with the National Computational Infrastructure (NCI) and the Pawsey Supercomputing Research Centre, this hands-on workshop will equip you with the practical skills to run and optimise efficient Nextflow workflows on HPC systems. Attendees will be guided through a series of hands-on exercises to configure nf-core workflows on NCI Gadi (PBS) and Pawsey Setonix (SLURM), and will iteratively develop a bioinformatics workflow to process larger files, and more samples. Lead trainers:  Fred Jaya, Senior Bioinformatician (Australian BioCommons), Sydney Informatics Hub, University of Sydney Dr Michael Geaghan, Senior Bioinformatician (Australian BioCommons), Sydney Informatics Hub, University of Sydney Facilitators:  Dr Mitchell O’Brien, Sydney Informatics Hub (SIH), University of Sydney Dr Georgina Samaha, Sydney Informatics Hub (SIH), University of Sydney Dr Cali Willet, Sydney Informatics Hub (SIH), University of Sydney Dr Kisaru Liyanage, National Computational Infrastructure (NCI) Australia Wenjing Xue, National Computational Infrastructure (NCI) Australia Dr Kristina Gagalova, Curtin University (Nextflow Ambassador) Gayatri Aniruddha, The University of Western Australia Infrastructure provision:  Dr Sarah Beecroft, Pawset (Technical Support) Dr Abdullah Shaikh, National Computational Infrastructure (NCI) (Technical Support) Host: Dr Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Materials shared elsewhere: The materials were developed by the Sydney Informatics Hub, University of Sydney.  Training materials webpage: https://sydney-informatics-hub.github.io/nextflow-hpc-workshop/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Workflow, Nextflow
WEBINAR SERIES: Leveraging deep learning to design custom protein-binding-proteins

This record collates training materials associated with the Australian BioCommons webinar series 'Leveraging deep learning to design custom protein-binding proteins' that took place between July - November 2025.
Series description
Deep learning methods are speeding up the process of designing...

Keywords: Structural Biology, Deep learning, Life science, AI, Bioinformatics

WEBINAR SERIES: Leveraging deep learning to design custom protein-binding-proteins https://dresa.org.au/materials/webinar-series-leveraging-deep-learning-to-design-custom-protein-binding-proteins This record collates training materials associated with the Australian BioCommons webinar series 'Leveraging deep learning to design custom protein-binding proteins' that took place between July - November 2025. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Series metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Materials shared elsewhere: Recordings of all webinars in this series are available Australian BioCommons YouTube channel. The slides from these webinars are shared in Zenodo. Using AI protein design to design binding proteins to challenging bacterial transporters Dr Rhys Grinter, University of Melbourne Recording: https://youtu.be/3Ad2gUjeSL8 Slides: https://zenodo.org/records/16511653 AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors Dr Cyntia Taveneau, Monash University Recording: https://youtu.be/GSoOfyJUYSA Slides: https://zenodo.org/records/17033917 Using in silico design methods to create de novo proteins that selectively modulate apoptosis Dr Richard Birkinshaw, WEHI Recording: https://youtu.be/9-3sHy1ybpE Slides: https://zenodo.org/records/17148914 Introducing ProteinDJ: A modular and open-source framework for protein design workflows Dr Josh Hardy, WEHI Recording: https://youtu.be/xwvF62HxaF0 Slides: https://zenodo.org/uploads/17337232 Baby steps in the AI-guided design of proteins to modulate gene transcription Professor Joel Mackay, University of Sydney Recording: https://youtu.be/tKqH8WlkIX4 Slides: https://zenodo.org/records/17605782   Melissa Burke (melissa@biocommons.org.au) Structural Biology, Deep learning, Life science, AI, Bioinformatics
WEBINAR: Baby steps in the AI-guided design of proteins to modulate gene transcription

This record includes training materials associated with the Australian BioCommons webinar ‘Baby steps in the AI-guided design of proteins to modulate gene transcription’. This webinar took place on 11 November 2025 and is part of the series 'Leveraging deep learning to design custom...

Keywords: Bioinformatics, Structural biology, Protein design, AI, Deep learning

WEBINAR: Baby steps in the AI-guided design of proteins to modulate gene transcription https://dresa.org.au/materials/webinar-baby-steps-in-the-ai-guided-design-of-proteins-to-modulate-gene-transcription This record includes training materials associated with the Australian BioCommons webinar ‘Baby steps in the AI-guided design of proteins to modulate gene transcription’. This webinar took place on 11 November 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding proteins'. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Professor Joel Mackay, University of Sydney Host: Dr Melissa Burke, Australian BioCommons Talk title: Baby steps in the AI-guided design of proteins to modulate gene transcription Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Mackay_2025_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube channel: https://youtu.be/tKqH8WlkIX4 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Structural biology, Protein design, AI, Deep learning
WEBINAR: ProteinDJ: A modular and open-source framework for protein design workflows

This record includes training materials associated with the Australian BioCommons webinar ‘ProteinDJ: A modular and open-source framework for protein design workflows’. This webinar took place on 7 October 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding...

Keywords: Bioinformatics, Structural Biology, Deep learning, AI, Protein design

WEBINAR: ProteinDJ: A modular and open-source framework for protein design workflows https://dresa.org.au/materials/webinar-proteindj-a-modular-and-open-source-framework-for-protein-design-workflows This record includes training materials associated with the Australian BioCommons webinar ‘ProteinDJ: A modular and open-source framework for protein design workflows’. This webinar took place on 7 October 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding proteins'. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Josh Hardy, WEHI Host: Dr Melissa Burke, Australian BioCommons Talk title: ProteinDJ: A modular and open-source framework for protein design workflows Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Hardy_2025_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/xwvF62HxaF0 Related materials ProteinDJ is openly available on GitHub: https://github.com/PapenfussLab/proteindj ProteinDJ is described in the preprint: https://www.biorxiv.org/content/10.1101/2025.09.24.678028v1 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Structural Biology, Deep learning, AI, Protein design
WORKSHOP: Machine learning in the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’ which took place on 19 - 20 August 2025.
Event description
Machine learning promises to revolutionise life science research by speeding up data analysis, enabling...

Keywords: Machine learning, Life science, Bioinformatics

WORKSHOP: Machine learning in the life sciences https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences-aade1c3f-da82-4b4b-b938-acaf23c73548 This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’ which took place on 19 - 20 August 2025. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job.  Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We dis Lead trainer:  Dr Benjamin Goudey, AI Technical Lead, Australian BioCommons Facilitators: Dr Giorgia Mori, BioCloud Training and Communications Officer, Australian BioCommons Host: Dr Melissa Burke, Training Manager, Australian BioCommons. Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.   Materials shared elsewhere: The slides and Google colab notebook used in this workshop are available on GitHub:https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR Melissa Burke (melissa@biocommons.org.au) Machine learning, Life science, Bioinformatics
ProteinDJ: A modular and open-source framework for protein design workflows

This record includes training materials associated with the Australian BioCommons webinar ‘ProteinDJ: A modular and open-source framework for protein design workflows’. This webinar took place on 7 October 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding...

Keywords: Bioinformatics, Structural Biology, Deep learning, AI, Protein design

ProteinDJ: A modular and open-source framework for protein design workflows https://dresa.org.au/materials/proteindj-a-modular-and-open-source-framework-for-protein-design-workflows This record includes training materials associated with the Australian BioCommons webinar ‘ProteinDJ: A modular and open-source framework for protein design workflows’. This webinar took place on 7 October 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding proteins'. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Josh Hardy, WEHI Host: Dr Melissa Burke, Australian BioCommons Talk title: ProteinDJ: A modular and open-source framework for protein design workflows Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Hardy_2025_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/xwvF62HxaF0 Related materials ProteinDJ is openly available on GitHub: https://github.com/PapenfussLab/proteindj ProteinDJ is described in the preprint: https://www.biorxiv.org/content/10.1101/2025.09.24.678028v1 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Structural Biology, Deep learning, AI, Protein design
WEBINAR: Using in silico design methods to create de novo proteins that selectively modulate apoptosis

This record includes training materials associated with the Australian BioCommons webinar ‘Using in silico design methods to create de novo proteins that selectively modulate apoptosis’. This webinar took place on 16 September 2025 and is part of the series 'Leveraging deep learning to design...

Keywords: BIoinformatics, Proteomics, Deep learning, Protein design, Structural biology

WEBINAR: Using in silico design methods to create de novo proteins that selectively modulate apoptosis https://dresa.org.au/materials/webinar-using-in-silico-design-methods-to-create-de-novo-proteins-that-selectively-modulate-apoptosis This record includes training materials associated with the Australian BioCommons webinar ‘Using in silico design methods to create de novo proteins that selectively modulate apoptosis’. This webinar took place on 16 September 2025 and is part of the series 'Leveraging deep learning to design custom protein-binding proteins'. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Richard Birkinshaw Host: Dr Melissa Burke, Australian BioCommons Talk title: Using in silico design methods to create de novo proteins that selectively modulate apoptosis Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Birkinshaw_2025_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/9-3sHy1ybpE Melissa Burke (melissa@biocommons.org.au) BIoinformatics, Proteomics, Deep learning, Protein design, Structural biology
WEBINAR: Getting started with spatial omics

This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with spatial omics’. This webinar took place on 10 September 2025.
Event description
Spatial omics provides unprecedented opportunities for the understanding of cells, tissues and systems by...

Keywords: Bioinformatics, Spatial omics

WEBINAR: Getting started with spatial omics https://dresa.org.au/materials/webinar-getting-started-with-spatial-omics This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with spatial omics’. This webinar took place on 10 September 2025. Event description Spatial omics provides unprecedented opportunities for the understanding of cells, tissues and systems by combining omics and imaging technologies. This webinar provides a starting point from which to navigate the evolving field of spatial omics and the numerous methods available. We’ll begin with an overview of spatial omics and how it differs from other omics methods. We then explore considerations for analysis of spatial omics data, compare and contrast available tools and delve into some of the theory behind the bioinformatics. Speakers:  Dr Michael Geaghan, Australian BioCommons & Sydney Informatics Hub Dr Sarah Williams, QCIF Ltd. Content development: Dr Michael Geaghan, Australian BioCommons & Sydney Informatics Hub Dr Sarah Williams, QCIF Ltd. Dr Nicholas Matigian, QCIF Ltd Fred Jaya, Australian BioCommons & Sydney Informatics Hub Dr Mitchell O'Brien, Australian BioCommons & Sydney Informatics Hub Host: Dr Christina Hall, Australian BioCommons Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. 2025-09-10 Spatial-omics webinar.pdf: A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/fUbXyFFGohg Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Spatial omics
WEBINAR: AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors

This record includes training materials associated with the Australian BioCommons webinar ‘AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors’. This webinar took place on 12 August 2025 and is part of the series “Leveraging deep learning to design custom protein-binding...

Keywords: Bioinformatics, Structural Biology, Deep learning, Protein design

WEBINAR: AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors https://dresa.org.au/materials/webinar-aicrs-ai-designed-anti-crisprs-as-programmable-crispr-inhibitors This record includes training materials associated with the Australian BioCommons webinar ‘AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors’. This webinar took place on 12 August 2025 and is part of the series “Leveraging deep learning to design custom protein-binding proteins”. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Cynita Taveneau, Monash University Host: Dr Melissa Burke, Australian BioCommons Talk title: AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Taveneau_2025_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/GSoOfyJUYSA Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Structural Biology, Deep learning, Protein design
TERN Educational Resources

TERN education aims to advance understanding and contribute to managing Australia’s environment and unique ecosystems through awareness raising of our long-term environmental monitoring programs, protocols and data products.

We will do this by providing learners, educators, and, end-users,...

Keywords: Ecosystem science, environment, ecology, data, FAIR training material

TERN Educational Resources https://dresa.org.au/materials/tern-educational-resources TERN education aims to advance understanding and contribute to managing Australia’s environment and unique ecosystems through awareness raising of our long-term environmental monitoring programs, protocols and data products. We will do this by providing learners, educators, and, end-users, with a multitude of data-driven educational activities that can be accessed via a modular, self-paced format with online and offline options. The TERN education approach: beginner, intermediate, and advanced TERN tools and learning experiences are aimed at beginner, intermediate, and advanced levels. This approach enables individuals to self-define their interests and abilities regardless of their affiliations (e.g., K—12 teacher, citizen scientist, family). As individuals take more responsibility and control of their own learning, it is critical that TERN facilitates ample free-choice learning opportunities where individuals can easily access, use, and contribute to TERN products to meet their needs and interests. TERN will include in-person and virtual capabilities to enable educational and public use of the TERN ecosystem research infrastructure. tern@uq.edu.au Ecosystem science, environment, ecology, data, FAIR training material
ARDC Digital Research Skills Strategy Tool

This is a decision tool to help you determine components of a digital research skills strategy.
When you create a skills strategy, you need to work out:

Which people
Need which skills
At what level
Delivered how

This tool helps you:

Create typical user roles that outline groups who need...

Keywords: Training material, skills strategy, decision tools, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework, role profile, capabilities, user personas, proficiency levels, training activities, skills, digital skills, digital research infrastructure

ARDC Digital Research Skills Strategy Tool https://dresa.org.au/materials/ardc-digital-research-skills-strategy-tool This is a decision tool to help you determine components of a digital research skills strategy. When you create a skills strategy, you need to work out: Which people Need which skills At what level Delivered how This tool helps you: Create typical user roles that outline groups who need distinct clusters of skills; Describe training preferences and pain points for each role; Identify which skills in the ARDC Digital Research Capabilities and Skills Framework each role needs; Nominate the level of competency each role requires; and Map out the training approach for each skill. The Skilled Workforce Development Team of the Australian Research Data Commons developed this tool to support the ARDC mission to accelerate Australian research and innovation. contact@ardc.edu.au Training material, skills strategy, decision tools, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework, role profile, capabilities, user personas, proficiency levels, training activities, skills, digital skills, digital research infrastructure
WORKSHOP: Nextflow for the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Nextflow for the life sciences’. This workshop took place over two sessions on 22 - 23 July 2025.
Event description
The rise of big data has made it essential to be able to analyse and perform experiments...

Keywords: Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Workflows, Nextflow

WORKSHOP: Nextflow for the life sciences https://dresa.org.au/materials/workshop-nextflow-for-the-life-sciences This record includes training materials associated with the Australian BioCommons workshop ‘Nextflow for the life sciences’. This workshop took place over two sessions on 22 - 23 July 2025. Event description The rise of big data has made it essential to be able to analyse and perform experiments on large datasets in a portable and reproducible manner. Nextflow is a popular bioinformatics workflow orchestrator that makes it easy to run data-intensive computational pipelines. It enables scalable and reproducible scientific workflows using software containers on any infrastructure. It allows the adaptation of workflows written in most languages and provides the ability to customise and optimise workflows for different computational environments, types and sizes of data. Learn to build reproducible and scalable scientific workflows with Nextflow in this two-part workshop. Part one covers the fundamental principles of Nextflow pipeline development using the “Hello Nextflow” materials. Part two offers practical, hands-on experience creating a multi-sample Nextflow workflow for RNAseq data preparation Lead trainers:  Fred Jaya, Senior Bioinformatician (Australian BioCommons), Sydney Informatics Hub, University of Sydney. Dr Michael Geaghan, Senior Bioinformatician (Australian BioCommons), Sydney Informatics Hub, University of Sydney. Facilitators:  Brisbane: Magdalena Antczak (Queensland Cyber Infrastructure Foundation (QCIF)) and Marie-Emilie Gauthier (Queensland University of Technology (QUT)) Perth: Sarah Beecroft and Pratihba Raghunandan (Pawsey Supercomputing Centre) Canberra: Kisaru Liyanage(National Computational Infrastructure (NCI)) Adelaide: John Salamon and Michael Roach (South Australian Genomics Centre (SAGC)) Melbourne: Emma Gail and Grace Hall (Melbourne Bioinformatics), Richard Lupat (Peter MacCallum Cancer Centre) Sydney: Thanh Nguyen, Eric Urng, Matthew Hobbs (Garvan Institute of Medical Research) Infrastructure provision: Wenjing Xue (National Computational Infrastructure (NCI)) Host: Dr Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Materials shared elsewhere: The materials were developed by the Sydney Informatics Hub, University of Sydney. The workshop was enabled through the Australian BioCommons - BioCLI Platforms Project (NCRIS via Bioplatforms Australia). Training materials webpage: https://sydney-informatics-hub.github.io/hello-nextflow-2025/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Workflows, Nextflow
WEBINAR: Deciphering AI for the Life Sciences

This record includes training materials associated with the Australian BioCommons webinar ‘Deciphering AI for the Life Sciences'. This webinar took place on 18 March 2025.
Event description 
Curious about how Artificial Intelligence (AI) is transforming life sciences? 
AI is reshaping life...

Keywords: Life Sciences, Bioinformatics

WEBINAR: Deciphering AI for the Life Sciences https://dresa.org.au/materials/webinar-deciphering-ai-for-the-life-sciences This record includes training materials associated with the Australian BioCommons webinar ‘Deciphering AI for the Life Sciences'. This webinar took place on 18 March 2025. Event description  Curious about how Artificial Intelligence (AI) is transforming life sciences?  AI is reshaping life sciences by enabling researchers to analyze complex datasets, automate workflows, and gain deeper insights into biological processes. This introductory webinar will break down AI concepts, clarify key terminology, and showcase real-world examples of AI applications in the life sciences. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainer: Dr Benjamin Goudey, AI Technical Lead, Australian BioCommons Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. DOME_Webinar (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=ijFg3VbO2VM Melissa Burke (melissa@biocommons.org.au) Life Sciences, Bioinformatics
TERN Knowledge base

Link to a knowledge base created to access all TERN developed tools and services. The knowledge base consist of user manual to use TERN Data Discovery Portal, TERN SHaRED - data submission tool, TERN EcoPlots, TERN EcoImages, TERN CoESRA.

Keywords: Ecology, research data, Data analysis, data, Research infrastructure, acoustic, workshops, training, Statistical Analysis, Biodiversity data, Camera traps, NCRIS, Ecosystem science, environment, ecology, R

TERN Knowledge base https://dresa.org.au/materials/tern-knowledge-base Link to a knowledge base created to access all TERN developed tools and services. The knowledge base consist of user manual to use TERN Data Discovery Portal, TERN SHaRED - data submission tool, TERN EcoPlots, TERN EcoImages, TERN CoESRA. esupport@tern.org.au Ecology, research data, Data analysis, data, Research infrastructure, acoustic, workshops, training, Statistical Analysis, Biodiversity data, Camera traps, NCRIS, Ecosystem science, environment, ecology, R
WEBINAR: Using AI protein design to design binding proteins to challenging bacterial transporters

This record includes training materials associated with the Australian BioCommons webinar ‘Using AI protein design to design binding proteins to challenging bacterial transporters’. This webinar took place on 15 July 2025 and is part of the series “Leveraging deep learning to design custom...

Keywords: Bioinformatics, Structural biology, Deep learning, Protein design

WEBINAR: Using AI protein design to design binding proteins to challenging bacterial transporters https://dresa.org.au/materials/webinar-using-ai-protein-design-to-design-binding-proteins-to-challenging-bacterial-transporters This record includes training materials associated with the Australian BioCommons webinar ‘Using AI protein design to design binding proteins to challenging bacterial transporters’. This webinar took place on 15 July 2025 and is part of the series “Leveraging deep learning to design custom protein-binding proteins”. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Rhys Grinter, University of Melbourne Host: Dr Melissa Burke, Australian BioCommons Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Grinter_slides_2025 (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/3Ad2gUjeSL8 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Structural biology, Deep learning, Protein design
WORKSHOP: Retrieving nucleotide sequencing data from the European Nucleotide Archive

This record includes training materials associated with the Australian BioCommons workshop ‘Retrieving nucleotide sequencing data from the European Nucleotide Archive’. This workshop took place on 3 April 2025.
Event description
The European Nucleotide Archive (ENA) is the European node of the...

Keywords: Bioinformatics, Data retrieval, Genomics, FAIR

WORKSHOP: Retrieving nucleotide sequencing data from the European Nucleotide Archive https://dresa.org.au/materials/workshop-retrieving-nucleotide-sequencing-data-from-the-european-nucleotide-archive This record includes training materials associated with the Australian BioCommons workshop ‘Retrieving nucleotide sequencing data from the European Nucleotide Archive’. This workshop took place on 3 April 2025. Event description The European Nucleotide Archive (ENA) is the European node of the International Nucleotide Sequence Database Collaboration (INSDC), providing a comprehensive record of the world’s nucleotide sequencing information, covering raw read sequencing data, sequence assembly information and functional annotation. The three INSDC members (ENA, NCBI-SRA and DDBJ-SRA) routinely exchange data which ensures nucleotide data is archived and shared across geographically dispersed locations (Europe, USA and Japan). The ENA is provided by EMBL’s European Bioinformatics Institute, EMBL-EBI. This workshop provides an introduction to the ENA data and metadata model and data retrieval tools, followed by an opportunity to practice retrieving a range of different data types from the ENA using a variety of tools and protocols. Lead trainers:  Dr Joana Pauperio, Biodiversity Curator, European Nucleotide Archive, EMBL-European Bioinformatics Institute Maira Ihsan, User Support Bioinformatician, European Nucleotide Archive, EMBL-European Bioinformatics Institute Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. ENA_data_retrieval_slides (PDF): Slides that introduce the ENA metadata model and provide an overview of methods of data retrieval from the ENA ENA_data_retrieval_practical (PDF): A practical guide that provides step by step instructions on how to retrieve data from the ENA Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Data retrieval, Genomics, FAIR
WORKSHOP SERIES: Submitting sequencing data and genome assemblies to the European Nucleotide Archive

This record includes training materials associated with the Australian BioCommons workshop series ‘Submitting sequencing data and genome assemblies to the European Nucleotide Archive. These workshops took place between 25 March - 2 April 2025
Event description
The European Nucleotide Archive...

Keywords: Bioinformatics, Data submission, FAIR, Genomics, eDNA, metagenomics, Genome assembly and annotation

WORKSHOP SERIES: Submitting sequencing data and genome assemblies to the European Nucleotide Archive https://dresa.org.au/materials/workshop-series-submitting-sequencing-data-and-genome-assemblies-to-the-european-nucleotide-archive This record includes training materials associated with the Australian BioCommons workshop series ‘Submitting sequencing data and genome assemblies to the European Nucleotide Archive. These workshops took place between 25 March - 2 April 2025 Event description The European Nucleotide Archive (ENA) is the European node of the International Nucleotide Sequence Database Collaboration (INSDC), providing a comprehensive record of the world’s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. The three INSDC members (ENA, NCBI-SRA and DDBJ-SRA) routinely exchange data which ensures nucleotide data is archived and shared across geographically dispersed locations (Europe, USA and Japan). The ENA is provided by EMBL’s European Bioinformatics Institute, EMBL-EBI. ENA team members Dr Joana Pauperio and Maira Ihsan will deliver a series of related workshops on submitting raw read sequencing, Metagenome-Assembled Genome (MAG), environmental DNA (eDNA) and genome assembly and annotation data to ENA.  Each workshop will begin with an introduction to the ENA data and metadata model. You will then be guided through hands-on exercises using example data sets to practice data submission via one of three submission routes: Interactive web-based submission: these are completed by filling out web forms in your browser and downloading template spreadsheets that can be completed off-line and uploaded to ENA.  Command-line based submission: Data submissions of this type are completed via the command line using ENA's bespoke Webin-CLI program. This validates your submissions entirely before you complete them, allowing you maximum control of the process. Webin-CLI is the only way to submit assembled genomes and transcriptomes. Programmatic submission: these are completed by preparing your submissions as XML/JSON documents and either sending them to ENA using a program such as cURL or using ENA's Webin Portal Workshops in this series include: 25 March 2025, 1 - 4 pm AEDT: Submitting raw read sequencing data using interactive web-based tools 26 March 2025, 1 - 4 pm AEDT: Submitting raw read sequencing data using programmatic tools 27 March 2025, 1 - 3 pm AEDT: Submitting raw-read sequencing data using command line based tools 31 March 2025, 1 - 4 pm AEDT: Submitting genome assembly and annotation data using the command line 1 April 2025, 1 - 4 pm AEDT: Submitting Metagenome-Assembled Genome (MAG) data to ENA and MGNify using the command line Metagenome-Assembled Genome (MAG) Command-line submission 2 April 2025, 1 - 4 pm AEDT: Submitting environmental DNA (eDNA) data Lead trainers:  Dr Joana Pauperio, Biodiversity Curator, European Nucleotide Archive, EMBL-European Bioinformatics Institute Maira Ihsan, User Support Bioinformatician, European Nucleotide Archive, EMBL-European Bioinformatics Institute Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the series of workshops including, description, event URL, learning objectives, prerequisites, technical requirements etc. Each workshop in this series has: Slides that introduce the ENA metadata model and provide an overview of the submission process and/or type of data that is the focus of the workshop. A practical guide that provides step by step instructions on how to submit data Submitting raw read sequencing data using interactive web-based tools Slides: ENA_submission_interactive_slides.pdf Practical: ENA_submission_interactive_practical.pdf Submitting raw read sequencing data using programmatic tools Slides: ENA_submission_programmatic_slides.pdf Practical: ENA_submission_programmatic_practical.pdf  Submitting raw-read sequencing data using command line based tools Slides: ENA_submission_commandline_slides.pdf Practical: ENA_submission_commandline_practical.pdf Submitting genome assembly and annotation data using the command line Slides: ENA_submission_assemblies_annotations_slides.pdf Practical: ENA_submission_assemblies_annotations_practical.pdf Submitting Metagenome-Assembled Genome (MAG) data to ENA and MGNify using the command line Metagenome-Assembled Genome (MAG) Command-line submission Slides: ENA_submission_MAGs_slides.pdf Practical: ENA_submission_MAGs_practical.pdf Submitting environmental DNA (eDNA) data Slides: ENA_submission_eDNA_slides.pdf Practical: ENA_submission_eDNA_practical.pdf Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Data submission, FAIR, Genomics, eDNA, metagenomics, Genome assembly and annotation
WEBINAR: Our journey incorporating AI into our cancer computational research

This record includes training materials associated with the Australian BioCommons webinar ‘Our journey incorporating AI into our cancer computational research’. This webinar is part of the webinar series 'AI in the life sciences: exploring possibilities, inspiring change' and took place on 11...

Keywords: Bioinformatics http://edamontology.org/topic_0091

WEBINAR: Our journey incorporating AI into our cancer computational research https://dresa.org.au/materials/webinar-our-journey-incorporating-ai-into-our-cancer-computational-research This record includes training materials associated with the Australian BioCommons webinar ‘Our journey incorporating AI into our cancer computational research’. This webinar is part of the webinar series 'AI in the life sciences: exploring possibilities, inspiring change' and took place on 11 June 2025. Event description Join us for a series of webinars where we explore how Artificial Intelligence (AI) is shaping the future of lifesciences!This series provides an accessible introduction to AI while giving direct access to experts and practical insights into real-world applications. Designed to inspire and help you recognise potential applications of AI in the life sciences, these webinars will spark new ways of thinking so that you can start applying AI in your work. The webinars include: A foundational session covering AI basics, its evolution, and why it matters for life sciences. (Watch the recording here!) Guest speaker sessions where leading experts from academia and industry share how AI is being applied in different domains Live Q&A to engage with speakers, ask questions, and participate in discussions. Dr. Anna Trigos session focuses on exploring the real-world challenges that many researchers face before any models are built or predictions made. Expect practical reflections, and inspiration for those at the beginning of their own AI journeys. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Speaker: Dr Anna Trigos, Peter MacCallum Cancer Centre Host:  Dr Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. AI_presentation_trigos (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=vCkGbWuyLaQ Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091
Submitting a Data Request to Health Data Australia: Documentation for Data Requesters

This documentation is a supplementary resource for researchers submitting a formal request for access to a dataset listed on the Health Data Australia (HDA) platform. This was developed as part of a work package for Health Studies Australian National Data Asset.

Keywords: HeSANDA, Health Data Australia, Data Request, Clinical Trials, Secondary Data, Training Material

Submitting a Data Request to Health Data Australia: Documentation for Data Requesters https://dresa.org.au/materials/submitting-a-data-request-to-health-data-australia-documentation-for-data-requesters This documentation is a supplementary resource for researchers submitting a formal request for access to a dataset listed on the Health Data Australia (HDA) platform. This was developed as part of a work package for Health Studies Australian National Data Asset. contact@ardc.edu.au HeSANDA, Health Data Australia, Data Request, Clinical Trials, Secondary Data, Training Material
WEBINAR: No code, no problem - data analysis for biologists with Galaxy Australia

This record includes training materials associated with the Australian BioCommons webinar ‘No code, no problem - data analysis for biologists with Galaxy Australia’. This webinar took place on 19 April 2025.
Event description
Life science research is all about data. Turning that data into useful...

Keywords: Bioinformatics, Data analysis

WEBINAR: No code, no problem - data analysis for biologists with Galaxy Australia https://dresa.org.au/materials/webinar-no-code-no-problem-data-analysis-for-biologists-with-galaxy-australia This record includes training materials associated with the Australian BioCommons webinar ‘No code, no problem - data analysis for biologists with Galaxy Australia’. This webinar took place on 19 April 2025. Event description Life science research is all about data. Turning that data into useful biological knowledge and publication-worthy figures requires specialised bioinformatics resources and compute. This is where Galaxy comes in. Galaxy is an accessible, online platform for reproducible and transparent data analysis. You don’t need command line skills to use it and it includes 1000’s of popular, community backed bioinformatics tools which is why it has become the go-to platform for thousands of life scientists worldwide. Join us to learn how Galaxy is being used in a wide range of research including genome assembly and annotation, metagenomics, proteomics, transcriptomics, data visualisation and more. We’ll show you the features that make this such a versatile platform whether you are new to data analysis or are looking to take your workflows to the next level! Galaxy Australia is a fully subsidised service provided by Australian BioCommons. The service receives NCRIS funding through Bioplatforms Australia, as well as The University of Melbourne and Queensland Government RICF funding. Speakers: Dr Tiffanie Nelson, Genomics Research Community Engagement Lead, Australian BioCommons Dr Tristan Reynolds, Galaxy Outreach Specialist, The University of Melbourne   Host: Dr Melissa Burke, Training Manager, Australian BioCommons   Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Slides - No code no problem - data analysis with Galaxy (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/XGGbCzhFlFI   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Data analysis
Secondary Use of Clinical Trials Data in Health Research: A Practical Guide

This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.  
Four overall scenarios for data reuse were identified; scenario...

Keywords: Secondary Data Use, Clinical Trials, Training Material, HeSANDA, Health Data Australia, HDA

Secondary Use of Clinical Trials Data in Health Research: A Practical Guide https://dresa.org.au/materials/secondary-use-of-clinical-trials-data-in-health-research-a-practical-guide-13bd896d-0f4d-4488-b341-59571c434b96 This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.   Four overall scenarios for data reuse were identified; scenario 1: evidence synthesis, scenario 2: secondary analyses, scenario 3: reproducibility, replication and validation, and scenario 4: education and methods development. View an online version of this pdf document on the ARDC website. contact@ardc.edu.au Secondary Data Use, Clinical Trials, Training Material, HeSANDA, Health Data Australia, HDA
WORKSHOP: RNAseq: reads to differential expression

This record includes training materials associated with the Australian BioCommons workshop ‘RNAseq: reads to differential expression’. This workshop took place on 10 - 11 September 2024.
Event description
RNA-Seq is a popular method for measuring differential gene expression. This hands-on...

Keywords: Bioinformatics, Transcriptomics, RNAseq

WORKSHOP: RNAseq: reads to differential expression https://dresa.org.au/materials/workshop-rnaseq-reads-to-differential-expression This record includes training materials associated with the Australian BioCommons workshop ‘RNAseq: reads to differential expression’. This workshop took place on 10 - 11 September 2024. Event description RNA-Seq is a popular method for measuring differential gene expression. This hands-on workshop introduces the concepts of RNA-Seq analysis, from data preparation through to statistical testing for differential gene expression. You’ll learn how to use popular tools and workflows to analyse the data, produce graphical summaries and identify differentially expressed genes. The workshop will focus on the use of Galaxy, a web-based platform for accessible, reproducible, and transparent computational biological research. Widely used by researchers world wide, Galaxy gives you access to 1000’s of popular tools for analysis and processing of biological data. Galaxy Australia is a fully subsidised service provided by Australian BioCommons.  Lead trainer: Mike Thang, Galaxy Australia / QCIF Facilitators: Dr Selene Fernandez Valverde, UNSW Host: Dr Melissa Burke, Australian BioCommons.   Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule (PDF): provides a breakdown of the timing of the session. Materials shared elsewhere: The workshop followed the Galaxy Training Network Tutorials: RNAseq: reads to counts RNAseq counts to genes   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Transcriptomics, RNAseq
WORKSHOP: Phylogenetics: back to basics

This record includes training materials associated with the Australian BioCommons workshop ‘Phylogenetics: Back to basics’’. This workshop took place on 1 July 2024.
Event description
Phylogenetics is essential for comparing biological species and understanding biodiversity for conservation. This...

Keywords: Bioinformatics, Phylogenetics

WORKSHOP: Phylogenetics: back to basics https://dresa.org.au/materials/workshop-phylogenetics-back-to-basics This record includes training materials associated with the Australian BioCommons workshop ‘Phylogenetics: Back to basics’’. This workshop took place on 1 July 2024. Event description Phylogenetics is essential for comparing biological species and understanding biodiversity for conservation. This workshop discusses the basic principles and methods of phylogenetic inference and what you can learn from phylogenetic estimation. It is intended to help you make informed decisions about which methods to use in your research. Using real-life data and standard tools that are (mostly) available in Galaxy, the workshop demonstrates the principles behind a variety of methods used to estimate phylogenetic trees from aligned sequence data or distance data. This is not a "how to" workshop, but is instead aimed at giving you a better understanding of the principles of phylogenetics and how the methods work. Maybe you've even built phylogenetic trees before but want to know more about the principles behind the tools. Lead trainer: Professor Michael Charleston, University of Tasmania Facilitator: Michael Thang, QCIF and Galaxy Austrlaia Host: Dr Melissa Burke, Australian BioCommons.   Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule (PDF): provides a breakdown of the timing of the session. Materials shared elsewhere: This workshop follows the tutorial ‘Phylogenetics: back to basics’’ developed by Professor Michael Charleston and available on the Galaxy Training Network. https://training.galaxyproject.org/training-material/topics/evolution/tutorials/abc_intro_phylo/tutorial.html   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Phylogenetics
WEBINAR: DOME - Machine Learning Best Practices & Recommendations

This record includes training materials associated with the Australian BioCommons webinar ‘DOME - Machine Learning Best Practices & Recommendations’. This webinar took place on 5 December 2024.
Event description 
As the adoption of Artificial Intelligence (AI) and Machine Learning (ML)...

Keywords: Bioinformatics http://edamontology.org/topic_0091, Machine Learning http://edamontology.org/topic_3474

WEBINAR: DOME - Machine Learning Best Practices & Recommendations https://dresa.org.au/materials/webinar-dome-machine-learning-best-practices-recommendations This record includes training materials associated with the Australian BioCommons webinar ‘DOME - Machine Learning Best Practices & Recommendations’. This webinar took place on 5 December 2024. Event description  As the adoption of Artificial Intelligence (AI) and Machine Learning (ML) accelerates across life science research, the demand for standardised practices has become crucial to ensure transparency, reproducibility, and adherence to FAIR principles. In response to these needs, DOME (Data Optimization Model Evaluation) has been developed as a key solution - a set of community-wide recommendations designed to guide supervised ML analysis reporting in biological studies. DOME offers broad, field-agnostic guidelines to enhance the impact of ML applications while ensuring reproducibility. This framework not only supports robust model evaluation but also serves as a valuable resource for training and capacity building in life sciences.  Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainer: Dr Fotis Psomopoulos, Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. DOME_Webinar (PDF): A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=ijFg3VbO2VM     Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091, Machine Learning http://edamontology.org/topic_3474
WORKSHOP: Machine learning in the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024.
Event description
Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of...

Keywords: Bioinformatics, Life Science, Machine Learning

WORKSHOP: Machine learning in the life sciences https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them. Lead trainer:  Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health Facilitators: Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University William Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF) Dr Giorgia Mori, Sydney Informatics Hub, University of Sydney  Joseph McConnell, University of Adelaide Jessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955 Host: Dr Melissa Burke, Australian BioCommons. Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule (PDF): Schedule describing the timing of sessions for the in person and online events Materials shared elsewhere:   This workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Life Science, Machine Learning
WORKSHOP: Genetic Outlier Analysis

This record includes training materials associated with the Australian BioCommons workshop ‘Genetic Outlier Analysis’. 
These workshops took place on:

27 - 28 February 2024: Online via Zoom
10 - 11 April 2024: In person in Melbourne
4 - 5 July 2024: In person in Sydney 

Event description
There...

Keywords: Bioinformatics, Genetics

WORKSHOP: Genetic Outlier Analysis https://dresa.org.au/materials/workshop-genetic-outlier-analysis This record includes training materials associated with the Australian BioCommons workshop ‘Genetic Outlier Analysis’.  These workshops took place on: 27 - 28 February 2024: Online via Zoom 10 - 11 April 2024: In person in Melbourne 4 - 5 July 2024: In person in Sydney  Event description There are many interesting patterns that you can extract from genetic variant data. This can include patterns of linkage, balancing selection, or even inbreeding signals. One of the most common approaches is to find sites on the genome that are under selection.  This workshop introduces the basics of genetic selection analysis. It will step you through the process of identifying signals of selection using your own data (or an example genomic dataset) using the outlier analysis method.  Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. This workshop is presented by the Australian BioCommons and the Genetics Society of AustralAsia Lead trainer:  Dr Katarina Stuart, Research Fellow, University of Auckland.  Facilitators: Adele Barugahare, Monash Genomics and Bioinformatics Platform  Dr Georgina Samaha, Sydney Informatics Hub, University of Sydney  Dr Ching-Yu Lu, Sydney Informatics Hub, University of Sydney Soleille Miller, University of NSW Dr Nandan Deshpande, Sydney Informatics Hub, University of Sydney   Infrastructure provision: Audrey Stott, Pawsey Supercomputing Research Centre Host: Dr Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedules describing the timing of sessions for the in person and online events Materials shared elsewhere: These workshops followed the materials developed by Dr Katarina Stuart https://github.com/katarinastuart/Ev1_SelectionMetaAnalysis   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Genetics
WORKSHOP: Making sense of gene and protein lists with functional enrichment analysis

This record includes training materials associated with the Australian BioCommons workshop ‘Making sense of gene and protein lists with functional enrichment analysis’. This workshop took place over two, 3 hour sessions on 20, 21 November 2024.
Event description
Omics experiments often generate...

Keywords: Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Enrichment analysis http://edamontology.org/operation_3501

WORKSHOP: Making sense of gene and protein lists with functional enrichment analysis https://dresa.org.au/materials/workshop-making-sense-of-gene-and-protein-lists-with-functional-enrichment-analysis This record includes training materials associated with the Australian BioCommons workshop ‘Making sense of gene and protein lists with functional enrichment analysis’. This workshop took place over two, 3 hour sessions on 20, 21 November 2024. Event description Omics experiments often generate long lists of genes or proteins. Functional enrichment analysis identifies biological trends in the data by assessing these lists against gene ontology and pathway information. This can help interpret the experimental results in the context of larger biological systems. This workshop continues from our introductory webinar and provides a practical introduction to functional enrichment analysis. Using example data you will get hands-on with some of the most commonly used databases and tools for over representation (ORA) and gene set enrichment analysis (GSEA) and for identifying enriched biological functions in a list of genes or proteins. We’ll focus on tools available online and in R. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainers: Dr Hossein Valipour Kahrood, Bioinformatician, Monash Genomics and Bioinformatics Platform Dr Cali Willet, Senior Research Bioinformatician, Sydney Informatics Hub, University of Sydney Facilitators: Georgina Samaha, Australian BioCommons Laura Perlaza-Jimenez, Monash Genomics and Bioinformatics Platform Infrastructure provision: Uwe Winter, Australian BioCommonsHost: Dr. Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. R notebooks (zip): R markdown and html rendered files, input files. Materials shared elsewhere: Training materials webpage Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Enrichment analysis http://edamontology.org/operation_3501
WORKSHOP: Introduction to Machine Learning in R - from data to knowledge

This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024.
Event description
With the rise in high-throughput sequencing...

Keywords: Bioinformatics, Machine Learning

WORKSHOP: Introduction to Machine Learning in R - from data to knowledge https://dresa.org.au/materials/workshop-introduction-to-machine-learning-in-r-from-data-to-knowledge This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024. Event description With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially. A major issue is to mine useful knowledge from these heterogeneous collections of data. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine Learning (ML), a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets, can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics. This hands-on workshop will introduce participants to the ML taxonomy and the applications of common ML algorithms to health data. The workshop will cover the foundational concepts and common methods being used to analyse omics data sets by providing a practical context through the use of basic but widely used R libraries. Participants will acquire an understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead trainers: Dr Fotis Psomopoulos, Senior Researcher,  Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Facilitators: Dr Giorgia Mori, Australian BioCommons Dr Eden Zhang, Sydney Informatics Hub Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) Infrastructure provision: Uwe Winter, Australian BioCommons Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Files and materials shared elsewhere: Training materials webpage Data and documentation     Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning