• Eija Korpelainen, CSC - IT Center for Science, Finland

Single-cell omics (SCO), the ability to profile cells for various modalities, has revolutionized life science research. Single-cell technologies allow characterization of rare cell types and diverse cell states, and are thus well-suited to study many processes such as development and disease pathogenesis. Recently many technologies have been developed for spatially resolved transcriptomics, which adds spatial resolution to the data. The field is moving fast, and new technologies are accompanied by rapid development of new data analysis methods and tools.

While single-cell and spatial technologies open up unprecedented opportunities, they also create a major training challenge. How do we keep up to date with technology and analytics developments? Which SCO data types should we cover in our courses? Which analysis methods and tools should be taught? How to teach the complex theory of these methods, especially if course participants do not have a computational background?

Providing training and user support in a national core facility, I have had the opportunity to gain experience with different approaches to tackle the SCO training challenge. These include flipped classroom, eLearning, online courses, and web interfaces to analysis tools. In this talk I will share these experiences and also present international collaboration initiatives in SCO training.

• Marta Lloret-Llinares, EMBL's European Bioinformatics Institute, United Kingdom
• Catherine Brooksbank, EMBL's European Bioinformatics Institute, United Kingdom
• José Carbonell-Caballero, Barcelona Supercomputing Center, Spain
• Vera Matser, The Alan Turing Institute, United Kingdom
• Daniel Thomas-Lopez, EMBL's European Bioinformatics Institute, United Kingdom

PerMedCoE is the High Performance Computing (HPC) / Exascale Centre of Excellence for Personalised Medicine in Europe and aims at advancing personalised medicine by adapting cell-level simulation methods to pre-exascale systems to translate omics data into molecular disease models. The training programme contributes to this goal by educating professionals in life sciences and HPC communities about PerMedCoE developments.

PerMedCoE designed a competency-based training programme that focuses on user needs to enable them to run large scale cell-level simulations in HPC environments. The definition of relevant competencies and a training needs analysis supported our decision to prioritise training in modelling, use of HPC resources, and programming.

The PerMedCoE training programme targets consortium partners to increase capacity in tool development, and external users to facilitate the adoption of these tools. A combination of formats such as webinars, online and face-to-face courses allows us to reach a wide audience, including software developers and biomedical researchers.

The PerMedCoE training programme has been well received with positive feedback after the events and people returning to subsequent activities. This contributes to the creation of a community around the project and to expand the use of cell-level simulation tools, which facilitates the development of computational personalised medicine.

• Sofia Fertuzinhos, Yale University, United States
• Judy Spak, Yale University, United States
• Holly Grossetta-Nardini, Yale University, United States
• John Gallagher, Yale University, United States
• Rolando Garcia-Milian, Yale University, United States

The generation, analysis, and interpretation of -omics data have become essential in modern biological research, but many researchers lack the necessary computational skills. The Bioinformatics Support Hub team has developed a program at the Cushing/Whitney Medical Library at Yale School of Medicine that addresses this need by providing bioinformatics education, personalized consultations, and research assistance. The program guides researchers through a defined cycle of -omics data analysis, from data downloading to data processing, statistical analysis, biological interpretation, and data deposition in public repositories. The population acessing this program is diverse including students, postdoctoral researchers, clinicians, and faculty. Participants in workshops and consultations report the achievement of tangible research outcomes. The Bioinformatics Support Hub team also fosters collaboration and coordination among different departments, acting as a unifying force within the organization. Continuous improvement and feedback collection are integral to the program's success, with peer-to-peer teaching and engagement with developers of new tools and software to address knowledge gaps and challenges. Overall, the program developed by the Bioinformatics Support Hub team at the Yale’s Medical Library, empowers researchers with bioinformatics knowledge, skills, and tools to advance biomedical and clinical research, while fostering a culture of learning and collaboration within the research community.

• Constance Jeffery, University of Illinois at Chicago, United States

When the COVID-19 crisis shut down most undergraduate research opportunities, we developed an online, remote, computer-based Research Experiences for Undergraduates on the topic of Macromolecular Structure and Function. The program provided a mentored research experience and training in professional skills to assist the participants in pursuing a degree and future career in STEM. This fully virtual project involved faculty at four geographically-distributed institutions specializing in diverse but complementary approaches to study macromolecular structure and function. This project can also serve as an example for future remote, online projects that would especially be helpful for students who don’t have access to similar programs at their universities, cannot travel to attend a summer program, have physical challenges that make it difficult for them to work in a lab, or students whose research opportunities are limited due to war or civil unrest.

• Thomas Cokelaer, Institut Pasteur, France
• Sarah Cohen-Boulakia, Université Paris-Saclay, France
• Frédéric Lemoine, Institut Pasteur, France

Motivation: The reproducibility crisis has highlighted the importance of improving the way bioinformatics data analyses are implemented, executed, and shared. To address this, various tools such as content versioning systems, workflow management systems, and software environment management systems have been developed. While these tools are becoming more widely used, there is still much work to be done to increase their adoption. The most effective way to ensure reproducibility becomes a standard part of most bioinformatics data analysis projects is to integrate it into the curriculum of bioinformatics Master's programs.
Results: In this manuscript, we present the Reprohackathon, a Master's course that we have been running for the last three years at Université Paris-Saclay (France), and that has been attended by a total of 123 students. The course is divided into two parts. The first part includes lessons on the challenges related to reproducibility, content versioning systems, container management, and workflow systems. In the second part, students work on a data analysis project for 3-4 months, reanalyzing data from a previously published study. The Reprohackaton has taught us many valuable lessons, such as the fact that implementing reproducible analyses is a complex and challenging task that requires significant effort. However, providing in-depth teaching of the concepts and the tools during a Master's degree program greatly improves students' understanding and abilities in this area.

• Leyla Jael Castro, ZB MED Information Centre for Life Sciences, Germany
• Patricia M. Palagi, SIB Swiss Institute of Bioinformatics, Switzerland
• Michelle D. Brazas, Ontario Institute for Cancer Research, Canada

Stand-alone bioinformatics training events and e-learning solutions are among the most popular training modes because they address point-of-need learning and limited time frames for upskilling. However, finding relevant bioinformatics training courses and materials is difficult because such resources are not consistently marked up for Internet searches, thus not distinguishable from any other bioinformatics resource. The lack of markup standards for training resource discovery, reuse, and aggregation limits their usefulness and knowledge-translation potential. A set of Bioschemas Training profiles for training courses and materials - Course, CourseInstance, and TrainingMaterial - has been developed, published, and implemented through a collaborative effort between the Global Organization for Bioinformatics Learning, Education, and Training (GOBLET), the Bioschemas Training community, and the ELIXIR FAIR Training Focus Group. Here we present these three Bioschemas Training profiles and some corresponding implementations from organizations and training providers that have already deployed these profiles on their websites. We also discuss some challenges faced by implementers along with potential solutions. Over time, continued implementation of these profiles by training providers will lower the barriers to skill development, facilitating the discovery of relevant training events to meet individuals’ learning needs and the discovery and reuse of training materials.

• Nicola Mulder, Computational Biology Division, University of Cape Town, South Africa
• Verena Ras, Computational Biology Division, University of Cape Town, South Africa
• Shaun Aron, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, South Africa
• Tshinakaho Malesa, Computational Biology Division, University of Cape Town, South Africa
• Sindiswa Lukhele, Computational Biology Division, University of Cape Town, South Africa
• Sumir Panji, Computational Biology Division, University of Cape Town, South Africa

A major aim of the H3ABioNet project has been to design a sustainable approach to build and develop bioinformatics capacity to support genomics research across Africa. An integral component of this approach has focused on developing high quality training materials. H3ABioNet has thus embarked on a large effort to make our training materials (including supporting tools such as containers and workflows) FAIR. H3ABioNet ensures webpages reflect the most relevant information to ensure optimum findability and accessibility of materials. The use of tags and keywords across webpages helps to improve Search Engine Optimisation (SEO) along with submitting materials to a public repository to assign a permanent identifier, and improve data provenance and discoverability. We have also recently begun the process of implementing bioschemas (https://bioschemas.org/) across training webpages, making them more discoverable to web scrapers and training databases and repositories like GOBLET (https://www.mygoblet.org/) and TeSS (https://tess.elixir-europe.org/), thus increasing their findability. Since most materials within H3ABioNet are released under a creative commons license, materials are also free for re-use and distribution, allowing the reach of training materials to extend far beyond H3ABioNet. We aim to highlight our experiences with this talk.

• Geert van Geest, SIB Swiss Institute of Bioinformatics, Switzerland
• Yann Haefliger, SIB Swiss Institute of Bioinformatics, Switzerland
• Patricia M. Palagi, SIB Swiss Institute of Bioinformatics, Switzerland

Applying the FAIR principles to training material encourages its re-use, positively impacting trainees and course developers. Trainees can find and learn from training materials at their own pace, and trainers can build further upon existing training materials. In the past years, at SIB, we have applied the FAIR principles to our training material, and in this presentation, we will go through our established workflow. Along this process, we realized, like many other organizations, that git repositories provide an excellent framework to share and reuse training material. Because git is created for co-development, it is straightforward to work together both within and outside an organization and share materials that are under constant development. However, material on GitHub or GitLab can be hard to find. Therefore, we created Glittr.org, a web application containing bioinformatics training material on GitHub/Lab. With this application, users can search and sort training material based on topic, popularity, recency, and other characteristics. With these initiatives, we aim to contribute to the re-use and co-development of bioinformatics training material.

• Dusanka Nikolic
• Sarah Morgan

The Bioinformatics Education Summit is an annual meeting of trainers and educators which has been running since May 2019. It is a working meeting, focused on not just discussing issues faced in teaching and learning Bioinformatics, but also to develop solutions and suggest best practices. Previous summits have led to revisions of the ISCB competency framework and associated accreditation schemes, along with the development of a global TtT course and best practices for virtual training delivery and working in LMICs. This years summit was hosted by Wellcome Connecting Science at the Wellcome Genome Campus, Hinxton, UK. It was a hybrid meeting, with a group of parrticipants on-site and a global audience spread between Australia, Latin America, Asia, Africa, Europe and North America. This session will provide an overview of the topics for this years summits, progress made in the work and opportunities for other trainers and educators to get involved.

• Stephen Piccolo, Brigham Young University, United States
• Paul Denny, University of Auckland, New Zealand
• Andrew Luxton-Reilly, University of Auckland, New Zealand
• Samuel Payne, Brigham Young University, New Zealand
• Perry Ridge, Brigham Young University, United States

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model—OpenAI's ChatGPT—can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.

• Mohammad Asif Khan, University of Doha for Science and Technology, Qatar

The life and health science communities are facing a critical challenge with managing a discovery process that is becoming ever more quantitative and data driven. Key to meeting this challenge is bioinformatics. Progress towards educating and training life scientists in the fundamentals of bioinformatics, however, reveals only a modest progress, with much more to be done. We assembled a working group of experienced educators and trainers, recruited through their membership in, and involvement with, international and regional organisations dedicated to bioinformatics education and training (as part of a Bioinformatics Grand Challenges Consortium (BGCC; https://www.apbionet.org/bgcc)) to identify a consensus set of grand challenges. In this talk, I will share the working group’s experience in defining and identifying the seven consensus grand challenges, which are difficult to solve, yet in urgent need of solutions. All stakeholders need to work together in a united effort to resolve these continuing obstacles to progress throughout biology and medicine.

• Asif Khan
• Eija Korpelainen
• Michelle Brazas
• Russell Schwartz
• Jason Williams

The panel discussion will consider some of the grand challenges of bioinformatics education. Participants will discuss how we define a "grand challenge" and consider a range of challenges facing computational biology educators that are expected to require significant community effort in the coming decades. It will further begin the speculation on pathways to tackling these challenges by computational biology educators and the broader life sciences community.